diff --git a/CHANGES.md b/CHANGES.md
index c9871d8b1..553509a40 100644
--- a/CHANGES.md
+++ b/CHANGES.md
@@ -16,6 +16,8 @@
 
 - Fixed loading netParams in some scenarios (bug caused by srting functions pre-processing)
 
+- Fix of `plotRaster` pops coloring if ordered not by gid
+
 # Version 1.0.5
 
 **New features**
diff --git a/netpyne/analysis/spikes.py b/netpyne/analysis/spikes.py
index 39bb9c403..98fc65297 100644
--- a/netpyne/analysis/spikes.py
+++ b/netpyne/analysis/spikes.py
@@ -375,6 +375,8 @@ def prepareSpikeData(
         'orderBy': orderBy,
         'axisArgs': axisArgs,
         'legendLabels': legendLabels,
+        'cellGids': df['pop'].index.tolist(),
+        'cellPops': df['pop'].tolist(),
     }
     
     if colorbyPhase:
diff --git a/netpyne/plotting/plotRaster.py b/netpyne/plotting/plotRaster.py
index 0c3c44e84..e4edc256b 100644
--- a/netpyne/plotting/plotRaster.py
+++ b/netpyne/plotting/plotRaster.py
@@ -228,6 +228,8 @@ def plotRaster(
     if type(rasterData) == str:
         rasterData = loadData(rasterData)
 
+    popsOfCellsByGid = zip([], [])
+
     # If input is a dictionary, pull the data out of it
     if type(rasterData) == dict:
 
@@ -245,6 +247,10 @@ def plotRaster(
         axisArgs = rasterData.get('axisArgs')
         legendLabels = rasterData.get('legendLabels')
 
+        popsOfCellsByGid = zip( # ordered the same
+            rasterData.get('cellGids', []),
+            rasterData.get('cellPops', []))
+
     # If input is a list or tuple, the first item is spike times, the second is spike indices
     elif type(rasterData) == list or type(rasterData) == tuple:
         spkTimes = rasterData[0]
@@ -288,32 +294,13 @@ def plotRaster(
             + ') must be the same size'
         )
 
-    # Create a dictionary with the color for each pop
-    if not colorList:
-        from .plotter import colorList
-    popColorsTemp = {popLabel: colorList[ipop % len(colorList)] for ipop, popLabel in enumerate(popLabels)}
-    if popColors:
-        popColorsTemp.update(popColors)
-    popColors = popColorsTemp
-
-    # Create a list to link cell indices to their populations
-    indPop = []
-    for popLabel, popNumCell in zip(popLabels, popNumCells):
-        indPop.extend(int(popNumCell) * [popLabel])
-
-    # Create a dictionary to link cells to their population color
-    cellInds = list(set(spkInds))
-    indColors = {cellInd: popColors[indPop[int(cellInd)]] for cellInd in cellInds}
-
-    # Create a list of spkColors to be fed into the scatter plot
-    spkColors = [indColors[spkInd] for spkGid, spkInd in zip(spkGids, spkInds)]
-
     # Set the time range appropriately
     if 'timeRange' in kwargs:
         timeRange = kwargs['timeRange']
     elif 'timeRange' in rasterData:
         timeRange = rasterData['timeRange']
     else:
+        import numpy as np
         timeRange = [0, np.ceil(max(spkTimes))]
 
     # Set features for raster plot colored by phase
@@ -325,6 +312,33 @@ def plotRaster(
         if 'pop_background' in colorbyPhase:
             if colorbyPhase['pop_background'] == True:
                 kwargs['background'] = {'popLabels': popLabels, 'popNumCells': popNumCells, 'timeRange': timeRange}
+    else:
+        # Create a dictionary with the color for each pop
+        if not colorList:
+            from .plotter import colorList
+        popColorsTemp = {popLabel: colorList[ipop % len(colorList)] for ipop, popLabel in enumerate(popLabels)}
+        if popColors:
+            popColorsTemp.update(popColors)
+        popColors = popColorsTemp
+
+        if orderBy == 'gid':
+            # Create a list to link cell indices to their populations
+            indPop = []
+            for popLabel, popNumCell in zip(popLabels, popNumCells):
+                indPop.extend(int(popNumCell) * [popLabel])
+
+            def color(_, ind):
+                return popColors[indPop[int(ind)]]
+        else:
+            popByGid = {gid: pop for (gid, pop) in popsOfCellsByGid}
+            def color(gid, _):
+                pop = popByGid.get(gid)
+                if not pop:
+                    return [.0, .0, .0] # default to black
+                return popColors.get(pop)
+
+        # Create a list of spkColors to be fed into the scatter plot
+        spkColors = [color(gid, ind) for gid, ind in zip(spkGids, spkInds)]
 
     # Create a dictionary with the inputs for a scatter plot
     scatterData = {}
diff --git a/netpyne/specs/netParams.py b/netpyne/specs/netParams.py
index e5db3bc3b..5a4112408 100644
--- a/netpyne/specs/netParams.py
+++ b/netpyne/specs/netParams.py
@@ -846,5 +846,5 @@ def setNestedParam(self, paramLabel, paramVal):
     def setCfgMapping(self, cfg):
         if hasattr(self, 'mapping'):
             for k, v in self.mapping.items():
-                if getattr(cfg, k, None):
+                if hasattr(cfg, k):
                     self.setNestedParam(v, getattr(cfg, k))
diff --git a/netpyne/tutorials/README.md b/netpyne/tutorials/README.md
index 43e7f6c62..ab68a098e 100644
--- a/netpyne/tutorials/README.md
+++ b/netpyne/tutorials/README.md
@@ -6,20 +6,19 @@ We don't want to affect your system in any way, so we will operate from a virtua
 
 You can open a terminal and enter the following at the prompt:
 
-    mkdir netpyne_tuts && cd netpyne_tuts && export PATH=/bin:/usr/bin && python3 -m venv env && source env/bin/activate && python3 -m pip install --upgrade pip && python3 -m pip install --upgrade ipython && python3 -m pip install --upgrade ipykernel && python3 -m pip install --upgrade jupyter && ipython kernel install --user --name=env && python3 -m pip install --upgrade neuron && git clone https://github.com/Neurosim-lab/netpyne.git && python3 -m pip install -e netpyne && cp -r netpyne/netpyne/tutorials . && cd tutorials && jupyter notebook
+    mkdir netpyne_tuts && cd netpyne_tuts && export PATH=/bin:/usr/bin && python3 -m venv env && source env/bin/activate && python3 -m pip install --upgrade pip && python3 -m pip install --upgrade ipython && python3 -m pip install --upgrade ipykernel && python3 -m pip install --upgrade jupyter && ipython kernel install --user --name=env && python3 -m pip install --upgrade neuron && git clone --depth 1 https://github.com/suny-downstate-medical-center/netpyne.git && python3 -m pip install -e netpyne && cp -r netpyne/netpyne/tutorials . && cd tutorials && jupyter notebook
 
 ## Installation option two
 
-You can execute **netpyne_tut0.py** by opening a terminal and entering:
+You can execute **install_tuts.py** by downloading it, opening a terminal and entering:
 
-    python3 netpyne_tut0.py
+    python3 install_tuts.py
 
 ## Installation option three
 
-You can execute **netpyne_tut0.sh** by opening a terminal and entering:
+You can execute **install_tuts.sh** by downloading it, opening a terminal and entering:
 
-    chmod u+x netpyne_tut0.sh
-    ./netpyne_tut0.sh
+    sh install_tuts.sh
 
 ## Installation summary
 
@@ -31,7 +30,7 @@ Either of these options will do the same thing.  They will:
 - Activate (enter) the virtual environment
 - Upgrade pip and install necessary packages
 - Create a Jupyter kernel out of **env**
-- Clone the NetPyNE GitHub repository
+- Clone the NetPyNE GitHub repository (using shallow cloning)
 - Install NetPyNE using pip 
 - Copy the NetPyNE **tutorials** directory into **netpyne_tuts**
 - Change into the **tutorials** directory
@@ -39,7 +38,7 @@ Either of these options will do the same thing.  They will:
 
 ## Future use
 
-This installation step only needs to be performed once.  To re-enter the virtual environment in the future, change to the **netpyne_tuts** directory and execute the following:
+This installation step only needs to be performed once. To re-enter the virtual environment in the future, change to the **netpyne_tuts** directory and execute the following:
 
     source env/bin/activate
     jupyter notebook
diff --git a/netpyne/tutorials/cells/BS0284.swc b/netpyne/tutorials/cells/BS0284.swc
new file mode 100644
index 000000000..1d4aec235
--- /dev/null
+++ b/netpyne/tutorials/cells/BS0284.swc
@@ -0,0 +1,2272 @@
+# SWC to SWC conversion from L-Measure. Sridevi Polavaram: spolavar@gmu.edu
+# Original fileName:W:\ToBackup\Kaitlyn\Processing\Suter_shepherd\CNG.swc\BS0284.ASC.swc.CNG.swc
+#
+# Original file BS0284.ASC.swc edited using StdSwc version 1.31 on 3/26/16.

+# Irregularities and fixes documented in BS0284.ASC.swc.std.  See StdSwc1.31.doc for more information.

+#

+# Neurolucida to SWC conversion from L-Measure. Sridevi Polavaram: spolavar@gmu.edu

+# Original fileName:Y:\ToBackup\Kaitlyn\Processing\Suter_shepherd\Source_Version\BS0284.ASC

+# The original file has 19 soma contours that are averaged into 19 soma points

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+ 2245 4 55.41 25.83 -23.63 0.295 2244
+ 2246 4 60.66 20.69 -24.56 0.295 2245
+ 2247 4 63.13 17.76 -26.48 0.295 2246
+ 2248 4 66.99 12.84 -26.9 0.295 2247
+ 2249 4 71 11.27 -28.38 0.295 2248
+ 2250 4 72.75 5.88 -29.03 0.295 2249
+ 2251 4 72.56 4.61 -29.97 0.295 2250
+ 2252 4 76.72 0.47 -33.54 0.295 2251
+ 2253 4 79.85 -4.1 -36.75 0.295 2252
+ 2254 4 81.53 -6.28 -37.37 0.295 2253
+ 2255 4 81.73 -8.79 -37.44 0.295 2254
+ 2256 4 85.52 -13.01 -40.88 0.295 2255
+ 2257 4 87.94 -16.27 -43.8 0.28 2256
+ 2258 4 88.98 -20.42 -50.3 0.28 2257
+ 2259 4 92.38 -23.19 -53.6 0.28 2258
+ 2260 4 99.93 -28.76 -58.42 0.28 2259
+ 2261 4 101.77 -30.34 -60.13 0.265 2260
+ 2262 4 104.46 -37.92 -60.09 0.265 2261
+ 2263 4 107.44 -40.23 -56.12 0.265 2262
diff --git a/netpyne/tutorials/cells/CSTR_cellParams.json b/netpyne/tutorials/cells/CSTR_cellParams.json
new file mode 100644
index 000000000..8e353abe5
--- /dev/null
+++ b/netpyne/tutorials/cells/CSTR_cellParams.json
@@ -0,0 +1,554 @@
+{
+  "secs": {
+    "soma": {
+      "ions": {
+        "na": {
+          "i": 10.0,
+          "e": 42.0,
+          "o": 140.0
+        },
+        "ca": {
+          "i": 0.00005,
+          "e": 132.4579341637009,
+          "o": 2.0
+        },
+        "k": {
+          "i": 54.4,
+          "e": -104.0,
+          "o": 2.5
+        }
+      },
+      "mechs": {
+        "kBK": {
+          "tau": 1.0,
+          "caPmax": 1.0,
+          "caPmin": 0.0,
+          "caVhh": 0.002,
+          "caVhmin": -2.1899738592999967,
+          "gpeak": 0.01529200755489,
+          "caPh": 0.002,
+          "caPk": 1.0,
+          "k": 17.0,
+          "caVhmax": 155.67
+        },
+        "pas": {
+          "e": -90.7942106535,
+          "g": 0.00008772270604035548
+        },
+        "cat": {
+          "gcatbar": 0.0
+        },
+        "ih": {
+          "aslope": 7.12195833953,
+          "gbar": 0.00000999986721729,
+          "ascale": 0.00276508832116,
+          "bslope": 28.5415800392,
+          "bscale": 0.154401580683,
+          "ashift": 118.919921764
+        },
+        "kap": {
+          "vhalfn": 32.7885075379,
+          "gbar": 0.0898600246397,
+          "sh": 0.0,
+          "vhalfl": -59.7867409796,
+          "tq": -52.0967985869
+        },
+        "can": {
+          "gcanbar": 0.00000460717910591
+        },
+        "cal": {
+          "gcalbar": 0.00000441583533572
+        },
+        "nax": {
+          "gbar": 0.0345117294903,
+          "sh": 0.0
+        },
+        "kdr": {
+          "gbar": 0.0131103978049,
+          "sh": 0.0,
+          "vhalfn": 11.6427471384
+        }
+      },
+      "geom": {
+        "diam": 13.5574965259,
+        "cm": 1.98838095104,
+        "nseg": 1,
+        "L": 12.53064375245,
+        "Ra": 88.8088669637,
+        "pt3d": [
+          [
+            0,
+            0,
+            0,
+            13.5574965259
+          ],
+          [
+            0,
+            12.53064375245,
+            0,
+            13.5574965259
+          ]
+        ]
+      },
+      "topol": {},
+      "vinit": -75.8801688855
+    },
+    "Adend1": {
+      "ions": {
+        "na": {
+          "i": 10.0,
+          "e": 42.0,
+          "o": 140.0
+        },
+        "ca": {
+          "i": 0.00005,
+          "e": 132.4579341637009,
+          "o": 2.0
+        },
+        "k": {
+          "i": 54.4,
+          "e": -104.0,
+          "o": 2.5
+        }
+      },
+      "mechs": {
+        "kBK": {
+          "tau": 1.0,
+          "caPmax": 1.0,
+          "caPmin": 0.0,
+          "caVhh": 0.002,
+          "caVhmin": -2.1899738592999967,
+          "gpeak": 0.01529200755489,
+          "caPh": 0.002,
+          "caPk": 1.0,
+          "k": 17.0,
+          "caVhmax": 155.67
+        },
+        "pas": {
+          "e": -90.7942106535,
+          "g": 0.00008296662661333986
+        },
+        "cat": {
+          "gcatbar": 0.0
+        },
+        "ih": {
+          "aslope": 7.12195833953,
+          "gbar": 0.00000999986721729,
+          "ascale": 0.00276508832116,
+          "bslope": 28.5415800392,
+          "bscale": 0.154401580683,
+          "ashift": 118.919921764
+        },
+        "kap": {
+          "vhalfn": 32.7885075379,
+          "gbar": 0.0898600246397,
+          "sh": 0.0,
+          "vhalfl": -59.7867409796,
+          "tq": -52.0967985869
+        },
+        "can": {
+          "gcanbar": 0.00000460717910591
+        },
+        "cal": {
+          "gcalbar": 0.00000441583533572
+        },
+        "nax": {
+          "gbar": 0.0345117294903,
+          "sh": 0.0
+        },
+        "kdr": {
+          "gbar": 0.0131103978049,
+          "sh": 0.0,
+          "vhalfn": 11.6427471384
+        }
+      },
+      "geom": {
+        "diam": 2.86090460606,
+        "cm": 1.9931856141,
+        "nseg": 1,
+        "L": 58.9231269108,
+        "Ra": 88.8088669637,
+        "pt3d": [
+          [
+            0,
+            12.53064375245,
+            0,
+            2.86090460606
+          ],
+          [
+            0,
+            71.45377066325,
+            0,
+            2.86090460606
+          ]
+        ]
+      },
+      "topol": {
+        "childX": 0.0,
+        "parentSec": "soma",
+        "parentX": 1.0
+      },
+      "vinit": -75.8801688855
+    },
+    "Adend2": {
+      "ions": {
+        "na": {
+          "i": 10.0,
+          "e": 42.0,
+          "o": 140.0
+        },
+        "ca": {
+          "i": 0.00005,
+          "e": 132.4579341637009,
+          "o": 2.0
+        },
+        "k": {
+          "i": 54.4,
+          "e": -104.0,
+          "o": 2.5
+        }
+      },
+      "mechs": {
+        "kBK": {
+          "tau": 1.0,
+          "caPmax": 1.0,
+          "caPmin": 0.0,
+          "caVhh": 0.002,
+          "caVhmin": -2.1899738592999967,
+          "gpeak": 0.01529200755489,
+          "caPh": 0.002,
+          "caPk": 1.0,
+          "k": 17.0,
+          "caVhmax": 155.67
+        },
+        "pas": {
+          "e": -90.7942106535,
+          "g": 0.00008296662661333986
+        },
+        "cat": {
+          "gcatbar": 0.0
+        },
+        "ih": {
+          "aslope": 7.12195833953,
+          "gbar": 0.00000999986721729,
+          "ascale": 0.00276508832116,
+          "bslope": 28.5415800392,
+          "bscale": 0.154401580683,
+          "ashift": 118.919921764
+        },
+        "kap": {
+          "vhalfn": 32.7885075379,
+          "gbar": 0.0898600246397,
+          "sh": 0.0,
+          "vhalfl": -59.7867409796,
+          "tq": -52.0967985869
+        },
+        "can": {
+          "gcanbar": 0.00000460717910591
+        },
+        "cal": {
+          "gcalbar": 0.00000441583533572
+        },
+        "nax": {
+          "gbar": 0.0345117294903,
+          "sh": 0.0
+        },
+        "kdr": {
+          "gbar": 0.0131103978049,
+          "sh": 0.0,
+          "vhalfn": 11.6427471384
+        }
+      },
+      "geom": {
+        "diam": 2.86090460606,
+        "cm": 1.9931856141,
+        "nseg": 1,
+        "L": 58.9231269108,
+        "Ra": 88.8088669637,
+        "pt3d": [
+          [
+            0,
+            71.45377066325,
+            0,
+            2.86090460606
+          ],
+          [
+            0,
+            130.37689757405,
+            0,
+            2.86090460606
+          ]
+        ]
+      },
+      "topol": {
+        "childX": 0.0,
+        "parentSec": "Adend1",
+        "parentX": 1.0
+      },
+      "vinit": -75.8801688855
+    },
+    "Adend3": {
+      "ions": {
+        "na": {
+          "i": 10.0,
+          "e": 42.0,
+          "o": 140.0
+        },
+        "ca": {
+          "i": 0.00005,
+          "e": 132.4579341637009,
+          "o": 2.0
+        },
+        "k": {
+          "i": 54.4,
+          "e": -104.0,
+          "o": 2.5
+        }
+      },
+      "mechs": {
+        "kBK": {
+          "tau": 1.0,
+          "caPmax": 1.0,
+          "caPmin": 0.0,
+          "caVhh": 0.002,
+          "caVhmin": -2.1899738592999967,
+          "gpeak": 0.01529200755489,
+          "caPh": 0.002,
+          "caPk": 1.0,
+          "k": 17.0,
+          "caVhmax": 155.67
+        },
+        "pas": {
+          "e": -90.7942106535,
+          "g": 0.00008296662661333986
+        },
+        "cat": {
+          "gcatbar": 0.0
+        },
+        "ih": {
+          "aslope": 7.12195833953,
+          "gbar": 0.00000999986721729,
+          "ascale": 0.00276508832116,
+          "bslope": 28.5415800392,
+          "bscale": 0.154401580683,
+          "ashift": 118.919921764
+        },
+        "kap": {
+          "vhalfn": 32.7885075379,
+          "gbar": 0.0898600246397,
+          "sh": 0.0,
+          "vhalfl": -59.7867409796,
+          "tq": -52.0967985869
+        },
+        "can": {
+          "gcanbar": 0.00000460717910591
+        },
+        "cal": {
+          "gcalbar": 0.00000441583533572
+        },
+        "nax": {
+          "gbar": 0.0345117294903,
+          "sh": 0.0
+        },
+        "kdr": {
+          "gbar": 0.0131103978049,
+          "sh": 0.0,
+          "vhalfn": 11.6427471384
+        }
+      },
+      "geom": {
+        "diam": 2.86090460606,
+        "cm": 1.9931856141,
+        "nseg": 1,
+        "L": 58.9231269108,
+        "Ra": 88.8088669637,
+        "pt3d": [
+          [
+            0,
+            130.37689757405,
+            0,
+            2.86090460606
+          ],
+          [
+            0,
+            189.30002448484998,
+            0,
+            2.86090460606
+          ]
+        ]
+      },
+      "topol": {
+        "childX": 0.0,
+        "parentSec": "Adend2",
+        "parentX": 1.0
+      },
+      "vinit": -75.8801688855
+    },
+    "axon": {
+      "ions": {
+        "na": {
+          "i": 10.0,
+          "e": 42.0,
+          "o": 140.0
+        },
+        "k": {
+          "i": 54.4,
+          "e": -104.0,
+          "o": 2.5
+        }
+      },
+      "mechs": {
+        "nax": {
+          "gbar": 0.1725586474515,
+          "sh": 0.0
+        },
+        "kap": {
+          "vhalfn": 32.7885075379,
+          "gbar": 0.4493001231985,
+          "sh": 0.0,
+          "vhalfl": -59.7867409796,
+          "tq": -52.0967985869
+        },
+        "pas": {
+          "e": -90.7942106535,
+          "g": 0.0004120813684418219
+        },
+        "kdr": {
+          "gbar": 0.0655519890245,
+          "sh": 0.0,
+          "vhalfn": 11.6427471384
+        }
+      },
+      "geom": {
+        "diam": 0.552948640016,
+        "cm": 1.98999103645,
+        "nseg": 1,
+        "L": 119.72972477280001,
+        "Ra": 88.8088669637,
+        "pt3d": [
+          [
+            0,
+            0,
+            0,
+            0.552948640016
+          ],
+          [
+            0,
+            -119.72972477280001,
+            0,
+            0.552948640016
+          ]
+        ]
+      },
+      "topol": {
+        "childX": 0.0,
+        "parentSec": "soma",
+        "parentX": 0.0
+      },
+      "vinit": -75.8801688855
+    },
+    "Bdend": {
+      "ions": {
+        "na": {
+          "i": 10.0,
+          "e": 42.0,
+          "o": 140.0
+        },
+        "ca": {
+          "i": 0.00005,
+          "e": 132.4579341637009,
+          "o": 2.0
+        },
+        "k": {
+          "i": 54.4,
+          "e": -104.0,
+          "o": 2.5
+        }
+      },
+      "mechs": {
+        "kBK": {
+          "tau": 1.0,
+          "caPmax": 1.0,
+          "caPmin": 0.0,
+          "caVhh": 0.002,
+          "caVhmin": -2.1899738592999967,
+          "gpeak": 0.01529200755489,
+          "caPh": 0.002,
+          "caPk": 1.0,
+          "k": 17.0,
+          "caVhmax": 155.67
+        },
+        "pas": {
+          "e": -90.7942106535,
+          "g": 0.00004343420865783602
+        },
+        "cat": {
+          "gcatbar": 0.0
+        },
+        "ih": {
+          "aslope": 7.12195833953,
+          "gbar": 0.00000999986721729,
+          "ascale": 0.00276508832116,
+          "bslope": 28.5415800392,
+          "bscale": 0.154401580683,
+          "ashift": 118.919921764
+        },
+        "kap": {
+          "vhalfn": 32.7885075379,
+          "gbar": 0.0898600246397,
+          "sh": 0.0,
+          "vhalfl": -59.7867409796,
+          "tq": -52.0967985869
+        },
+        "can": {
+          "gcanbar": 0.00000460717910591
+        },
+        "cal": {
+          "gcalbar": 0.00000441583533572
+        },
+        "nax": {
+          "gbar": 0.0345117294903,
+          "sh": 0.0
+        },
+        "kdr": {
+          "gbar": 0.0131103978049,
+          "sh": 0.0,
+          "vhalfn": 11.6427471384
+        }
+      },
+      "geom": {
+        "diam": 1.02617341611,
+        "cm": 1.98526067401,
+        "nseg": 1,
+        "L": 72.960082572,
+        "Ra": 88.8088669637,
+        "pt3d": [
+          [
+            0,
+            6.53064375245,
+            0,
+            1.02617341611
+          ],
+          [
+            72.960082572,
+            6.53064375245,
+            0,
+            1.02617341611
+          ]
+        ]
+      },
+      "topol": {
+        "childX": 0.0,
+        "parentSec": "soma",
+        "parentX": 0.5
+      },
+      "vinit": -75.8801688855
+    }
+  },
+  "globals": {
+    "celsius": 34.0,
+    "v_init": -75.8801688855,
+    "erev_ih": -37.0
+  },
+  "secLists": {}
+}
\ No newline at end of file
diff --git a/netpyne/tutorials/cells/FScell.hoc b/netpyne/tutorials/cells/FScell.hoc
new file mode 100644
index 000000000..5ff57de1f
--- /dev/null
+++ b/netpyne/tutorials/cells/FScell.hoc
@@ -0,0 +1,177 @@
+//Interneuron for PFC - fast spiking parvalbumin interneuron 
+//Based on Durstewitz and Gabriel 2006
+//"Irregular spiking in NMDA-driven prefrontal cortex neurons"
+
+
+begintemplate FScell
+
+public soma, axon, dend
+
+create soma, axon, dend
+
+
+proc init () {
+
+create soma, axon, dend
+
+soma_nafin=0.045 
+soma_kdrin=0.018  
+soma_Kslowin=0.000725*0.1
+soma_hin=0.00001
+soma_kapin=0.0032*15
+soma_canin=0.0003
+soma_kctin=0.0001
+
+soma {
+     nseg=1
+     L=10 //27
+     diam=11 //29
+
+	insert pas
+	cm=1.2            //microF/cm2
+	g_pas =1/10000    //mho/cm2   
+	e_pas = v_initin   //(Kawaguchi k Kubota, 1993 --> -73+-3.9)
+	v_initin= -73
+	Ra=150 
+
+     insert Nafx
+     gnafbar_Nafx= soma_nafin
+
+     insert kdrin
+     gkdrbar_kdrin= soma_kdrin
+
+     insert IKsin	
+     gKsbar_IKsin= soma_Kslowin
+
+     insert hin
+     gbar_hin=soma_hin
+
+     insert kapin
+     gkabar_kapin=soma_kapin
+
+     insert canin
+     gcalbar_canin=soma_canin
+
+     insert kctin
+     gkcbar_kctin=soma_kctin
+
+    insert cadyn
+
+    insert kBK
+    tau_kBK = 1.0
+    caPmax_kBK = 1.0
+    caPmin_kBK = 0.0
+    caVhh_kBK = 0.002
+    caVhmin_kBK = -2.1899738592999967
+    gpeak_kBK = 0.01529200755489
+    caPh_kBK = 0.002
+    caPk_kBK = 1.0
+    k_kBK = 17.0
+    caVhmax_kBK = 155.67
+}
+
+axon {
+	nseg=1
+	L=115*2
+	diam=1.5
+	
+	insert pas
+	cm=1.2            //microF/cm2
+	g_pas =1/10000  //mho/cm2   
+	e_pas = v_initin
+	v_initin= -73
+	Ra=150 
+
+	insert Nafx
+	gnafbar_Nafx=soma_nafin*10
+
+	insert kdrin
+	gkdrbar_kdrin=soma_kdrin*0.5
+}
+
+
+dend {
+	nseg=1
+	L=2*22*2 
+	diam=7
+
+
+	insert pas
+	cm=1.2         //microF/cm2
+	g_pas =1/10000    //mho/cm2   
+	e_pas = v_initin//(Kawaguchi k Kubota, 1993 --> -73+-3.9)
+	v_initin= -73
+	Ra=150
+
+	insert Nafx
+	gnafbar_Nafx=0.018*5
+
+	insert kdrin
+	gkdrbar_kdrin=0.018*0.5
+
+     	insert kapin
+    	gkabar_kapin=soma_kapin*10
+
+}
+
+	ko0_k_ion = 3.82   //mM
+ 	ki0_k_ion = 140    //mM
+	celsius   = 23
+	
+	connect dend(0), soma(1) 
+	connect axon(0), soma(0) 
+}
+
+init()
+
+endtemplate FScell
+
+
+//Creating new interneurons
+
+// objref FScell1
+
+// FScell1 = new FScell()
+
+// //Create list with segments
+// objref insoma_list, incell_list
+
+// insoma_list = new SectionList()
+// FScell1.soma insoma_list.append()
+
+// incell_list = new SectionList()
+// FScell1.soma incell_list.append()
+// FScell1.axon incell_list.append()
+// FScell1.dend incell_list.append()
+
+// proc current_balancein() {
+
+//   finitialize($1)
+//   fcurrent()
+
+//   printf("Balancing each compartment to %d mV\n", $1)
+
+//   forsec incell_list{
+//    for (x) {
+//     	if (ismembrane("na_ion")) {e_pas(x)=v(x)+ina(x)/g_pas(x)} 
+//    	if (ismembrane("k_ion")) {e_pas(x)=e_pas(x)+ik(x)/g_pas(x)} 
+
+//       	if (ismembrane("ca_ion")) {e_pas(x)=e_pas(x)+ica(x)/g_pas(x)}
+// //        if (ismembrane("Ca_ion")) {e_pas(x)=e_pas(x)+iCa(x)/g_pas(x)}
+//  //       if (ismembrane("in_ion")) {e_pas(x)=e_pas(x)+in(x)/g_pas(x)} //ican
+//        if (ismembrane("h")) {e_pas(x)=e_pas(x)+ihi(x)/g_pas(x)}
+
+// //        d = distance(1,x)
+// //	xdist = find_vector_distance_precise(secname(),x)    // calc. perpedicular distance      
+//   //	 printf("x = %e, xdist = %e, d = %e, e_pas = %e mV, rm = %e mA/(mVcm2)\n", x,  xdist, d, e_pas(x), 1./g_pas(x))
+//       //	fcurrent()
+//     }
+//   }
+
+// //finitialize(v_init)
+// fcurrent()
+// }
+
+
+
+// current_balancein(-73)  
\ No newline at end of file
diff --git a/netpyne/tutorials/cells/HHCellFile.py b/netpyne/tutorials/cells/HHCellFile.py
new file mode 100644
index 000000000..3d394db0e
--- /dev/null
+++ b/netpyne/tutorials/cells/HHCellFile.py
@@ -0,0 +1,81 @@
+from matplotlib import pyplot
+import random
+from datetime import datetime
+import pickle
+from neuron import h, gui
+
+
+class Cell(object):
+    def __init__(self):
+        self.synlist = []
+        self.createSections()
+        self.buildTopology()
+        self.defineGeometry()
+        self.defineBiophysics()
+        self.createSynapses()
+        self.nclist = []
+
+    def createSections(self):
+        pass
+
+    def buildTopology(self):
+        pass
+
+    def defineGeometry(self):
+        pass
+
+    def defineBiophysics(self):
+        pass
+
+    def createSynapses(self):
+        """Add an exponentially decaying synapse """
+        synsoma = h.ExpSyn(self.soma(0.5))
+        synsoma.tau = 2
+        synsoma.e = 0
+        syndend = h.ExpSyn(self.dend(0.5))
+        syndend.tau = 2
+        syndend.e = 0
+        self.synlist.append(synsoma) # synlist is defined in Cell
+        self.synlist.append(syndend) # synlist is defined in Cell
+
+
+    def createNetcon(self, thresh=10):
+        """ created netcon to record spikes """
+        nc = h.NetCon(self.soma(0.5)._ref_v, None, sec = self.soma)
+        nc.threshold = thresh
+        return nc
+
+
+class HHCellClass(Cell):
+    """HH cell: A soma with active channels"""
+    def createSections(self):
+        """Create the sections of the cell."""
+        self.soma = h.Section(name='soma', cell=self)
+        self.dend = h.Section(name='dend', cell=self)
+
+    def defineGeometry(self):
+        """Set the 3D geometry of the cell."""
+        self.soma.L = 18.8
+        self.soma.diam = 18.8
+        self.soma.Ra = 123.0
+
+        self.dend.L = 200.0
+        self.dend.diam = 1.0
+        self.dend.Ra = 100.0
+
+    def defineBiophysics(self):
+        """Assign the membrane properties across the cell."""
+        # Insert active Hodgkin-Huxley current in the soma
+        self.soma.insert('hh')
+        self.soma.gnabar_hh = 0.12  # Sodium conductance in S/cm2
+        self.soma.gkbar_hh = 0.036  # Potassium conductance in S/cm2
+        self.soma.gl_hh = 0.003    # Leak conductance in S/cm2
+        self.soma.el_hh = -70       # Reversal potential in mV
+
+        self.dend.insert('pas')
+        self.dend.g_pas = 0.001  # Passive conductance in S/cm2
+        self.dend.e_pas = -65    # Leak reversal potential mV
+        self.dend.nseg = 1000
+
+    def buildTopology(self):
+        self.dend.connect(self.soma(1))
diff --git a/netpyne/tutorials/cells/IT2_reduced_cellParams.json b/netpyne/tutorials/cells/IT2_reduced_cellParams.json
new file mode 100644
index 000000000..165a7b99e
--- /dev/null
+++ b/netpyne/tutorials/cells/IT2_reduced_cellParams.json
@@ -0,0 +1,633 @@
+{
+  "secs": {
+    "soma": {
+      "ions": {
+        "na": {
+          "i": 10.0,
+          "e": 42.0,
+          "o": 140.0
+        },
+        "ca": {
+          "i": 0.00005,
+          "e": 132.4579341637009,
+          "o": 2.0
+        },
+        "k": {
+          "i": 54.4,
+          "e": -104.0,
+          "o": 2.5
+        }
+      },
+      "mechs": {
+        "kBK": {
+          "tau": 1.0,
+          "caPmax": 1.0,
+          "caPmin": 0.0,
+          "caVhh": 0.002,
+          "caVhmin": 43.919142291200004,
+          "gpeak": 0.0000445651933019,
+          "caPh": 0.002,
+          "caPk": 1.0,
+          "k": 17.0,
+          "caVhmax": 155.67
+        },
+        "pas": {
+          "e": -87.1335623948,
+          "g": 0.00009442294539558377
+        },
+        "cat": {
+          "gcatbar": 9.29455717585E-7
+        },
+        "ih": {
+          "aslope": 7.09800576233,
+          "ascale": 0.00320887293027,
+          "bscale": 0.285307415701,
+          "gbar": 0.000033176340367,
+          "ashift": 119.696272155,
+          "bslope": 23.2995848558
+        },
+        "kap": {
+          "tq": -49.7149526489,
+          "gbar": 0.0240195239098,
+          "sh": 0.0,
+          "vhalfl": -36.7754836348,
+          "vhalfn": 32.179925527
+        },
+        "can": {
+          "gcanbar": 8.13137955053E-7
+        },
+        "kdr": {
+          "gbar": 0.00833766634808,
+          "sh": 0.0,
+          "vhalfn": 11.6427471384
+        },
+        "cal": {
+          "gcalbar": 0.00000239132864454
+        },
+        "nax": {
+          "gbar": 0.0768253702194,
+          "sh": 0.0
+        },
+        "cadad": {
+          "taur": 99.1146852282,
+          "depth": 0.119408607923,
+          "kd": 0.0,
+          "cainf": 0.00024,
+          "kt": 0.0
+        }
+      },
+      "weightNorm": [
+        0.0012489909542124195
+      ],
+      "geom": {
+        "diam": 18.2149102762,
+        "cm": 2.4998269977,
+        "nseg": 1,
+        "L": 18.4123467666,
+        "Ra": 70.0015514222,
+        "pt3d": [
+          [
+            0,
+            40.0,
+            0,
+            18.2149102762
+          ],
+          [
+            0,
+            48.4123467666,
+            0,
+            18.2149102762
+          ]
+        ]
+      },
+      "topol": {},
+      "vinit": -88.7986728464
+    },
+    "Adend1": {
+      "ions": {
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+          "e": 42.0,
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+        },
+        "ca": {
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+        },
+        "k": {
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+          "e": -104.0,
+          "o": 2.5
+        }
+      },
+      "mechs": {
+        "kBK": {
+          "tau": 1.0,
+          "caPmax": 1.0,
+          "caPmin": 0.0,
+          "caVhh": 0.002,
+          "caVhmin": 43.919142291200004,
+          "gpeak": 0.0000445651933019,
+          "caPh": 0.002,
+          "caPk": 1.0,
+          "k": 17.0,
+          "caVhmax": 155.67
+        },
+        "pas": {
+          "e": -87.1335623948,
+          "g": 0.00007199592136286027
+        },
+        "cat": {
+          "gcatbar": 9.29455717585E-7
+        },
+        "ih": {
+          "aslope": 7.09800576233,
+          "ascale": 0.00320887293027,
+          "bscale": 0.285307415701,
+          "gbar": 0.000033176340367,
+          "ashift": 119.696272155,
+          "bslope": 23.2995848558
+        },
+        "kap": {
+          "tq": -49.7149526489,
+          "gbar": 0.0240195239098,
+          "sh": 0.0,
+          "vhalfl": -36.7754836348,
+          "vhalfn": 32.179925527
+        },
+        "can": {
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+        },
+        "kdr": {
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+          "sh": 0.0,
+          "vhalfn": 11.6427471384
+        },
+        "cal": {
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+        "nax": {
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+          "sh": 0.0
+        },
+        "cadad": {
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+          "depth": 0.119408607923,
+          "kd": 0.0,
+          "cainf": 0.00024,
+          "kt": 0.0
+        }
+      },
+      "weightNorm": [
+        0.001289511362519787
+      ],
+      "geom": {
+        "diam": 1.5831889597,
+        "cm": 2.74242941886,
+        "nseg": 1,
+        "L": 96.75,
+        "Ra": 70.0015514222,
+        "pt3d": [
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+            0,
+            48.4123467666,
+            0,
+            1.5831889597
+          ],
+          [
+            0,
+            145.1623467666,
+            0,
+            1.5831889597
+          ]
+        ]
+      },
+      "topol": {
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+        "parentSec": "soma",
+        "parentX": 1.0
+      },
+      "vinit": -88.7986728464
+    },
+    "Adend2": {
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+        },
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+        }
+      },
+      "mechs": {
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+        },
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+          "e": -87.1335623948,
+          "g": 0.00007199592136286027
+        },
+        "cat": {
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+        },
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+          "bscale": 0.285307415701,
+          "gbar": 0.000033176340367,
+          "ashift": 119.696272155,
+          "bslope": 23.2995848558
+        },
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+          "sh": 0.0,
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+          "vhalfn": 32.179925527
+        },
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+        },
+        "kdr": {
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+          "sh": 0.0,
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+        },
+        "cal": {
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+        },
+        "nax": {
+          "gbar": 0.0768253702194,
+          "sh": 0.0
+        },
+        "cadad": {
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+          "depth": 0.119408607923,
+          "kd": 0.0,
+          "cainf": 0.00024,
+          "kt": 0.0
+        }
+      },
+      "weightNorm": [
+        0.0013388693828493333
+      ],
+      "geom": {
+        "diam": 1.5831889597,
+        "cm": 2.74242941886,
+        "nseg": 1,
+        "L": 96.75,
+        "Ra": 70.0015514222,
+        "pt3d": [
+          [
+            0,
+            145.1623467666,
+            0,
+            1.5831889597
+          ],
+          [
+            0,
+            241.9123467666,
+            0,
+            1.5831889597
+          ]
+        ]
+      },
+      "topol": {
+        "childX": 0.0,
+        "parentSec": "Adend1",
+        "parentX": 1.0
+      },
+      "vinit": -88.7986728464
+    },
+    "Adend3": {
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+        },
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+          "o": 2.0
+        },
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+          "e": -104.0,
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+        }
+      },
+      "mechs": {
+        "kBK": {
+          "tau": 1.0,
+          "caPmax": 1.0,
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+        },
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+          "e": -87.1335623948,
+          "g": 0.00007199592136286027
+        },
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+        },
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+          "bscale": 0.285307415701,
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+          "ashift": 119.696272155,
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+        },
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+        },
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+        "nax": {
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+          "sh": 0.0
+        },
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+          "depth": 0.119408607923,
+          "kd": 0.0,
+          "cainf": 0.00024,
+          "kt": 0.0
+        }
+      },
+      "weightNorm": [
+        0.0013641410516106618
+      ],
+      "geom": {
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+        "cm": 2.74242941886,
+        "nseg": 1,
+        "L": 96.75,
+        "Ra": 70.0015514222,
+        "pt3d": [
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+            0,
+            241.9123467666,
+            0,
+            1.5831889597
+          ],
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+            0,
+            338.66234676659997,
+            0,
+            1.5831889597
+          ]
+        ]
+      },
+      "topol": {
+        "childX": 0.0,
+        "parentSec": "Adend2",
+        "parentX": 1.0
+      },
+      "vinit": -88.7986728464
+    },
+    "axon": {
+      "ions": {
+        "na": {
+          "i": 10.0,
+          "e": 42.0,
+          "o": 140.0
+        },
+        "k": {
+          "i": 54.4,
+          "e": -104.0,
+          "o": 2.5
+        }
+      },
+      "mechs": {
+        "nax": {
+          "gbar": 0.384126851097,
+          "sh": 0.0
+        },
+        "kap": {
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+          "sh": 0.0,
+          "vhalfl": -36.7754836348,
+          "vhalfn": 32.179925527
+        },
+        "pas": {
+          "e": -87.1335623948,
+          "g": 0.00035435694659685776
+        },
+        "kdr": {
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+          "sh": 0.0,
+          "vhalfn": 11.6427471384
+        }
+      },
+      "geom": {
+        "diam": 1.40966286462,
+        "cm": 2.4630760526,
+        "nseg": 1,
+        "L": 594.292937602,
+        "Ra": 70.0015514222,
+        "pt3d": [
+          [
+            0,
+            0,
+            0,
+            1.40966286462
+          ],
+          [
+            0,
+            -594.292937602,
+            0,
+            1.40966286462
+          ]
+        ]
+      },
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+        "parentSec": "soma",
+        "parentX": 0.0
+      },
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+    },
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+      "ions": {
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+        },
+        "ca": {
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+        },
+        "k": {
+          "i": 54.4,
+          "e": -104.0,
+          "o": 2.5
+        }
+      },
+      "mechs": {
+        "kBK": {
+          "tau": 1.0,
+          "caPmax": 1.0,
+          "caPmin": 0.0,
+          "caVhh": 0.002,
+          "caVhmin": 43.919142291200004,
+          "gpeak": 0.0000445651933019,
+          "caPh": 0.002,
+          "caPk": 1.0,
+          "k": 17.0,
+          "caVhmax": 155.67
+        },
+        "pas": {
+          "e": -87.1335623948,
+          "g": 0.00014147647761414165
+        },
+        "cat": {
+          "gcatbar": 9.29455717585E-7
+        },
+        "ih": {
+          "aslope": 7.09800576233,
+          "ascale": 0.00320887293027,
+          "bscale": 0.285307415701,
+          "gbar": 0.000033176340367,
+          "ashift": 119.696272155,
+          "bslope": 23.2995848558
+        },
+        "kap": {
+          "tq": -49.7149526489,
+          "gbar": 0.0240195239098,
+          "sh": 0.0,
+          "vhalfl": -36.7754836348,
+          "vhalfn": 32.179925527
+        },
+        "can": {
+          "gcanbar": 8.13137955053E-7
+        },
+        "kdr": {
+          "gbar": 0.00833766634808,
+          "sh": 0.0,
+          "vhalfn": 11.6427471384
+        },
+        "cal": {
+          "gcalbar": 0.00000239132864454
+        },
+        "nax": {
+          "gbar": 0.0768253702194,
+          "sh": 0.0
+        },
+        "cadad": {
+          "taur": 99.1146852282,
+          "depth": 0.119408607923,
+          "kd": 0.0,
+          "cainf": 0.00024,
+          "kt": 0.0
+        }
+      },
+      "weightNorm": [
+        0.001261282500702921
+      ],
+      "geom": {
+        "diam": 2.2799248874,
+        "cm": 2.74086279376,
+        "nseg": 1,
+        "L": 96.75,
+        "Ra": 70.0015514222,
+        "pt3d": [
+          [
+            0,
+            0,
+            0,
+            2.2799248874
+          ],
+          [
+            96.75,
+            0,
+            0,
+            2.2799248874
+          ]
+        ]
+      },
+      "topol": {
+        "childX": 0.0,
+        "parentSec": "soma",
+        "parentX": 0.5
+      },
+      "vinit": -88.7986728464
+    }
+  },
+  "globals": {
+    "celsius": 34.0,
+    "v_init": -88.7986728464,
+    "erev_ih": -37.0
+  },
+  "secLists": {
+    "alldend": [
+      "Adend1",
+      "Adend2",
+      "Adend3",
+      "Bdend"
+    ],
+    "apicdend": [
+      "Adend1",
+      "Adend2",
+      "Adend3"
+    ],
+    "spiny": [
+      "Adend1",
+      "Adend2",
+      "Adend3",
+      "Bdend"
+    ],
+    "perisom": [
+      "soma"
+    ]
+  },
+  "conds": {
+    "cellModel": "HH_reduced",
+    "cellType": "IT",
+    "ynorm": [
+      0.12,
+      0.31
+    ]
+  }
+}
\ No newline at end of file
diff --git a/netpyne/tutorials/cells/PT5B_full_cellParams.json b/netpyne/tutorials/cells/PT5B_full_cellParams.json
new file mode 100644
index 000000000..340942463
--- /dev/null
+++ b/netpyne/tutorials/cells/PT5B_full_cellParams.json
@@ -0,0 +1 @@
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"dend_1", "soma", "dend_8", "dend_48", "dend_44", "dend_47", "dend_41", "dend_40", "dend_42", "dend_53", "dend_51", "dend_57", "dend_28", "dend_27", "dend_24", "dend_21", "dend_38", "dend_34", "dend_30", "dend_33"], "SectionList[2]": ["axon"], "apicdend": ["apic_8", "apic_9", "apic_7", "apic_0", "apic_1", "apic_5", "apic_2", "apic_3", "apic_27", "apic_26", "apic_25", "apic_24", "apic_23", "apic_22", "apic_21", "apic_20", "apic_29", "apic_28", "apic_34", "apic_35", "apic_36", "apic_37", "apic_30", "apic_32", "apic_33", "apic_89", "apic_88", "apic_81", "apic_80", "apic_83", "apic_82", "apic_85", "apic_84", "apic_87", "apic_86", "apic_4", "apic_96", "apic_97", "apic_94", "apic_95", "apic_92", "apic_93", "apic_90", "apic_91", "apic_98", "apic_99", "apic_38", "apic_39", "apic_18", "apic_19", "apic_16", "apic_17", "apic_14", "apic_15", "apic_12", "apic_13", "apic_10", "apic_11", "apic_102", "apic_101", "apic_100", "apic_69", "apic_68", "apic_63", "apic_62", "apic_61", "apic_60", "apic_67", "apic_66", "apic_65", "apic_64", "apic_78", "apic_79", "apic_70", "apic_71", "apic_72", "apic_73", "apic_74", "apic_75", "apic_76", "apic_77", "apic_45", "apic_44", "apic_47", "apic_46", "apic_41", "apic_40", "apic_43", "apic_42", "apic_49", "apic_48", "apic_31", "apic_52", "apic_53", "apic_50", "apic_51", "apic_56", "apic_57", "apic_54", "apic_55", "apic_58", "apic_59", "apic_6"], "apical": ["apic_0", "apic_1", "apic_2", "apic_3", "apic_4", "apic_5", "apic_6", "apic_7", "apic_8", "apic_9", "apic_10", "apic_11", "apic_12", "apic_13", "apic_14", "apic_15", "apic_16", "apic_17", "apic_18", "apic_19", "apic_20", "apic_21", "apic_22", "apic_23", "apic_24", "apic_25", "apic_26", "apic_27", "apic_28", "apic_29", "apic_30", "apic_31", "apic_32", "apic_33", "apic_34", "apic_35", "apic_36", "apic_37", "apic_38", "apic_39", "apic_40", "apic_41", "apic_42", "apic_43", "apic_44", "apic_45", "apic_46", "apic_47", "apic_48", "apic_49", "apic_50", "apic_51", "apic_52", "apic_53", "apic_54", "apic_55", "apic_56", "apic_57", "apic_58", "apic_59", "apic_60", "apic_61", "apic_62", "apic_63", "apic_64", "apic_65", "apic_66", "apic_67", "apic_68", "apic_69", "apic_70", "apic_71", "apic_72", "apic_73", "apic_74", "apic_75", "apic_76", "apic_77", "apic_78", "apic_79", "apic_80", "apic_81", "apic_82", "apic_83", "apic_84", "apic_85", "apic_86", "apic_87", "apic_88", "apic_89", "apic_90", "apic_91", "apic_92", "apic_93", "apic_94", "apic_95", "apic_96", "apic_97", "apic_98", "apic_99", "apic_100", "apic_101", "apic_102"], "apical_oblique": ["apic_79", "apic_80", "apic_81", "apic_82", "apic_83", "apic_84", "apic_85", "apic_86", "apic_87", "apic_88", "apic_89", "apic_90", "apic_91", "apic_92", "apic_93", "apic_94", "apic_95", "apic_96", "apic_97", "apic_98", "apic_99", "apic_100", "apic_101", "apic_102"], "somatic": ["soma"], "apical_uppertrunk": ["apic_23", "apic_24", "apic_25", "apic_32", "apic_33", "apic_46", "apic_51", "apic_52", "apic_53", "apic_78"], "apic_lower": ["dend_16", "dend_17", "dend_14", "dend_15", "dend_12", "dend_13", "dend_10", "dend_11", "apic_8", "apic_9", "dend_18", "dend_19", "apic_7", "apic_0", "apic_1", "apic_5", "apic_2", "apic_3", "apic_23", "apic_22", "apic_21", "apic_20", "dend_63", "dend_62", "dend_61", "dend_60", "dend_67", "dend_66", "dend_65", "dend_64", "dend_68", "dend_9", "dend_4", "dend_5", "dend_6", "dend_7", "dend_0", "dend_1", "dend_2", "dend_3", "soma", "apic_89", "apic_88", "apic_81", "apic_80", "apic_83", "apic_82", "apic_85", "apic_84", "apic_87", "apic_86", "dend_8", "apic_4", "dend_49", "dend_48", "dend_45", "dend_44", "dend_47", "dend_46", "dend_41", "dend_40", "dend_43", "dend_42", "apic_96", "apic_97", "apic_94", "apic_95", "apic_92", "apic_93", "apic_90", "apic_91", "apic_98", "apic_99", "apic_18", "apic_19", "apic_16", "apic_17", "apic_14", "apic_15", "apic_12", "apic_13", "apic_10", "apic_11", "dend_58", "dend_59", "dend_52", "dend_53", "dend_50", "dend_51", "dend_56", "dend_57", "dend_54", "dend_55", "axon", "apic_102", "apic_101", "apic_100", "dend_29", "dend_28", "dend_27", "dend_26", "dend_25", "dend_24", "dend_23", "dend_22", "dend_21", "dend_20", "apic_78", "apic_79", "dend_38", "dend_39", "dend_34", "dend_35", "dend_36", "dend_37", "dend_30", "dend_31", "dend_32", "dend_33", "apic_52", "apic_6"], "spiny": ["dend_16", "dend_17", "dend_14", "dend_15", "dend_12", "dend_13", "dend_10", "dend_11", "apic_8", "apic_9", "dend_18", "dend_19", "apic_7", "apic_5", "apic_2", "apic_3", "apic_27", "apic_26", "apic_25", "apic_24", "apic_23", "apic_22", "apic_21", "apic_20", "apic_29", "apic_28", "dend_63", "dend_62", "dend_61", "dend_60", "dend_67", "dend_66", "dend_65", "dend_64", "dend_68", "apic_34", "apic_35", "apic_36", "apic_37", "apic_30", "dend_9", "apic_32", "apic_33", "dend_4", "dend_5", "dend_6", "dend_7", "dend_0", "dend_1", "dend_2", "dend_3", "apic_89", "apic_88", "apic_81", "apic_80", "apic_83", "apic_82", "apic_85", "apic_84", "apic_87", "apic_86", "dend_8", "apic_4", "dend_49", "dend_48", "dend_45", "dend_44", "dend_47", "dend_46", "dend_41", "dend_40", "dend_43", "dend_42", "apic_96", "apic_97", "apic_94", "apic_95", "apic_92", "apic_93", "apic_90", "apic_91", "apic_98", "apic_99", "apic_38", "apic_39", "apic_18", "apic_19", "apic_16", "apic_17", "apic_14", "apic_15", "apic_12", "apic_13", "apic_10", "apic_11", "dend_58", "dend_59", "dend_52", "dend_53", "dend_50", "dend_51", "dend_56", "dend_57", "dend_54", "dend_55", "apic_102", "apic_101", "apic_100", "apic_69", "apic_68", "apic_63", "apic_62", "apic_61", "apic_60", "apic_67", "apic_66", "apic_65", "apic_64", "dend_29", "dend_28", "dend_27", "dend_26", "dend_25", "dend_24", "dend_23", "dend_22", "dend_21", "dend_20", "apic_78", "apic_79", "apic_70", "apic_71", "apic_72", "apic_73", "apic_74", "apic_75", "apic_76", "apic_77", "dend_38", "dend_39", "dend_34", "dend_35", "dend_36", "dend_37", "dend_30", "dend_31", "dend_32", "dend_33", "apic_45", "apic_44", "apic_47", "apic_46", "apic_41", "apic_40", "apic_43", "apic_42", "apic_49", "apic_48", "apic_31", "apic_52", "apic_53", "apic_50", "apic_51", "apic_56", "apic_57", "apic_54", "apic_55", "apic_58", "apic_59", "apic_6"], "apical_maintrunk": ["apic_0", "apic_1", "apic_2", "apic_3", "apic_4", "apic_5", "apic_6", "apic_7", "apic_8", "apic_9", "apic_10", "apic_11", "apic_12", "apic_13", "apic_14", "apic_15", "apic_16", "apic_17", "apic_18", "apic_19", "apic_20", "apic_21", "apic_22"], "alldend": ["dend_16", "dend_17", "dend_14", "dend_15", "dend_12", "dend_13", "dend_10", "dend_11", "apic_8", "apic_9", "dend_18", "dend_19", "apic_7", "apic_0", "apic_1", "apic_5", "apic_2", "apic_3", "apic_27", "apic_26", "apic_25", "apic_24", "apic_23", "apic_22", "apic_21", "apic_20", "apic_29", "apic_28", "dend_63", "dend_62", "dend_61", "dend_60", "dend_67", "dend_66", "dend_65", "dend_64", "dend_68", "apic_34", "apic_35", "apic_36", "apic_37", "apic_30", "dend_9", "apic_32", "apic_33", "dend_4", "dend_5", "dend_6", "dend_7", "dend_0", "dend_1", "dend_2", "dend_3", "apic_89", "apic_88", "apic_81", "apic_80", "apic_83", "apic_82", "apic_85", "apic_84", "apic_87", "apic_86", "dend_8", "apic_4", "dend_49", "dend_48", "dend_45", "dend_44", "dend_47", "dend_46", "dend_41", "dend_40", "dend_43", "dend_42", "apic_96", "apic_97", "apic_94", "apic_95", "apic_92", "apic_93", "apic_90", "apic_91", "apic_98", "apic_99", "apic_38", "apic_39", "apic_18", "apic_19", "apic_16", "apic_17", "apic_14", "apic_15", "apic_12", "apic_13", "apic_10", "apic_11", "dend_58", "dend_59", "dend_52", "dend_53", "dend_50", "dend_51", "dend_56", "dend_57", "dend_54", "dend_55", "apic_102", "apic_101", "apic_100", "apic_69", "apic_68", "apic_63", "apic_62", "apic_61", "apic_60", "apic_67", "apic_66", "apic_65", "apic_64", "dend_29", "dend_28", "dend_27", "dend_26", "dend_25", "dend_24", "dend_23", "dend_22", "dend_21", "dend_20", "apic_78", "apic_79", "apic_70", "apic_71", "apic_72", "apic_73", "apic_74", "apic_75", "apic_76", "apic_77", "dend_38", "dend_39", "dend_34", "dend_35", "dend_36", "dend_37", "dend_30", "dend_31", "dend_32", "dend_33", "apic_45", "apic_44", "apic_47", "apic_46", "apic_41", "apic_40", "apic_43", "apic_42", "apic_49", "apic_48", "apic_31", "apic_52", "apic_53", "apic_50", "apic_51", "apic_56", "apic_57", "apic_54", "apic_55", "apic_58", "apic_59", "apic_6"], "apic_upper": ["apic_27", "apic_26", "apic_25", "apic_24", "apic_29", "apic_28", "apic_34", "apic_35", "apic_36", "apic_37", "apic_30", "apic_32", "apic_33", "apic_38", "apic_39", "apic_69", "apic_68", "apic_63", "apic_62", "apic_61", "apic_60", "apic_67", "apic_66", "apic_65", "apic_64", "apic_70", "apic_71", "apic_72", "apic_73", "apic_74", "apic_75", "apic_76", "apic_77", "apic_45", "apic_44", "apic_47", "apic_46", "apic_41", "apic_40", "apic_43", "apic_42", "apic_49", "apic_48", "apic_31", "apic_53", "apic_50", "apic_51", "apic_56", "apic_57", "apic_54", "apic_55", "apic_58", "apic_59"], "apical_tuft": ["apic_26", "apic_27", "apic_28", "apic_29", "apic_30", "apic_31", "apic_34", "apic_35", "apic_36", "apic_37", "apic_38", "apic_39", "apic_40", "apic_41", "apic_42", "apic_43", "apic_44", "apic_45", "apic_47", "apic_48", "apic_49", "apic_50", "apic_54", "apic_55", "apic_56", "apic_57", "apic_58", "apic_59", "apic_60", "apic_61", "apic_62", "apic_63", "apic_64", "apic_65", "apic_66", "apic_67", "apic_68", "apic_69", "apic_70", "apic_71", "apic_72", "apic_73", "apic_74", "apic_75", "apic_76", "apic_77"], "apical_beforebranchpoint": ["apic_0", "apic_1", "apic_2", "apic_3", "apic_4", "apic_5", "apic_6", "apic_7", "apic_8", "apic_9", "apic_10", "apic_11", "apic_12", "apic_13", "apic_14", "apic_15", "apic_16", "apic_17", "apic_18", "apic_19", "apic_20", "apic_21", "apic_22", "apic_79", "apic_80", "apic_81", "apic_82", "apic_83", "apic_84", "apic_85", "apic_86", "apic_87", "apic_88", "apic_89", "apic_90", "apic_91", "apic_92", "apic_93", "apic_94", "apic_95", "apic_96", "apic_97", "apic_98", "apic_99", "apic_100", "apic_101", "apic_102"]}, "conds": {"cellModel": "HH_full", "cellType": "PT"}}
\ No newline at end of file
diff --git a/netpyne/tutorials/cells/friesen.py b/netpyne/tutorials/cells/friesen.py
new file mode 100644
index 000000000..9f78fa8e7
--- /dev/null
+++ b/netpyne/tutorials/cells/friesen.py
@@ -0,0 +1,113 @@
+# translated from /u/samn/vcsim/geom.hoc $Id: geom.hoc,v 1.77 2009/09/14 15:14:03 samn Exp $
+#
+# requires mod/OFThresh.mod mod/OFThpo.mod and other Friesen ion channels
+
+from neuron import h
+
+class FCELL:
+  def __init__ (self,ID,ty,col=0,poflag=0):
+    self.ID=ID
+    self.ty=ty
+    self.col=col
+    self.poflag=poflag
+    self.initsoma()  #init soma params
+    self.initdend()  #init dend params
+    self.initaxon()  #init axon params
+    self.doconnect() #connect dend,soma,axon
+
+  #* utility function
+  # get microfarad/cm2 given tau in ms,gmax in nS,diam in microns,L in microns
+  def getcmdens (self,sec,tau,gm):
+    return 100*tau*gm/sec(0.5).area()
+
+  def initsoma (self):
+    self.soma = h.Section()
+    self.soma.diam = 30
+    self.soma.L = 30
+    self.soma.nseg = 1
+    self.soma.Ra = 1
+    self.soma.insert('pas')
+    self.gabaa1 = h.GABAa(0.5,sec=self.soma)
+
+  def initdend (self):
+    self.dend = h.Section()
+    self.dend.nseg = 1
+    self.dend.diam = 2
+    self.dend.L = 500
+    self.dend.Ra = 1
+    self.dend.insert('pas')
+    self.ampa = h.AMPA(0.5,sec=self.dend)
+    self.nmda = h.NMDA(0.5,sec=self.dend)
+    self.gabaa0 = h.GABAa(0.5,sec=self.dend)
+
+  def initaxon (self):
+    self.axon = h.Section()
+    self.axon.nseg = 1
+    self.axon.diam = 1
+    self.axon.L = 200
+    self.axon.Ra = 1
+    self.axon.insert('pas')
+    if self.poflag:
+      self.ofths = h.OFPO(0.5,sec=self.axon)
+    else:
+      self.ofths = h.OFTH(0.5,sec=self.axon)
+
+  def doconnect (self):
+    #h.connect(self.soma(0),self.axon(1)) #connect 0 end of soma to 0 end of axon
+    self.soma.connect(self.axon,1,0)
+    #h.connect(self.dend(0),self.soma(1)) #connect 0 end of dend to 1 end of soma
+    self.dend.connect(self.soma,1,0)
+
+  def setri (self,gsa,gsd,ga):
+    self.soma.Ra=1; self.soma.Ra=1e3/self.soma(0.5).ri()/gsa
+    self.dend.Ra=1; self.soma.Ra=1e3/self.dend(0.5).ri()/gsd
+    self.axon.Ra=1; self.axon.Ra=1e3/self.axon(0.5).ri()/ga
+
+  def printV (self):
+    print('dend.v=',self.dend(0.5).v)
+    print('soma.v=',self.soma(0.5).v)
+    print('axon.v=',self.axon(0.5).v)
+
+  def connect2target (self,targ):
+    return h.NetCon(self.ofths,targ)
+
+# Make a regular spiking Friesen cell - default params from /u/samn/vcsim/params.hoc
+def MakeRSFCELL ():
+  cell=FCELL(0,19)
+  cell.setri(85,50,50)
+  # soma
+  cell.soma.insert('A')
+  cell.soma.gmax_A = 145*0.1/cell.soma(0.5).area()
+  cell.soma.VhlfMaxm_A = -24
+  cell.soma.slopem_A = 3.2 #should stay pos
+  cell.soma.taum_A = 82
+  cell.soma.VhlfMaxh_A = 8
+  cell.soma.slopeh_A = -4.9 #should stay neg
+  cell.soma.tauh_A =	5
+  cell.soma.erev_A = -82
+  cell.soma.cm = 1 # cell.getcmdens(cell.soma,19,8.3)
+  cell.soma.e_pas = -65
+  cell.soma.g_pas = 8.3*0.1/cell.soma(0.5).area()
+  # dend
+  cell.dend.cm = cell.getcmdens(cell.dend,19,8.3)
+  cell.dend.e_pas = -65
+  cell.dend.g_pas = 8.3*0.1/cell.dend(0.5).area()
+  # axon
+  cell.axon.cm = cell.getcmdens(cell.axon,19,8.3)
+  cell.axon.e_pas = -65
+  cell.axon.g_pas = 8.3*0.1/cell.axon(0.5).area()
+  # friesen spiker
+  cell.ofths.apdur = 0.9 # action potential duration in ms
+  cell.ofths.refrac = 1e3/91 # absolute refractory period - 91Hz max frequency
+  cell.ofths.gkbase = 0.091 # 91nS == 0.091uS
+  cell.ofths.gkinc = 0.013 # 13nS == 0.013uS
+  cell.ofths.taugka = 69
+  cell.ofths.tauk = 2.3
+  cell.ofths.vth = -40
+  cell.ofths.vthinc = 0
+  cell.ofths.tauvtha = 1
+  cell.ofths.gnamax = 0.6 # 0.6nS == 0.6uS
+  cell.ofths.ena = 55
+  cell.ofths.ek = -82
+  cell.ofths.gkmin = 1e-5 # 0.01nS == 0.00001uS
+  return cell
diff --git a/netpyne/tutorials/cells/geom.hoc b/netpyne/tutorials/cells/geom.hoc
new file mode 100644
index 000000000..34a000ec6
--- /dev/null
+++ b/netpyne/tutorials/cells/geom.hoc
@@ -0,0 +1,3923 @@
+//* template E21
+begintemplate E21
+  public soma,dendrite
+  create soma, dendrite[1]
+
+proc init () { local i,j
+  ndend = 66
+  create soma, dendrite[ndend]
+  creat1()
+  creat2()
+  creat3()
+  creat4()
+  creat5()
+  creat6()
+  creat7()
+  creat8()
+  creat9()
+  creat10()
+  creat11()
+  creat12()
+}
+
+proc creat1 () {
+  access soma
+  {pt3dclear()}
+  {pt3dadd(-285,51,34,12)}
+  {pt3dadd(-295,59,42,12)}
+
+  {soma connect dendrite[0](0), 1}
+  {access dendrite[0]}
+  {pt3dclear()}
+  {pt3dadd(-289.373,55.791,39.707,8.392)}
+  {pt3dadd(-299.68,48.01,42.22,1.39)}
+  {pt3dadd(-301.68,47.4,42.22,1.39)}
+  {pt3dadd(-307.59,46.3,43.73,1.15)}
+  {pt3dadd(-311.13,45.17,44.56,1.15)}
+  {pt3dadd(-311.18,45.39,44.56,1.15)}
+
+  {dendrite[0] connect dendrite[1](0), 1}
+  {access dendrite[1]}
+  {pt3dclear()}
+  {pt3dadd(-311.18,45.39,44.56,1.15)}
+  {pt3dadd(-314.21,41.54,44.56,0.92)}
+
+  {dendrite[1] connect dendrite[2](0), 1}
+  {access dendrite[2]}
+  {pt3dclear()}
+  {pt3dadd(-314.21,41.54,44.56,0.92)}
+  {pt3dadd(-319.94,33.19,46.61,0.69)}
+  {pt3dadd(-320.11,29.8,44.12,0.46)}
+
+  {dendrite[2] connect dendrite[3](0), 1}
+  {access dendrite[3]}
+  {pt3dclear()}
+  {pt3dadd(-320.11,29.8,44.12,0.46)}
+  {pt3dadd(-323.63,27.99,45.63,0.46)}
+  {pt3dadd(-324.37,26.92,45.63,0.46)}
+
+  {dendrite[2] connect dendrite[4](0), 1}
+  {access dendrite[4]}
+  {pt3dclear()}
+  {pt3dadd(-320.11,29.8,44.12,0.46)}
+  {pt3dadd(-320.86,27.55,44.12,0.46)}
+  {pt3dadd(-322.95,27.39,44.12,0.46)}
+  {pt3dadd(-323.42,26.13,45.86,0.46)}
+  {pt3dadd(-324.57,24.74,47.07,0.46)}
+  {pt3dadd(-324.94,24.2,47.07,0.46)}
+  {pt3dadd(-324.85,23.75,47.07,0.46)}
+  {pt3dadd(-324.99,23.25,48.66,0.46)}
+  {pt3dadd(-324.78,22.12,49.04,0.46)}
+  {pt3dadd(-324.97,21.85,49.57,0.46)}
+  {pt3dadd(-324.58,19.81,49.65,0.46)}
+  {pt3dadd(-324.27,19.4,50.4,0.46)}
+  {pt3dadd(-325.09,18.77,50.56,0.46)}
+  {pt3dadd(-325.55,18.69,50.63,0.46)}
+  {pt3dadd(-325.96,18.38,50.71,0.46)}
+  {pt3dadd(-326.91,18.44,50.71,0.46)}
+  {pt3dadd(-328.2,16.55,53.51,0.46)}
+  {pt3dadd(-328.35,16.05,53.66,0.46)}
+  {pt3dadd(-328.76,15.74,53.74,0.46)}
+  {pt3dadd(-329.13,15.2,54.95,0.46)}
+  {pt3dadd(-329.72,14.62,58.51,0.46)}
+  {pt3dadd(-330.13,14.31,58.66,0.46)}
+  {pt3dadd(-330.36,14.27,58.96,0.46)}
+  {pt3dadd(-330.73,13.73,63.05,0.46)}
+  {pt3dadd(-330.91,13.46,63.13,0.46)}
+  {pt3dadd(-330.83,13.01,63.13,0.46)}
+  {pt3dadd(-330.97,12.51,63.13,0.46)}
+  {pt3dadd(-330.84,11.83,63.13,0.46)}
+  {pt3dadd(-331.03,11.56,63.13,0.46)}
+  {pt3dadd(-330.9,10.88,63.13,0.46)}
+  {pt3dadd(-330.55,10.24,63.13,0.46)}
+  {pt3dadd(-328.96,10.54,63.13,0.46)}
+  {pt3dadd(-329.27,10.95,63.13,0.46)}
+  {pt3dadd(-330.45,10.97,63.13,0.46)}
+  {pt3dadd(-331.17,11.06,63.13,0.46)}
+  {pt3dadd(-331.58,10.75,65.33,0.46)}
+  {pt3dadd(-332.41,10.13,66.16,0.46)}
+  {pt3dadd(-333.31,9.96,66.92,0.46)}
+  {pt3dadd(-333.5,9.69,66.92,0.46)}
+  {pt3dadd(-334.41,9.52,67.52,0.46)}
+  {pt3dadd(-335.04,9.17,67.52,0.46)}
+  {pt3dadd(-335.68,8.81,67.9,0.46)}
+  {pt3dadd(-336.32,8.46,67.9,0.46)}
+  {pt3dadd(-336.73,8.15,67.9,0.46)}
+  {pt3dadd(-337.15,7.83,68.58,0.46)}
+  {pt3dadd(-338.01,7.44,68.58,0.46)}
+  {pt3dadd(-338.46,7.35,68.81,0.46)}
+  {pt3dadd(-338.88,7.04,68.96,0.46)}
+  {pt3dadd(-340.01,6.83,69.19,0.46)}
+  {pt3dadd(-340.42,6.52,69.49,0.46)}
+  {pt3dadd(-341.1,6.39,69.72,0.46)}
+  {pt3dadd(-341.52,6.08,69.95,0.46)}
+  {pt3dadd(-343.79,5.65,70.63,0.46)}
+  {pt3dadd(-343.97,5.38,71.01,0.46)}
+  {pt3dadd(-345.11,5.17,71.46,0.46)}
+  {pt3dadd(-345.74,4.82,71.84,0.46)}
+  {pt3dadd(-352.55,3.54,71.92,0.46)}
+  {pt3dadd(-353.5,3.6,71.92,0.46)}
+  {pt3dadd(-356.91,2.96,71.92,0.46)}
+  {pt3dadd(-357.55,2.61,71.92,0.46)}
+  {pt3dadd(-358.19,2.25,71.92,0.46)}
+  {pt3dadd(-358.83,1.9,71.99,0.46)}
+  {pt3dadd(-359.24,1.59,71.99,0.46)}
+  {pt3dadd(-359.69,1.5,71.99,0.46)}
+  {pt3dadd(-360.29,0.92,71.99,0.46)}
+  {pt3dadd(-360.93,0.57,71.99,0.46)}
+  {pt3dadd(-361.38,0.48,71.99,0.46)}
+  {pt3dadd(-361.98,-0.1,71.99,0.46)}
+  {pt3dadd(-362.43,-0.18,71.99,0.46)}
+  {pt3dadd(-363.07,-0.54,71.99,0.46)}
+  {pt3dadd(-363.48,-0.85,71.99,0.46)}
+  {pt3dadd(-364.12,-1.2,71.99,0.46)}
+  {pt3dadd(-364.71,-1.78,71.99,0.46)}
+  {pt3dadd(-365.13,-2.1,72.14,0.46)}
+  {pt3dadd(-365.58,-2.18,72.14,0.46)}
+  {pt3dadd(-365.76,-2.45,72.52,0.46)}
+  {pt3dadd(-365.91,-2.95,72.9,0.46)}
+  {pt3dadd(-365.69,-4.08,73.2,0.46)}
+  {pt3dadd(-365.88,-4.35,73.36,0.46)}
+  {pt3dadd(-366.02,-4.85,74.42,0.46)}
+  {pt3dadd(-366.35,-5.61,74.8,0.46)}
+  {pt3dadd(-366.49,-6.11,75.55,0.46)}
+  {pt3dadd(-366.68,-6.38,75.55,0.46)}
+  {pt3dadd(-366.86,-6.65,75.55,0.46)}
+  {pt3dadd(-367.46,-7.23,75.86,0.46)}
+  {pt3dadd(-367.87,-7.54,75.93,0.46)}
+  {pt3dadd(-368.24,-8.08,76.16,0.46)}
+  {pt3dadd(-368.42,-8.35,76.39,0.46)}
+  {pt3dadd(-369.1,-8.47,76.54,0.46)}
+  {pt3dadd(-370.19,-8.91,76.54,0.46)}
+  {pt3dadd(-371.17,-11.19,75.7,0.46)}
+  {pt3dadd(-372.45,-11.9,75.7,0.46)}
+  {pt3dadd(-372.19,-13.26,75.7,0.46)}
+  {pt3dadd(-371.43,-13.58,75.7,0.46)}
+  {pt3dadd(-370.7,-13.68,75.7,0.46)}
+
+  {dendrite[1] connect dendrite[5](0), 1}
+  {access dendrite[5]}
+  {pt3dclear()}
+  {pt3dadd(-314.21,41.54,44.56,0.92)}
+  {pt3dadd(-323.35,38.89,46.91,0.46)}
+  {pt3dadd(-327.58,38.55,49.42,0.46)}
+  {pt3dadd(-332.25,38.38,49.42,0.46)}
+
+  {dendrite[5] connect dendrite[6](0), 1}
+  {access dendrite[6]}
+  {pt3dclear()}
+  {pt3dadd(-332.25,38.38,49.42,0.46)}
+  {pt3dadd(-335.47,38.01,49.42,0.46)}
+  {pt3dadd(-338.15,37.27,49.42,0.46)}
+  {pt3dadd(-340.39,39.2,49.42,0.46)}
+  {pt3dadd(-341.69,37.32,49.42,0.23)}
+  {pt3dadd(-342.85,38.51,49.42,0.23)}
+  {pt3dadd(-342.76,38.05,49.42,0.23)}
+  {pt3dadd(-342.91,37.56,49.42,0.23)}
+  {pt3dadd(-343.13,37.51,49.42,0.23)}
+  {pt3dadd(-343.32,37.25,49.42,0.23)}
+  {pt3dadd(-343.36,37.47,49.42,0.23)}
+  {pt3dadd(-344.13,37.8,49.42,0.23)}
+  {pt3dadd(-344.66,38.17,49.42,0.23)}
+  {pt3dadd(-344.83,39.07,49.42,0.23)}
+  {pt3dadd(-346.18,40,49.42,0.23)}
+  {pt3dadd(-346.68,40.14,49.42,0.23)}
+  {pt3dadd(-346.68,40.14,49.42,0.23)}
+  {pt3dadd(-347.53,40.92,49.42,0.23)}
+  {pt3dadd(-347.84,41.33,49.42,0.23)}
+  {pt3dadd(-348.07,41.29,49.42,0.23)}
+  {pt3dadd(-348.15,41.74,49.42,0.23)}
+  {pt3dadd(-348.42,41.93,49.42,0.23)}
+  {pt3dadd(-348.55,42.61,49.42,0.23)}
+  {pt3dadd(-348.36,42.87,49.42,0.23)}
+  {pt3dadd(-348.75,44.92,49.42,0.23)}
+  {pt3dadd(-349.02,45.1,49.42,0.23)}
+  {pt3dadd(-349.06,45.33,49.42,0.23)}
+  {pt3dadd(-349.55,45.47,49.42,0.23)}
+  {pt3dadd(-350.09,45.84,49.42,0.23)}
+  {pt3dadd(-350.14,46.07,49.42,0.23)}
+  {pt3dadd(-350.36,46.02,49.42,0.23)}
+  {pt3dadd(-350.9,46.39,49.42,0.23)}
+  {pt3dadd(-352.12,46.63,49.42,0.23)}
+  {pt3dadd(-352.43,47.04,49.42,0.23)}
+  {pt3dadd(-352.52,47.5,49.42,0.23)}
+  {pt3dadd(-352.75,47.45,49.42,0.23)}
+  {pt3dadd(-353.51,51.54,49.42,0.23)}
+  {pt3dadd(-353.82,51.95,49.42,0.23)}
+  {pt3dadd(-354.13,52.36,49.42,0.23)}
+  {pt3dadd(-354.18,52.59,49.42,0.23)}
+  {pt3dadd(-354.4,52.54,49.42,0.23)}
+  {pt3dadd(-354.44,52.77,49.42,0.23)}
+  {pt3dadd(-354.71,52.96,49.42,0.23)}
+  {pt3dadd(-354.94,52.91,49.42,0.23)}
+  {pt3dadd(-355.21,53.1,49.42,0.23)}
+  {pt3dadd(-356.43,53.34,49.42,0.23)}
+  {pt3dadd(-358.47,52.96,49.42,0.23)}
+  {pt3dadd(-359.11,52.6,49.42,0.23)}
+  {pt3dadd(-360.02,52.43,49.42,0.23)}
+  {pt3dadd(-360.06,52.66,49.42,0.23)}
+  {pt3dadd(-360.29,52.62,49.42,0.23)}
+  {pt3dadd(-360.33,52.84,49.42,0.23)}
+  {pt3dadd(-361.14,53.4,49.42,0.23)}
+  {pt3dadd(-361.18,53.62,49.42,0.23)}
+  {pt3dadd(-362.13,53.68,49.42,0.23)}
+  {pt3dadd(-362.36,53.64,49.42,0.23)}
+  {pt3dadd(-362.55,53.37,49.42,0.23)}
+  {pt3dadd(-362.5,53.14,49.42,0.23)}
+  {pt3dadd(-363.86,52.89,49.42,0.23)}
+  {pt3dadd(-364.05,52.62,49.42,0.23)}
+  {pt3dadd(-364.96,52.45,49.42,0.23)}
+  {pt3dadd(-365.14,52.18,49.42,0.23)}
+  {pt3dadd(-365.37,52.14,49.42,0.23)}
+  {pt3dadd(-365.33,51.91,49.42,0.23)}
+  {pt3dadd(-365.55,51.87,49.42,0.23)}
+
+  {dendrite[5] connect dendrite[7](0), 1}
+  {access dendrite[7]}
+  {pt3dclear()}
+  {pt3dadd(-332.25,38.38,49.42,0.46)}
+  {pt3dadd(-335.39,37.56,49.42,0.46)}
+  {pt3dadd(-339.57,36.07,49.42,0.46)}
+  {pt3dadd(-341.15,36.95,49.42,0.46)}
+  {pt3dadd(-344.24,35.9,49.42,0.46)}
+  {pt3dadd(-348.53,33.69,49.42,0.46)}
+  {pt3dadd(-351.05,30.87,49.42,0.46)}
+  {pt3dadd(-357.32,29.23,49.42,0.46)}
+  {pt3dadd(-357.74,28.91,49.42,0.46)}
+  {pt3dadd(-358.06,28.15,49.42,0.46)}
+  {pt3dadd(-358.47,27.84,49.42,0.46)}
+  {pt3dadd(-358.35,27.16,49.42,0.46)}
+  {pt3dadd(-358.57,27.11,49.42,0.46)}
+  {pt3dadd(-358.53,26.89,49.42,0.46)}
+  {pt3dadd(-358.76,26.84,49.42,0.46)}
+  {pt3dadd(-359.13,26.31,49.42,0.46)}
+  {pt3dadd(-362.3,25.71,49.42,0.46)}
+  {pt3dadd(-362.8,25.85,49.42,0.46)}
+  {pt3dadd(-363.26,25.77,49.42,0.46)}
+  {pt3dadd(-363.3,26,49.42,0.46)}
+  {pt3dadd(-363.75,25.91,49.42,0.46)}
+  {pt3dadd(-364.29,26.28,49.42,0.46)}
+  {pt3dadd(-364.33,26.51,49.42,0.46)}
+  {pt3dadd(-364.6,26.69,49.42,0.46)}
+  {pt3dadd(-366.65,26.31,49.42,0.46)}
+  {pt3dadd(-366.83,26.04,49.42,0.46)}
+  {pt3dadd(-367.06,26,49.42,0.46)}
+  {pt3dadd(-367.01,25.77,49.42,0.46)}
+  {pt3dadd(-367.47,25.68,49.42,0.46)}
+  {pt3dadd(-367.88,25.37,49.42,0.46)}
+  {pt3dadd(-368.79,25.2,49.42,0.46)}
+  {pt3dadd(-369.2,24.89,49.42,0.46)}
+  {pt3dadd(-372.61,24.25,49.42,0.46)}
+  {pt3dadd(-372.79,23.98,49.42,0.46)}
+  {pt3dadd(-373.24,23.9,49.42,0.46)}
+  {pt3dadd(-373.88,23.55,49.42,0.46)}
+  {pt3dadd(-374.11,23.5,49.42,0.46)}
+  {pt3dadd(-374.52,23.19,49.42,0.46)}
+  {pt3dadd(-378.15,22.51,49.42,0.46)}
+  {pt3dadd(-378.34,22.24,49.42,0.46)}
+  {pt3dadd(-381.06,21.73,49.42,0.46)}
+  {pt3dadd(-381.33,21.92,49.42,0.46)}
+  {pt3dadd(-382.32,22.2,49.42,0.46)}
+  {pt3dadd(-383.91,21.9,49.42,0.46)}
+  {pt3dadd(-384.41,22.05,49.42,0.46)}
+  {pt3dadd(-389.63,21.07,49.42,0.46)}
+  {pt3dadd(-389.9,21.25,49.42,0.46)}
+  {pt3dadd(-390.58,21.13,49.42,0.46)}
+  {pt3dadd(-391.39,21.68,49.42,0.46)}
+  {pt3dadd(-392.16,22,49.42,0.46)}
+  {pt3dadd(-392.38,21.96,49.42,0.46)}
+  {pt3dadd(-392.92,22.33,49.42,0.46)}
+  {pt3dadd(-393.15,22.29,49.42,0.46)}
+  {pt3dadd(-393.19,22.52,49.42,0.46)}
+  {pt3dadd(-393.64,22.43,49.42,0.46)}
+  {pt3dadd(-393.91,22.61,49.42,0.46)}
+  {pt3dadd(-395.05,22.4,49.42,0.46)}
+  {pt3dadd(-395.77,22.5,49.42,0.46)}
+  {pt3dadd(-396.45,22.37,49.42,0.46)}
+  {pt3dadd(-397.67,22.62,49.42,0.46)}
+  {pt3dadd(-397.72,22.84,49.42,0.46)}
+  {pt3dadd(-397.99,23.03,49.42,0.46)}
+  {pt3dadd(-398.71,23.13,49.42,0.46)}
+  {pt3dadd(-399.62,22.96,49.42,0.46)}
+  {pt3dadd(-400.79,22.97,49.42,0.46)}
+  {pt3dadd(-405.33,22.12,49.42,0.46)}
+  {pt3dadd(-405.83,22.26,49.42,0.46)}
+  {pt3dadd(-410.14,21.46,49.42,0.46)}
+  {pt3dadd(-410.41,21.64,49.42,0.46)}
+  {pt3dadd(-412,21.34,49.42,0.46)}
+  {pt3dadd(-412.95,21.4,49.42,0.46)}
+  {pt3dadd(-413.86,21.23,49.42,0.46)}
+  {pt3dadd(-413.9,21.46,49.42,0.46)}
+  {pt3dadd(-415.89,22.03,49.42,0.46)}
+  {pt3dadd(-416.16,22.21,49.42,0.46)}
+  {pt3dadd(-417.83,22.37,49.42,0.46)}
+}
+
+proc creat2 () {
+  {dendrite[0] connect dendrite[8](0), 1}
+  {access dendrite[8]}
+  {pt3dclear()}
+  {pt3dadd(-311.18,45.39,44.56,1.15)}
+  {pt3dadd(-314.57,45.93,45.55,0.46)}
+  {pt3dadd(-316.55,46.5,45.55,0.46)}
+  {pt3dadd(-316.37,46.77,45.55,0.46)}
+  {pt3dadd(-316.58,47.9,45.55,0.46)}
+  {pt3dadd(-319.1,47.18,49.42,0.23)}
+  {pt3dadd(-319.38,46.18,49.42,0.23)}
+  {pt3dadd(-320.02,45.83,49.42,0.23)}
+  {pt3dadd(-320.43,45.52,49.42,0.23)}
+  {pt3dadd(-320.56,46.2,49.42,0.23)}
+  {pt3dadd(-320.37,46.47,49.42,0.23)}
+  {pt3dadd(-320.37,46.47,49.42,0.46)}
+  {pt3dadd(-321,47.29,49.42,0.46)}
+  {pt3dadd(-321.42,49.56,49.42,0.46)}
+  {pt3dadd(-321.28,50.06,49.42,0.46)}
+  {pt3dadd(-321.09,50.32,49.42,0.46)}
+  {pt3dadd(-321.18,50.78,49.42,0.46)}
+  {pt3dadd(-320.99,51.05,49.42,0.46)}
+  {pt3dadd(-323.29,52.03,51.77,0.23)}
+  {pt3dadd(-323.7,51.71,56.31,0.23)}
+  {pt3dadd(-323.58,51.03,55.02,0.23)}
+  {pt3dadd(-324.09,50,55.02,0.23)}
+  {pt3dadd(-324.27,49.73,55.02,0.23)}
+  {pt3dadd(-324.95,49.6,55.02,0.23)}
+  {pt3dadd(-325.68,49.7,55.02,0.23)}
+  {pt3dadd(-326.17,49.84,55.02,0.23)}
+  {pt3dadd(-331.62,48.82,55.02,0.23)}
+  {pt3dadd(-332.39,49.15,55.02,0.23)}
+  {pt3dadd(-333.15,49.48,56.77,0.23)}
+  {pt3dadd(-334.23,50.21,57.75,0.23)}
+  {pt3dadd(-334.5,50.4,58.13,0.23)}
+  {pt3dadd(-334.81,50.81,58.13,0.23)}
+  {pt3dadd(-335.08,50.99,58.28,0.23)}
+  {pt3dadd(-335.58,51.14,60.86,0.23)}
+  {pt3dadd(-336.94,50.88,61.08,0.23)}
+  {pt3dadd(-336.98,51.11,61.54,0.23)}
+
+  {dendrite[8] connect dendrite[9](0), 1}
+  {access dendrite[9]}
+  {pt3dclear()}
+  {pt3dadd(-336.98,51.11,61.54,0.23)}
+  {pt3dadd(-340.58,52.78,61.54,0.23)}
+  {pt3dadd(-341.04,52.7,62.6,0.23)}
+  {pt3dadd(-340.95,52.24,62.52,0.23)}
+  {pt3dadd(-341.41,52.16,62.52,0.23)}
+  {pt3dadd(-343.58,52.46,62.52,0.23)}
+  {pt3dadd(-344.3,52.56,62.52,0.23)}
+  {pt3dadd(-344.68,54.6,63.05,0.23)}
+  {pt3dadd(-345.53,55.38,63.05,0.23)}
+  {pt3dadd(-346.26,55.48,63.05,0.23)}
+  {pt3dadd(-347.07,56.03,63.05,0.23)}
+  {pt3dadd(-347.15,56.48,63.05,0.23)}
+  {pt3dadd(-347.46,56.89,63.05,0.23)}
+  {pt3dadd(-347.77,57.31,63.05,0.23)}
+  {pt3dadd(-348.31,57.67,63.05,0.23)}
+  {pt3dadd(-348.46,57.18,63.05,0.23)}
+  {pt3dadd(-348.64,56.91,63.58,0.23)}
+  {pt3dadd(-349.32,56.78,64.04,0.23)}
+  {pt3dadd(-349.68,57.42,64.87,0.23)}
+  {pt3dadd(-349.95,57.6,64.87,0.23)}
+  {pt3dadd(-350.53,58.2,64.87,0.23)}
+  {pt3dadd(-351.02,58.34,64.87,0.23)}
+  {pt3dadd(-351.48,58.26,65.18,0.23)}
+  {pt3dadd(-352.43,58.31,65.48,0.23)}
+  {pt3dadd(-352.6,59.22,67.22,0.23)}
+  {pt3dadd(-352.41,59.49,67.52,0.23)}
+  {pt3dadd(-352.04,60.03,67.83,0.23)}
+  {pt3dadd(-351.86,60.3,68.81,0.23)}
+  {pt3dadd(-351.45,60.61,68.81,0.23)}
+  {pt3dadd(-350.67,61.46,68.81,0.23)}
+  {pt3dadd(-350.52,61.96,68.81,0.23)}
+  {pt3dadd(-348.83,64.12,70.33,0.23)}
+  {pt3dadd(-349.21,66.16,71.61,0.23)}
+  {pt3dadd(-349.79,66.76,74.19,0.23)}
+  {pt3dadd(-350.87,67.49,74.19,0.23)}
+  {pt3dadd(-351.64,67.82,74.57,0.23)}
+  {pt3dadd(-352.36,67.92,75.17,0.23)}
+  {pt3dadd(-352.86,68.06,75.33,0.23)}
+  {pt3dadd(-354.08,68.3,75.63,0.23)}
+  {pt3dadd(-354.76,68.18,75.63,0.23)}
+  {pt3dadd(-355.26,68.32,76.08,0.23)}
+  {pt3dadd(-355.75,68.46,76.08,0.23)}
+  {pt3dadd(-356.25,68.6,76.39,0.23)}
+  {pt3dadd(-356.8,71.55,77.83,0.23)}
+  {pt3dadd(-356.62,71.82,77.98,0.23)}
+  {pt3dadd(-356.43,72.09,77.98,0.23)}
+  {pt3dadd(-356.52,72.54,78.58,0.23)}
+  {pt3dadd(-356.33,72.81,78.89,0.23)}
+  {pt3dadd(-356.15,73.08,78.89,0.23)}
+  {pt3dadd(-356,73.58,79.64,0.23)}
+  {pt3dadd(-355.86,74.07,79.64,0.23)}
+  {pt3dadd(-355.68,74.34,80.02,0.23)}
+  {pt3dadd(-355.27,74.65,80.4,0.23)}
+  {pt3dadd(-354.9,75.19,80.55,0.23)}
+  {pt3dadd(-354.8,75.92,80.63,0.23)}
+  {pt3dadd(-354.65,76.41,80.63,0.23)}
+  {pt3dadd(-354.97,76.82,80.63,0.23)}
+  {pt3dadd(-355.05,77.28,81.54,0.23)}
+  {pt3dadd(-355.86,77.83,80.78,0.23)}
+  {pt3dadd(-356.13,78.01,80.78,0.23)}
+
+  {dendrite[9] connect dendrite[10](0), 1}
+  {access dendrite[10]}
+  {pt3dclear()}
+  {pt3dadd(-356.13,78.01,80.78,0.23)}
+  {pt3dadd(-357.89,78.62,80.78,0.23)}
+
+  {dendrite[9] connect dendrite[11](0), 1}
+  {access dendrite[11]}
+  {pt3dclear()}
+  {pt3dadd(-356.13,78.01,80.78,0.23)}
+  {pt3dadd(-355.32,81.22,79.8,0.23)}
+
+  {dendrite[8] connect dendrite[12](0), 1}
+  {access dendrite[12]}
+  {pt3dclear()}
+  {pt3dadd(-336.98,51.11,61.54,0.23)}
+  {pt3dadd(-338.34,50.85,62.07,0.23)}
+  {pt3dadd(-338.53,50.58,63.43,0.23)}
+  {pt3dadd(-338.9,50.05,63.43,0.23)}
+  {pt3dadd(-339.31,49.73,63.43,0.23)}
+  {pt3dadd(-339.49,49.46,63.43,0.23)}
+  {pt3dadd(-340.18,49.34,63.43,0.23)}
+  {pt3dadd(-340.59,49.03,63.43,0.23)}
+  {pt3dadd(-341,48.71,63.43,0.23)}
+  {pt3dadd(-341.18,48.44,63.43,0.23)}
+  {pt3dadd(-341.59,48.13,63.43,0.23)}
+  {pt3dadd(-342.5,47.96,63.81,0.23)}
+  {pt3dadd(-343.04,48.33,64.19,0.23)}
+  {pt3dadd(-343.13,48.79,64.49,0.23)}
+  {pt3dadd(-342.63,48.64,66.16,0.23)}
+  {pt3dadd(-342.32,48.23,66.16,0.23)}
+  {pt3dadd(-342.23,47.78,66.16,0.23)}
+  {pt3dadd(-341.96,47.59,66.16,0.23)}
+  {pt3dadd(-341.75,46.46,67.52,0.23)}
+  {pt3dadd(-342.43,46.33,67.68,0.23)}
+  {pt3dadd(-342.84,46.02,67.68,0.23)}
+  {pt3dadd(-343.89,45.35,67.9,0.23)}
+  {pt3dadd(-344.08,45.09,69.57,0.23)}
+  {pt3dadd(-344.49,44.77,70.63,0.23)}
+  {pt3dadd(-344.59,44.05,71.16,0.23)}
+  {pt3dadd(-344.46,43.37,74.95,0.23)}
+  {pt3dadd(-343.97,43.23,76.54,0.23)}
+  {pt3dadd(-343.65,42.82,77.9,0.23)}
+  {pt3dadd(-343.39,42.63,77.9,0.23)}
+  {pt3dadd(-343.26,41.95,78.05,0.23)}
+  {pt3dadd(-342.99,41.77,78.2,0.23)}
+  {pt3dadd(-343.17,41.5,78.2,0.23)}
+  {pt3dadd(-343.85,41.37,78.43,0.23)}
+  {pt3dadd(-344.27,41.06,79.19,0.23)}
+  {pt3dadd(-344.76,41.2,79.19,0.23)}
+  {pt3dadd(-345.03,41.39,79.19,0.23)}
+
+  {soma connect dendrite[13](0), 1}
+  {access dendrite[13]}
+  {pt3dclear()}
+  {pt3dadd(-288.687,56.887,38.53,8.968)}
+  {pt3dadd(-296.96,61.37,43.2,1.62)}
+  {pt3dadd(-298.25,66.99,40.63,1.62)}
+  {pt3dadd(-302.62,76.51,39.94,1.39)}
+
+  {dendrite[13] connect dendrite[14](0), 1}
+  {access dendrite[14]}
+  {pt3dclear()}
+  {pt3dadd(-302.62,76.51,39.94,1.39)}
+  {pt3dadd(-301.62,83.74,39.94,0.92)}
+
+  {dendrite[14] connect dendrite[15](0), 1}
+  {access dendrite[15]}
+  {pt3dclear()}
+  {pt3dadd(-301.62,83.74,39.94,0.92)}
+  {pt3dadd(-305.48,86.77,36.38,0.69)}
+  {pt3dadd(-309.32,90.98,34.11,0.46)}
+  {pt3dadd(-309.49,91.89,34.11,0.46)}
+  {pt3dadd(-309.8,92.3,34.11,0.46)}
+  {pt3dadd(-310.39,95.48,33.2,0.46)}
+  {pt3dadd(-310.71,95.89,33.05,0.46)}
+  {pt3dadd(-311.2,96.03,32.44,0.46)}
+  {pt3dadd(-311.66,95.94,32.44,0.46)}
+  {pt3dadd(-312.11,95.86,31.46,0.46)}
+  {pt3dadd(-312.3,95.59,31.46,0.46)}
+  {pt3dadd(-312.71,95.28,31.46,0.46)}
+  {pt3dadd(-312.89,95.01,31.46,0.46)}
+  {pt3dadd(-313.3,94.7,31.46,0.46)}
+  {pt3dadd(-313.98,94.57,31.46,0.46)}
+  {pt3dadd(-314.57,95.17,31.46,0.46)}
+  {pt3dadd(-314.88,95.58,31.46,0.46)}
+  {pt3dadd(-315.19,95.99,31.23,0.46)}
+  {pt3dadd(-315.5,96.4,31.23,0.46)}
+  {pt3dadd(-315.59,96.85,29.34,0.46)}
+  {pt3dadd(-316.62,97.36,29.34,0.46)}
+  {pt3dadd(-317.34,97.46,29.34,0.46)}
+  {pt3dadd(-318.15,98.02,29.26,0.46)}
+  {pt3dadd(-318.69,98.39,29.26,0.46)}
+  {pt3dadd(-318.95,99.75,29.26,0.46)}
+  {pt3dadd(-319.53,100.34,29.26,0.46)}
+  {pt3dadd(-320.29,100.67,29.26,0.46)}
+  {pt3dadd(-321.2,100.5,29.26,0.46)}
+  {pt3dadd(-321.61,100.19,29.26,0.46)}
+  {pt3dadd(-322.02,99.87,23.35,0.46)}
+  {pt3dadd(-324.29,99.45,22.75,0.46)}
+  {pt3dadd(-325.56,99.92,22.75,0.46)}
+  {pt3dadd(-326.51,99.97,22.75,0.46)}
+  {pt3dadd(-328.55,99.59,21.54,0.46)}
+  {pt3dadd(-329.32,99.92,21.54,0.46)}
+  {pt3dadd(-329.81,100.06,21.38,0.46)}
+  {pt3dadd(-330.31,100.2,21.31,0.46)}
+  {pt3dadd(-330.85,100.57,21.31,0.46)}
+  {pt3dadd(-331.34,100.71,20.93,0.46)}
+  {pt3dadd(-331.66,101.12,20.63,0.46)}
+  {pt3dadd(-332.15,101.27,20.63,0.46)}
+  {pt3dadd(-334.65,100.8,20.63,0.46)}
+  {pt3dadd(-334.83,100.53,20.63,0.46)}
+  {pt3dadd(-335.25,100.22,20.63,0.46)}
+  {pt3dadd(-335.66,99.91,20.63,0.46)}
+  {pt3dadd(-335.84,99.64,20.63,0.46)}
+
+  {dendrite[15] connect dendrite[16](0), 1}
+  {access dendrite[16]}
+  {pt3dclear()}
+  {pt3dadd(-335.84,99.64,20.63,0.46)}
+  {pt3dadd(-337.13,101.51,20.55,0.23)}
+  {pt3dadd(-338.86,104.47,20.55,0.23)}
+  {pt3dadd(-341.68,103.8,20.56,0.23)}
+  {pt3dadd(-347.84,103.56,20.56,0.23)}
+  {pt3dadd(-350.21,104.35,18.97,0.23)}
+  {pt3dadd(-353.39,103.72,18.97,0.23)}
+  {pt3dadd(-354.85,103.8,18.97,0.23)}
+  {pt3dadd(-355.19,104.19,18.97,0.23)}
+  {pt3dadd(-356.49,104.55,18.97,0.23)}
+  {pt3dadd(-357.05,104.88,18.13,0.23)}
+  {pt3dadd(-357.46,105.48,18.06,0.23)}
+  {pt3dadd(-357.8,105.87,18.06,0.23)}
+  {pt3dadd(-358.17,107.2,17.98,0.23)}
+  {pt3dadd(-358.57,107.81,17.6,0.23)}
+  {pt3dadd(-359.42,108.3,17.45,0.23)}
+  {pt3dadd(-360.72,108.66,17.23,0.23)}
+  {pt3dadd(-361.22,108.76,17.23,0.23)}
+  {pt3dadd(-361.73,108.87,16.09,0.23)}
+  {pt3dadd(-362.8,109.29,15.41,0.23)}
+  {pt3dadd(-363.65,109.78,15.33,0.23)}
+  {pt3dadd(-364.5,110.26,15.33,0.23)}
+  {pt3dadd(-365.52,110.46,15.26,0.23)}
+  {pt3dadd(-367.74,109.86,15.26,0.23)}
+  {pt3dadd(-367.97,109.8,13.82,0.23)}
+  {pt3dadd(-368.88,110.51,13.82,0.23)}
+  {pt3dadd(-369.22,110.89,13.21,0.23)}
+  {pt3dadd(-369.63,111.5,13.06,0.23)}
+  {pt3dadd(-370.42,111.76,13.06,0.23)}
+  {pt3dadd(-370.86,111.64,13.06,0.23)}
+  {pt3dadd(-372.79,112.55,13.06,0.23)}
+  {pt3dadd(-373.92,113.2,13.06,0.23)}
+  {pt3dadd(-375.57,113.95,14.12,0.23)}
+  {pt3dadd(-375.93,117.92,9.27,0.23)}
+  {pt3dadd(-375.93,117.92,8.89,0.23)}
+  {pt3dadd(-377.29,118.5,8.21,0.23)}
+  {pt3dadd(-377.29,118.5,8.14,0.23)}
+  {pt3dadd(-377.29,121.13,7.08,0.23)}
+  {pt3dadd(-377.35,121.36,7.08,0.23)}
+  {pt3dadd(-379.01,123.06,6.92,0.23)}
+  {pt3dadd(-380.18,125.61,4.73,0.23)}
+
+  {dendrite[15] connect dendrite[17](0), 1}
+  {access dendrite[17]}
+  {pt3dclear()}
+  {pt3dadd(-335.84,99.64,20.63,0.46)}
+  {pt3dadd(-337.96,97.13,20.63,0.23)}
+  {pt3dadd(-339.94,98.3,20.63,0.23)}
+  {pt3dadd(-340.2,100.14,19.04,0.23)}
+  {pt3dadd(-341.34,100.79,18.29,0.23)}
+  {pt3dadd(-342.35,100.99,18.29,0.23)}
+  {pt3dadd(-343.53,100.91,18.29,0.23)}
+  {pt3dadd(-344.03,101.01,18.29,0.23)}
+  {pt3dadd(-344.54,101.11,18.29,0.23)}
+  {pt3dadd(-344.82,101.27,18.21,0.23)}
+  {pt3dadd(-345.17,101.66,18.06,0.23)}
+  {pt3dadd(-345.67,101.76,18.06,0.23)}
+  {pt3dadd(-345.85,102.43,16.47,0.23)}
+  {pt3dadd(-344.56,104.69,13.36,0.23)}
+  {pt3dadd(-344.8,105.58,13.29,0.23)}
+  {pt3dadd(-345.71,106.29,12.3,0.23)}
+  {pt3dadd(-346.74,110.08,12.3,0.23)}
+  {pt3dadd(-346.42,110.64,12.3,0.23)}
+  {pt3dadd(-346.25,110.93,12.3,0.23)}
+  {pt3dadd(-345.93,111.49,12.3,0.23)}
+  {pt3dadd(-345.48,111.62,12.3,0.23)}
+  {pt3dadd(-345.1,111.96,12.3,0.23)}
+  {pt3dadd(-344.71,112.3,12.3,0.23)}
+  {pt3dadd(-344.27,112.42,12.3,0.23)}
+  {pt3dadd(-344.29,113.38,11.39,0.23)}
+  {pt3dadd(-344.25,114.1,11.24,0.23)}
+  {pt3dadd(-344.37,114.55,11.17,0.23)}
+  {pt3dadd(-344.21,114.83,11.01,0.23)}
+  {pt3dadd(-344.93,117.5,9.2,0.23)}
+  {pt3dadd(-344.89,118.23,8.89,0.23)}
+  {pt3dadd(-344.85,118.96,8.51,0.23)}
+  {pt3dadd(-345.4,120.97,8.51,0.23)}
+  {pt3dadd(-345.36,121.69,8.51,0.23)}
+  {pt3dadd(-345.48,122.14,8.51,0.23)}
+  {pt3dadd(-345.38,122.65,8.51,0.23)}
+  {pt3dadd(-345.8,124.2,8.51,0.23)}
+}
+
+proc creat3 () {
+  {dendrite[14] connect dendrite[18](0), 1}
+  {access dendrite[18]}
+  {pt3dclear()}
+  {pt3dadd(-301.62,83.74,39.94,0.92)}
+  {pt3dadd(-298.52,88.54,39.94,0.69)}
+  {pt3dadd(-297.15,89.97,39.72,0.69)}
+
+  {dendrite[18] connect dendrite[19](0), 1}
+  {access dendrite[19]}
+  {pt3dclear()}
+  {pt3dadd(-297.15,89.97,39.72,0.69)}
+  {pt3dadd(-296.09,94.4,42.14,0.69)}
+  {pt3dadd(-296.14,94.62,42.14,0.69)}
+  {pt3dadd(-295.23,102.31,42.14,0.69)}
+
+  {dendrite[19] connect dendrite[20](0), 1}
+  {access dendrite[20]}
+  {pt3dclear()}
+  {pt3dadd(-295.23,102.31,42.14,0.69)}
+  {pt3dadd(-294.15,105.33,39.03,0.69)}
+  {pt3dadd(-291.97,108.78,39.03,0.69)}
+  {pt3dadd(-289.76,112.02,36.99,0.69)}
+  {pt3dadd(-288.59,118.34,35.63,0.69)}
+  {pt3dadd(-285.99,123.28,35.63,0.69)}
+  {pt3dadd(-282.23,134.55,35.63,0.69)}
+  {pt3dadd(-281.21,138.05,35.78,0.69)}
+
+  {dendrite[20] connect dendrite[21](0), 1}
+  {access dendrite[21]}
+  {pt3dclear()}
+  {pt3dadd(-281.21,138.05,35.78,0.69)}
+  {pt3dadd(-285.18,142.94,35.78,0.46)}
+  {pt3dadd(-287.73,145.28,35.78,0.46)}
+  {pt3dadd(-287.82,145.73,36.76,0.46)}
+  {pt3dadd(-288.35,146.1,36.76,0.46)}
+  {pt3dadd(-288.67,146.51,35.85,0.46)}
+  {pt3dadd(-288.75,146.97,35.55,0.46)}
+  {pt3dadd(-289.02,147.15,35.55,0.46)}
+  {pt3dadd(-289.33,147.56,35.25,0.46)}
+  {pt3dadd(-289.83,147.7,35.25,0.46)}
+  {pt3dadd(-290.14,148.11,35.25,0.46)}
+  {pt3dadd(-290.95,148.67,34.79,0.46)}
+  {pt3dadd(-291.22,148.85,34.72,0.46)}
+  {pt3dadd(-291.49,149.04,34.72,0.46)}
+  {pt3dadd(-292.21,149.14,34.72,0.46)}
+  {pt3dadd(-292.71,149.28,34.72,0.46)}
+  {pt3dadd(-293.2,149.42,34.72,0.46)}
+  {pt3dadd(-293.7,149.56,34.72,0.46)}
+  {pt3dadd(-294.42,149.66,34.72,0.46)}
+  {pt3dadd(-294.92,149.8,34.72,0.46)}
+  {pt3dadd(-295.42,149.94,34.72,0.46)}
+  {pt3dadd(-296.41,150.23,34.72,0.46)}
+  {pt3dadd(-296.91,150.37,34.72,0.46)}
+  {pt3dadd(-297.18,150.55,34.72,0.46)}
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+  {pt3dadd(-132.27,193.61,23.51,0.46)}
+  {pt3dadd(-131.82,193.69,23.35,0.46)}
+  {pt3dadd(-131.45,194.23,23.13,0.46)}
+  {pt3dadd(-131.31,194.73,23.13,0.46)}
+  {pt3dadd(-131.12,194.99,23.13,0.46)}
+  {pt3dadd(-130.94,195.26,23.13,0.46)}
+  {pt3dadd(-129.81,200.4,23.13,0.46)}
+  {pt3dadd(-130.02,205.29,20.93,0.46)}
+  {pt3dadd(-130.19,204.87,23.74,0.23)}
+}
+
+proc creat4 () {
+  {dendrite[22] connect dendrite[23](0), 1}
+  {access dendrite[23]}
+  {pt3dclear()}
+  {pt3dadd(-130.19,204.87,23.74,0.23)}
+  {pt3dadd(-124.83,200.4,32.68,0.23)}
+  {pt3dadd(-119.54,202.08,32.68,0.23)}
+  {pt3dadd(-113.49,201.81,31.16,0.23)}
+  {pt3dadd(-113.49,201.81,30.78,0.23)}
+  {pt3dadd(-110.31,202.44,30.78,0.23)}
+  {pt3dadd(-108.63,202.42,30.78,0.23)}
+  {pt3dadd(-105.12,203.61,30.78,0.23)}
+  {pt3dadd(-104.74,203.96,30.78,0.23)}
+  {pt3dadd(-103.4,204.32,30.78,0.23)}
+  {pt3dadd(-102.33,203.9,30.78,0.23)}
+  {pt3dadd(-101.76,203.57,29.88,0.23)}
+  {pt3dadd(-100.75,203.37,29.42,0.23)}
+  {pt3dadd(-99.67,202.95,29.42,0.23)}
+  {pt3dadd(-98.88,202.68,29.42,0.23)}
+  {pt3dadd(-96.35,202.18,29.42,0.23)}
+  {pt3dadd(-95.46,202.42,29.35,0.23)}
+  {pt3dadd(-94.85,202.82,29.35,0.23)}
+  {pt3dadd(-94.02,203.29,29.04,0.23)}
+  {pt3dadd(-88.45,204.81,28.51,0.23)}
+  {pt3dadd(-87.78,204.99,27.98,0.23)}
+  {pt3dadd(-86.95,205.45,26.77,0.23)}
+  {pt3dadd(-86.12,205.92,26.77,0.23)}
+  {pt3dadd(-85.51,206.32,26.62,0.23)}
+  {pt3dadd(-85.13,206.67,26.62,0.23)}
+  {pt3dadd(-84.46,206.85,26.62,0.23)}
+  {pt3dadd(-83.85,207.25,26.62,0.23)}
+  {pt3dadd(-80.96,208.04,26.01,0.23)}
+  {pt3dadd(-78.48,207.76,25.79,0.23)}
+  {pt3dadd(-76.62,207.07,25.79,0.23)}
+  {pt3dadd(-75.49,206.42,25.03,0.23)}
+  {pt3dadd(-74.76,206.38,25.03,0.23)}
+  {pt3dadd(-73.58,206.46,24.8,0.23)}
+  {pt3dadd(-71.3,206.85,24.8,0.23)}
+  {pt3dadd(-70.79,206.75,24.8,0.23)}
+  {pt3dadd(-68.05,207.25,24.8,0.23)}
+  {pt3dadd(-67.1,207.27,24.72,0.23)}
+  {pt3dadd(-66.43,207.45,22.76,0.23)}
+  {pt3dadd(-65.14,207.09,22.3,0.23)}
+  {pt3dadd(-62.79,207.25,21.47,0.23)}
+  {pt3dadd(-61.61,207.33,21.39,0.23)}
+  {pt3dadd(-60.66,207.35,20.79,0.23)}
+  {pt3dadd(-57.99,208.08,20.79,0.23)}
+  {pt3dadd(-57.04,208.1,19.57,0.23)}
+  {pt3dadd(-52.8,209.25,18.97,0.23)}
+  {pt3dadd(-52.07,209.21,18.97,0.23)}
+  {pt3dadd(-50.74,209.58,18.97,0.23)}
+  {pt3dadd(-45.74,210.45,18.97,0.23)}
+  {pt3dadd(-45.25,211.3,18.97,0.23)}
+  {pt3dadd(-44.98,212.08,18.97,0.23)}
+  {pt3dadd(-42.65,213.2,18.97,0.23)}
+  {pt3dadd(-41.88,213.89,18.97,0.23)}
+  {pt3dadd(-41.11,214.57,18.97,0.23)}
+  {pt3dadd(-41.01,215.08,18.97,0.23)}
+  {pt3dadd(-40.85,215.36,18.97,0.23)}
+  {pt3dadd(-40.53,215.93,18.97,0.23)}
+  {pt3dadd(-40.43,216.44,18.97,0.23)}
+  {pt3dadd(-40.04,216.78,18.97,0.23)}
+  {pt3dadd(-39.94,217.29,18.97,0.23)}
+  {pt3dadd(-39.78,217.57,18.97,0.23)}
+  {pt3dadd(-39.67,218.08,18.97,0.23)}
+  {pt3dadd(-37.73,221.48,18.97,0.23)}
+  {pt3dadd(-37.4,222.04,18.97,0.23)}
+  {pt3dadd(-36.76,223.18,18.97,0.23)}
+  {pt3dadd(-36.21,223.8,18.97,0.23)}
+  {pt3dadd(-35.56,224.94,18.97,0.23)}
+  {pt3dadd(-35.4,225.22,18.97,0.23)}
+  {pt3dadd(-35.07,225.79,18.97,0.23)}
+  {pt3dadd(-34.53,226.41,18.97,0.23)}
+  {pt3dadd(-33.82,227.32,18.97,0.23)}
+  {pt3dadd(-33.65,227.61,18.97,0.23)}
+  {pt3dadd(-32.88,228.3,18.97,0.23)}
+  {pt3dadd(-32.17,229.21,18.97,0.23)}
+  {pt3dadd(-31.57,229.61,18.97,0.23)}
+  {pt3dadd(-30.96,230.02,18.97,0.23)}
+  {pt3dadd(-30.57,230.36,18.97,0.23)}
+  {pt3dadd(-29.97,230.76,18.97,0.23)}
+  {pt3dadd(-29.52,230.89,18.97,0.23)}
+  {pt3dadd(-28.81,231.8,18.97,0.23)}
+  {pt3dadd(-27.66,232.83,18.97,0.23)}
+  {pt3dadd(-27.49,233.11,18.97,0.23)}
+  {pt3dadd(-27.33,233.4,18.97,0.23)}
+  {pt3dadd(-26.72,233.8,18.97,0.23)}
+  {pt3dadd(-26.34,234.14,18.97,0.23)}
+  {pt3dadd(-25.51,234.61,18.97,0.23)}
+  {pt3dadd(-24.68,235.07,18.97,0.23)}
+  {pt3dadd(-24.01,235.26,18.97,0.23)}
+  {pt3dadd(-23.02,236.01,18.97,0.23)}
+  {pt3dadd(-16.11,237.89,18.97,0.23)}
+  {pt3dadd(-13.27,238.9,18.97,0.23)}
+  {pt3dadd(-11.77,239.55,18.97,0.23)}
+  {pt3dadd(-7.76,240.64,18.97,0.23)}
+  {pt3dadd(-5.25,241.08,18.97,0.23)}
+  {pt3dadd(-4.24,240.88,18.97,0.23)}
+  {pt3dadd(-3.06,240.96,18.97,0.23)}
+  {pt3dadd(-1.83,240.82,18.89,0.23)}
+  {pt3dadd(0.58,240.76,18.89,0.23)}
+  {pt3dadd(1.09,240.66,18.59,0.23)}
+  {pt3dadd(1.7,241.06,18.51,0.23)}
+  {pt3dadd(2.31,241.47,18.06,0.23)}
+  {pt3dadd(2.69,241.81,17.83,0.23)}
+  {pt3dadd(3.08,242.15,17.76,0.23)}
+  {pt3dadd(3.46,242.5,21.32,0.23)}
+  {pt3dadd(5.57,243.55,21.32,0.23)}
+  {pt3dadd(6.4,244.02,21.32,0.23)}
+  {pt3dadd(10.63,245.17,21.32,0.23)}
+  {pt3dadd(11.46,245.63,21.32,0.23)}
+  {pt3dadd(12.29,246.1,21.32,0.23)}
+  {pt3dadd(13.06,246.79,21.32,0.23)}
+  {pt3dadd(13.45,247.13,21.32,0.23)}
+  {pt3dadd(14.06,247.54,21.32,0.23)}
+  {pt3dadd(14.44,247.88,21.32,0.23)}
+  {pt3dadd(14.83,248.22,21.32,0.23)}
+  {pt3dadd(15.49,248.41,21.32,0.23)}
+  {pt3dadd(16.33,248.87,21.32,0.23)}
+  {pt3dadd(17.89,249.3,21.24,0.23)}
+  {pt3dadd(20.4,249.74,21.01,0.23)}
+  {pt3dadd(23.52,250.59,23.13,0.23)}
+  {pt3dadd(24.06,251.22,23.66,0.23)}
+  {pt3dadd(24.45,251.56,23.66,0.23)}
+  {pt3dadd(24.94,252.41,23.74,0.23)}
+  {pt3dadd(25.32,252.76,23.74,0.23)}
+  {pt3dadd(25.64,253.32,23.74,0.23)}
+  {pt3dadd(26.25,253.73,23.74,0.23)}
+  {pt3dadd(26.74,254.58,23.74,0.23)}
+  {pt3dadd(27.35,254.98,23.74,0.23)}
+  {pt3dadd(27.73,255.33,23.82,0.23)}
+  {pt3dadd(27.89,255.61,23.89,0.23)}
+  {pt3dadd(28.28,255.95,23.89,0.23)}
+  {pt3dadd(28.66,256.3,23.89,0.23)}
+  {pt3dadd(29.11,256.42,23.89,0.23)}
+  {pt3dadd(29.27,256.7,23.89,0.23)}
+  {pt3dadd(29.66,257.05,23.89,0.23)}
+  {pt3dadd(30.27,257.45,23.89,0.23)}
+  {pt3dadd(31.1,257.92,23.97,0.23)}
+  {pt3dadd(31.54,258.04,24.04,0.23)}
+  {pt3dadd(33.77,259.61,26.01,0.23)}
+  {pt3dadd(35.77,262.79,28.21,0.23)}
+  {pt3dadd(36.04,263.58,28.21,0.23)}
+  {pt3dadd(35.92,264.02,28.21,0.23)}
+  {pt3dadd(36.24,264.59,28.21,0.23)}
+  {pt3dadd(36.28,265.32,28.21,0.23)}
+  {pt3dadd(36.44,265.6,28.21,0.23)}
+  {pt3dadd(36.77,266.17,28.21,0.23)}
+  {pt3dadd(37.25,267.02,28.21,0.23)}
+  {pt3dadd(37.42,267.3,28.13,0.23)}
+  {pt3dadd(37.74,267.87,28.13,0.23)}
+  {pt3dadd(38.13,268.21,27.07,0.23)}
+  {pt3dadd(38.29,268.49,27.07,0.23)}
+  {pt3dadd(38.55,269.28,27.07,0.23)}
+  {pt3dadd(38.65,269.79,27.6,0.23)}
+  {pt3dadd(39.67,269.59,28.82,0.23)}
+  {pt3dadd(41.19,269.28,28.82,0.23)}
+  {pt3dadd(41.63,269.4,28.97,0.23)}
+  {pt3dadd(42.18,270.03,29.27,0.23)}
+  {pt3dadd(42.44,270.82,29.8,0.23)}
+  {pt3dadd(43.05,271.22,31.09,0.23)}
+  {pt3dadd(43.88,271.69,31.09,0.23)}
+  {pt3dadd(44.27,272.03,31.47,0.23)}
+  {pt3dadd(44.93,272.22,31.85,0.23)}
+  {pt3dadd(45.32,272.56,32,0.23)}
+  {pt3dadd(45.99,272.74,32,0.23)}
+  {pt3dadd(47.04,273.27,32.07,0.23)}
+  {pt3dadd(49.27,273.88,32.22,0.23)}
+  {pt3dadd(50.57,273.51,32.22,0.23)}
+  {pt3dadd(51.8,273.37,32.38,0.23)}
+  {pt3dadd(52.15,272.98,32.45,0.23)}
+  {pt3dadd(53.06,272.28,32.45,0.23)}
+  {pt3dadd(53.62,271.95,32.45,0.23)}
+  {pt3dadd(53.97,271.57,32.45,0.23)}
+  {pt3dadd(54.53,271.24,32.45,0.23)}
+  {pt3dadd(54.94,270.64,32.45,0.23)}
+  {pt3dadd(55.45,270.53,32.45,0.23)}
+  {pt3dadd(55.85,269.93,32.45,0.23)}
+  {pt3dadd(56.19,269.54,32.45,0.23)}
+  {pt3dadd(56.76,269.22,32.45,0.23)}
+  {pt3dadd(58.18,268.41,32.45,0.23)}
+  {pt3dadd(58.52,268.02,32.45,0.23)}
+  {pt3dadd(58.87,267.64,32.45,0.23)}
+  {pt3dadd(59.15,267.48,32.45,0.23)}
+  {pt3dadd(59.72,267.15,32.45,0.23)}
+  {pt3dadd(60,266.99,32.68,0.23)}
+  {pt3dadd(61.42,266.18,33.06,0.23)}
+  {pt3dadd(62.49,265.76,32.6,0.23)}
+  {pt3dadd(63.5,265.55,31.01,0.23)}
+  {pt3dadd(65.02,265.25,31.01,0.23)}
+  {pt3dadd(65.75,265.21,31.01,0.23)}
+  {pt3dadd(66.64,265.45,31.01,0.23)}
+  {pt3dadd(67.37,265.41,31.01,0.23)}
+
+  {dendrite[22] connect dendrite[24](0), 1}
+  {access dendrite[24]}
+  {pt3dclear()}
+  {pt3dadd(-130.19,204.87,23.74,0.23)}
+  {pt3dadd(-128.13,207.82,23.74,0.46)}
+  {pt3dadd(-127.34,210.19,23.74,0.46)}
+  {pt3dadd(-126.75,214.18,23.74,0.46)}
+  {pt3dadd(-126.12,216.26,23.74,0.46)}
+  {pt3dadd(-126.28,218.61,23.74,0.23)}
+  {pt3dadd(-128.32,219.97,23.74,0.23)}
+  {pt3dadd(-127.33,223.35,23.74,0.23)}
+  {pt3dadd(-128.04,225.07,23.74,0.23)}
+  {pt3dadd(-128.64,227.3,23.74,0.23)}
+  {pt3dadd(-128.48,227.58,23.74,0.23)}
+  {pt3dadd(-128.72,228.47,23.74,0.23)}
+  {pt3dadd(-128.62,228.98,23.74,0.23)}
+  {pt3dadd(-128.74,229.42,23.74,0.23)}
+  {pt3dadd(-128.58,229.7,23.74,0.23)}
+  {pt3dadd(-128.7,230.15,23.74,0.23)}
+  {pt3dadd(-128.54,230.43,23.74,0.23)}
+  {pt3dadd(-129.09,232.44,23.74,0.23)}
+  {pt3dadd(-128.86,232.5,23.74,0.23)}
+  {pt3dadd(-129.04,233.17,23.74,0.23)}
+  {pt3dadd(-128.88,233.45,23.74,0.23)}
+  {pt3dadd(-128.78,233.95,23.74,0.23)}
+  {pt3dadd(-128.62,234.24,23.74,0.23)}
+  {pt3dadd(-128.17,234.36,23.74,0.23)}
+  {pt3dadd(-128.01,234.64,23.74,0.23)}
+  {pt3dadd(-127.63,234.99,23.74,0.23)}
+  {pt3dadd(-127.58,235.72,23.74,0.23)}
+  {pt3dadd(-128.49,239.06,23.74,0.23)}
+  {pt3dadd(-128.39,239.56,23.74,0.23)}
+  {pt3dadd(-128.57,240.23,23.74,0.23)}
+  {pt3dadd(-128.53,240.96,23.74,0.23)}
+  {pt3dadd(-128.24,244.37,22.91,0.23)}
+  {pt3dadd(-128.64,247.6,21.39,0.23)}
+  {pt3dadd(-128.48,247.89,21.32,0.23)}
+  {pt3dadd(-128.5,248.84,21.32,0.23)}
+  {pt3dadd(-128.4,249.34,21.32,0.23)}
+  {pt3dadd(-128.52,249.79,21.32,0.23)}
+  {pt3dadd(-128.42,250.3,21.32,0.23)}
+  {pt3dadd(-128.32,250.8,21.32,0.23)}
+  {pt3dadd(-127.99,251.37,21.32,0.23)}
+  {pt3dadd(-127.89,251.87,21.32,0.23)}
+  {pt3dadd(-127.34,252.5,21.32,0.23)}
+  {pt3dadd(-126.79,253.13,21.32,0.23)}
+  {pt3dadd(-126.41,253.47,20.86,0.23)}
+  {pt3dadd(-126.09,254.04,20.86,0.23)}
+  {pt3dadd(-125.7,254.38,20.71,0.23)}
+  {pt3dadd(-125.66,255.11,20.56,0.23)}
+  {pt3dadd(-125.84,255.78,20.56,0.23)}
+  {pt3dadd(-125.8,256.51,20.48,0.23)}
+  {pt3dadd(-126.22,258.07,18.82,0.23)}
+  {pt3dadd(-127.64,258.88,18.82,0.23)}
+  {pt3dadd(-128.82,258.8,18.29,0.23)}
+  {pt3dadd(-129.77,258.78,17.91,0.23)}
+  {pt3dadd(-130.05,258.94,16.92,0.23)}
+  {pt3dadd(-129.95,259.44,16.92,0.23)}
+  {pt3dadd(-130.07,259.89,16.92,0.23)}
+  {pt3dadd(-129.63,260.01,16.47,0.23)}
+  {pt3dadd(-129.52,260.52,16.47,0.23)}
+  {pt3dadd(-129.14,260.86,15.71,0.23)}
+  {pt3dadd(-128.75,261.21,15.71,0.23)}
+  {pt3dadd(-128.27,262.06,15.03,0.23)}
+  {pt3dadd(-128.11,262.34,14.95,0.23)}
+  {pt3dadd(-127.78,262.91,14.95,0.23)}
+  {pt3dadd(-127.68,263.41,14.73,0.23)}
+  {pt3dadd(-127.64,264.14,14.73,0.23)}
+  {pt3dadd(-127.54,264.65,14.2,0.23)}
+  {pt3dadd(-128.33,267.54,11.92,0.23)}
+  {pt3dadd(-128.22,268.05,11.92,0.23)}
+  {pt3dadd(-128.12,268.55,11.47,0.23)}
+  {pt3dadd(-128.02,269.06,11.24,0.23)}
+  {pt3dadd(-127.7,269.63,10.94,0.23)}
+  {pt3dadd(-127.6,270.13,10.18,0.23)}
+  {pt3dadd(-127.49,270.64,10.18,0.23)}
+  {pt3dadd(-127.39,271.14,10.18,0.23)}
+  {pt3dadd(-127.29,271.65,10.18,0.23)}
+  {pt3dadd(-127.41,272.09,8.67,0.23)}
+  {pt3dadd(-127.37,272.82,8.67,0.23)}
+  {pt3dadd(-127.27,273.33,8.36,0.23)}
+  {pt3dadd(-127.82,275.33,8.36,0.23)}
+  {pt3dadd(-127.65,275.62,8.36,0.23)}
+  {pt3dadd(-128.26,277.84,8.36,0.23)}
+  {pt3dadd(-128.16,278.35,8.36,0.23)}
+  {pt3dadd(-128.52,279.69,8.36,0.23)}
+  {pt3dadd(-128.54,280.64,8.36,0.23)}
+  {pt3dadd(-128.5,281.37,8.36,0.23)}
+  {pt3dadd(-128.74,282.26,8.29,0.23)}
+  {pt3dadd(-128.76,283.21,7.98,0.23)}
+  {pt3dadd(-129.01,284.1,6.62,0.23)}
+  {pt3dadd(-129.02,285.05,6.54,0.23)}
+  {pt3dadd(-128.98,285.78,6.54,0.23)}
+  {pt3dadd(-128.66,286.34,6.54,0.23)}
+  {pt3dadd(-128.56,286.85,6.54,0.23)}
+  {pt3dadd(-128.68,287.3,6.47,0.23)}
+  {pt3dadd(-128.35,287.86,6.24,0.23)}
+  {pt3dadd(-128.15,288.87,5.94,0.23)}
+  {pt3dadd(-128.27,289.32,5.41,0.23)}
+  {pt3dadd(-128.17,289.83,5.18,0.23)}
+  {pt3dadd(-128.25,291,5.18,0.23)}
+  {pt3dadd(-128.49,291.89,5.33,0.23)}
+  {pt3dadd(-128.39,292.4,5.64,0.23)}
+  {pt3dadd(-128.57,293.06,5.71,0.23)}
+  {pt3dadd(-128.59,294.02,5.94,0.23)}
+  {pt3dadd(-129.56,297.58,6.09,0.23)}
+  {pt3dadd(-129.46,298.08,6.09,0.23)}
+  {pt3dadd(-130.01,300.09,6.17,0.23)}
+  {pt3dadd(-130.64,300.63,6.17,0.23)}
+  {pt3dadd(-131.43,300.9,6.17,0.23)}
+  {pt3dadd(-131.77,301.28,6.17,0.23)}
+  {pt3dadd(-132.22,301.16,6.17,0.23)}
+  {pt3dadd(-132.95,301.2,6.17,0.23)}
+  {pt3dadd(-133.23,301.36,6.17,0.23)}
+}
+
+proc creat5 () {
+  {dendrite[19] connect dendrite[25](0), 1}
+  {access dendrite[25]}
+  {pt3dclear()}
+  {pt3dadd(-295.23,102.31,42.14,0.69)}
+  {pt3dadd(-296.61,105.33,42.59,0.23)}
+  {pt3dadd(-296.47,105.83,42.59,0.23)}
+  {pt3dadd(-296.83,106.47,42.59,0.23)}
+  {pt3dadd(-297.24,106.16,42.59,0.23)}
+  {pt3dadd(-297.78,106.52,42.59,0.23)}
+  {pt3dadd(-298.05,106.71,42.59,0.23)}
+  {pt3dadd(-298.32,106.89,42.59,0.23)}
+  {pt3dadd(-298.95,110.29,41.23,0.23)}
+  {pt3dadd(-300.99,109.91,45.25,0.23)}
+  {pt3dadd(-301.35,110.55,45.25,0.23)}
+  {pt3dadd(-301.48,111.23,45.25,0.23)}
+  {pt3dadd(-301.79,111.64,45.25,0.23)}
+  {pt3dadd(-302.1,112.05,45.25,0.23)}
+  {pt3dadd(-302.41,112.46,45.78,0.23)}
+  {pt3dadd(-303.18,112.79,46.23,0.23)}
+  {pt3dadd(-303.45,112.98,47.75,0.23)}
+  {pt3dadd(-304.62,112.99,47.75,0.23)}
+  {pt3dadd(-305.39,113.32,47.75,0.23)}
+  {pt3dadd(-306.24,114.1,48.35,0.23)}
+  {pt3dadd(-306.11,113.42,48.35,0.23)}
+  {pt3dadd(-306.26,112.92,48.35,0.23)}
+  {pt3dadd(-306.44,112.65,47.97,0.23)}
+  {pt3dadd(-306.85,112.34,47.9,0.23)}
+  {pt3dadd(-307.04,112.07,47.9,0.23)}
+  {pt3dadd(-307.45,115.39,48.74,0.23)}
+  {pt3dadd(-307.45,115.39,49.04,0.23)}
+  {pt3dadd(-307.99,115.76,49.12,0.23)}
+  {pt3dadd(-308.94,115.81,49.19,0.23)}
+  {pt3dadd(-309.71,116.14,49.42,0.23)}
+  {pt3dadd(-310.02,116.55,49.65,0.23)}
+  {pt3dadd(-310.1,117,50.03,0.23)}
+  {pt3dadd(-310.41,117.42,50.18,0.23)}
+  {pt3dadd(-310.32,118.14,50.25,0.23)}
+  {pt3dadd(-309.27,118.8,50.25,0.23)}
+  {pt3dadd(-308.94,119.57,51.77,0.23)}
+  {pt3dadd(-309.07,120.25,51.77,0.23)}
+  {pt3dadd(-308.92,120.75,52.45,0.23)}
+  {pt3dadd(-309.01,121.2,52.45,0.23)}
+  {pt3dadd(-309.36,121.84,52.75,0.23)}
+  {pt3dadd(-309.67,122.25,53.13,0.23)}
+  {pt3dadd(-309.99,122.66,53.13,0.23)}
+  {pt3dadd(-310.53,123.03,53.43,0.23)}
+  {pt3dadd(-310.79,123.21,54.12,0.23)}
+  {pt3dadd(-311.65,123.99,54.12,0.23)}
+  {pt3dadd(-312,124.63,55.33,0.23)}
+  {pt3dadd(-312.17,125.54,55.78,0.23)}
+  {pt3dadd(-311.76,125.85,56.09,0.23)}
+  {pt3dadd(-311.12,126.2,56.09,0.23)}
+  {pt3dadd(-310.21,126.37,56.16,0.23)}
+  {pt3dadd(-310.03,126.64,56.84,0.23)}
+  {pt3dadd(-309.97,127.59,57.75,0.23)}
+  {pt3dadd(-309.83,128.09,58.21,0.23)}
+  {pt3dadd(-309.37,128.17,59.72,0.23)}
+  {pt3dadd(-309,128.71,60.25,0.23)}
+  {pt3dadd(-308.45,129.52,60.4,0.23)}
+  {pt3dadd(-308.31,130.02,60.4,0.23)}
+  {pt3dadd(-308.61,131.6,60.63,0.23)}
+  {pt3dadd(-309.1,131.75,60.63,0.23)}
+  {pt3dadd(-309.37,131.93,58.21,0.23)}
+  {pt3dadd(-309.68,132.34,59.04,0.23)}
+  {pt3dadd(-308.59,132.78,59.04,0.23)}
+  {pt3dadd(-308.41,133.05,60.1,0.23)}
+  {pt3dadd(-308.35,134,60.1,0.23)}
+  {pt3dadd(-308.52,134.91,61.16,0.23)}
+  {pt3dadd(-309.2,134.78,61.54,0.23)}
+  {pt3dadd(-309.47,134.96,62.07,0.23)}
+  {pt3dadd(-310.01,135.33,62.07,0.23)}
+  {pt3dadd(-310.46,135.25,62.9,0.23)}
+  {pt3dadd(-311,135.62,64.19,0.23)}
+  {pt3dadd(-310.63,136.16,65.1,0.23)}
+  {pt3dadd(-310.53,136.88,65.1,0.23)}
+  {pt3dadd(-310.74,138.01,65.1,0.23)}
+  {pt3dadd(-311.24,138.15,65.55,0.23)}
+  {pt3dadd(-311.74,138.3,65.55,0.23)}
+  {pt3dadd(-312.46,138.4,67.37,0.23)}
+  {pt3dadd(-314.73,137.97,68.21,0.23)}
+  {pt3dadd(-315.27,138.34,69.34,0.23)}
+  {pt3dadd(-315.81,138.71,69.34,0.23)}
+  {pt3dadd(-316.08,138.89,70.25,0.23)}
+  {pt3dadd(-316.62,139.26,70.25,0.23)}
+  {pt3dadd(-317.57,139.32,70.25,0.23)}
+  {pt3dadd(-317.65,139.77,70.86,0.23)}
+
+  {dendrite[18] connect dendrite[26](0), 1}
+  {access dendrite[26]}
+  {pt3dclear()}
+  {pt3dadd(-297.15,89.97,39.72,0.69)}
+  {pt3dadd(-299.1,91.02,42.97,0.23)}
+  {pt3dadd(-299.74,94.42,43.28,0.23)}
+  {pt3dadd(-299.33,94.73,43.28,0.23)}
+  {pt3dadd(-298.96,95.27,43.2,0.23)}
+  {pt3dadd(-298.55,95.58,43.2,0.23)}
+  {pt3dadd(-298.18,96.12,43.2,0.23)}
+  {pt3dadd(-297.77,96.43,43.81,0.23)}
+  {pt3dadd(-297.58,96.7,44.72,0.23)}
+  {pt3dadd(-298.05,99.19,45.47,0.23)}
+  {pt3dadd(-298.49,100.29,45.7,0.23)}
+  {pt3dadd(-298.8,100.7,46.53,0.23)}
+  {pt3dadd(-299.11,101.11,46.69,0.23)}
+  {pt3dadd(-299.47,101.75,46.84,0.23)}
+  {pt3dadd(-299.78,102.16,47.06,0.23)}
+  {pt3dadd(-300.09,102.57,47.14,0.23)}
+  {pt3dadd(-300.26,103.48,47.14,0.23)}
+  {pt3dadd(-300.57,103.89,47.37,0.23)}
+  {pt3dadd(-301.04,106.38,48.73,0.23)}
+  {pt3dadd(-300.9,106.88,48.73,0.23)}
+  {pt3dadd(-301.99,106.31,48.74,0.23)}
+  {pt3dadd(-304.72,105.8,48.74,0.23)}
+  {pt3dadd(-305.75,106.31,48.74,0.23)}
+  {pt3dadd(-306.33,106.91,48.74,0.23)}
+  {pt3dadd(-306.6,107.09,48.74,0.23)}
+  {pt3dadd(-306.94,108.91,48.36,0.23)}
+  {pt3dadd(-307.26,109.32,50.48,0.23)}
+  {pt3dadd(-308.06,109.87,51.01,0.23)}
+  {pt3dadd(-308.6,110.24,51.01,0.23)}
+  {pt3dadd(-308.87,110.43,51.24,0.23)}
+  {pt3dadd(-309.18,110.84,54.27,0.23)}
+  {pt3dadd(-310.22,111.35,54.65,0.23)}
+  {pt3dadd(-311.03,111.9,54.72,0.23)}
+  {pt3dadd(-311.94,111.73,55.86,0.23)}
+  {pt3dadd(-312.35,111.42,55.86,0.23)}
+  {pt3dadd(-313.26,111.25,56.16,0.23)}
+  {pt3dadd(-313.67,110.94,56.62,0.23)}
+  {pt3dadd(-315.03,110.68,56.92,0.23)}
+  {pt3dadd(-316.06,111.19,56.92,0.23)}
+  {pt3dadd(-316.33,111.38,56.92,0.23)}
+  {pt3dadd(-316.65,111.79,57.52,0.23)}
+  {pt3dadd(-317.14,111.93,57.52,0.23)}
+  {pt3dadd(-317.64,112.07,59.34,0.23)}
+  {pt3dadd(-318.82,112.09,59.72,0.23)}
+  {pt3dadd(-319.31,112.23,59.57,0.23)}
+  {pt3dadd(-320.12,112.78,60.25,0.23)}
+  {pt3dadd(-320.62,112.92,60.48,0.23)}
+  {pt3dadd(-321.3,112.8,60.48,0.23)}
+  {pt3dadd(-321.57,112.98,63.81,0.23)}
+  {pt3dadd(-322.38,113.53,64.11,0.23)}
+  {pt3dadd(-322.69,113.95,64.27,0.23)}
+  {pt3dadd(-322.96,114.13,64.42,0.23)}
+  {pt3dadd(-324.4,114.33,64.42,0.23)}
+  {pt3dadd(-325.54,114.12,65.78,0.23)}
+  {pt3dadd(-326.8,114.58,65.78,0.23)}
+  {pt3dadd(-327.61,115.14,65.78,0.23)}
+  {pt3dadd(-328.65,115.65,65.78,0.23)}
+  {pt3dadd(-329.5,116.43,65.78,0.23)}
+  {pt3dadd(-329.85,117.07,65.78,0.23)}
+  {pt3dadd(-330.16,117.48,65.78,0.23)}
+  {pt3dadd(-330.74,118.07,65.78,0.23)}
+  {pt3dadd(-331.06,118.48,65.78,0.23)}
+  {pt3dadd(-331.37,118.89,65.78,0.23)}
+  {pt3dadd(-331.45,119.35,65.78,0.23)}
+  {pt3dadd(-331.76,119.76,65.78,0.23)}
+  {pt3dadd(-332.03,119.94,65.78,0.23)}
+  {pt3dadd(-332.12,120.4,65.78,0.23)}
+  {pt3dadd(-332.43,120.81,65.78,0.23)}
+  {pt3dadd(-332.52,121.26,65.78,0.23)}
+  {pt3dadd(-332.87,121.9,65.78,0.23)}
+  {pt3dadd(-333.18,122.31,65.78,0.23)}
+  {pt3dadd(-333.49,122.72,65.93,0.23)}
+  {pt3dadd(-333.81,123.13,65.93,0.23)}
+  {pt3dadd(-334.8,123.42,66.16,0.23)}
+  {pt3dadd(-335.34,123.79,66.61,0.23)}
+  {pt3dadd(-335.55,124.92,67.83,0.23)}
+  {pt3dadd(-336.67,125.89,67.83,0.23)}
+  {pt3dadd(-337.29,126.71,65.18,0.23)}
+  {pt3dadd(-337.61,127.12,64.8,0.23)}
+  {pt3dadd(-337.92,127.53,64.8,0.23)}
+  {pt3dadd(-338.73,128.08,64.8,0.23)}
+  {pt3dadd(-338.99,128.27,64.8,0.23)}
+  {pt3dadd(-339.31,128.68,64.8,0.23)}
+  {pt3dadd(-339.85,129.05,64.8,0.23)}
+  {pt3dadd(-340.16,129.46,64.8,0.23)}
+  {pt3dadd(-340.92,129.78,64.8,0.23)}
+  {pt3dadd(-341.69,130.11,64.8,0.23)}
+  {pt3dadd(-342.23,130.48,64.8,0.23)}
+  {pt3dadd(-343.04,131.03,64.8,0.23)}
+  {pt3dadd(-343.53,131.17,64.8,0.23)}
+  {pt3dadd(-344.34,131.73,64.8,0.23)}
+  {pt3dadd(-345.11,132.05,64.8,0.23)}
+  {pt3dadd(-345.92,132.61,64.8,0.23)}
+  {pt3dadd(-346.99,133.34,64.8,0.23)}
+  {pt3dadd(-347.89,134.35,64.8,0.23)}
+  {pt3dadd(-348.2,134.76,64.8,0.23)}
+  {pt3dadd(-348.82,135.58,64.8,0.23)}
+  {pt3dadd(-349.4,136.18,64.8,0.23)}
+  {pt3dadd(-349.67,136.36,64.8,0.23)}
+  {pt3dadd(-350.25,136.96,64.8,0.23)}
+  {pt3dadd(-350.75,137.1,64.8,0.23)}
+  {pt3dadd(-351.7,137.16,65.02,0.23)}
+  {pt3dadd(-353.42,137.54,65.02,0.23)}
+  {pt3dadd(-353.96,137.91,66.92,0.23)}
+  {pt3dadd(-354.72,138.24,66.99,0.23)}
+  {pt3dadd(-355.63,138.07,67.14,0.23)}
+  {pt3dadd(-357.57,138.41,67.45,0.23)}
+  {pt3dadd(-358.57,138.69,67.98,0.23)}
+  {pt3dadd(-359.64,139.43,67.98,0.23)}
+  {pt3dadd(-359.91,139.61,67.98,0.23)}
+  {pt3dadd(-360.81,140.62,67.98,0.23)}
+  {pt3dadd(-361.16,141.26,67.98,0.23)}
+  {pt3dadd(-361.47,141.67,67.98,0.23)}
+  {pt3dadd(-364.41,141.16,70.71,0.23)}
+  {pt3dadd(-364.41,141.16,71.01,0.23)}
+  {pt3dadd(-368.99,144.3,73.13,0.23)}
+  {pt3dadd(-369.67,144.17,75.02,0.23)}
+  {pt3dadd(-370.17,144.31,76.01,0.23)}
+  {pt3dadd(-370.51,146.12,76.99,0.23)}
+
+  {dendrite[13] connect dendrite[27](0), 1}
+  {access dendrite[27]}
+  {pt3dclear()}
+  {pt3dadd(-302.62,76.51,39.94,1.39)}
+  {pt3dadd(-308.39,76.6,37.22,0.69)}
+
+  {dendrite[27] connect dendrite[28](0), 1}
+  {access dendrite[28]}
+  {pt3dclear()}
+  {pt3dadd(-308.39,76.6,37.22,0.69)}
+  {pt3dadd(-309.14,74.35,35.02,0.69)}
+  {pt3dadd(-309.33,74.08,35.02,0.69)}
+  {pt3dadd(-309.51,73.81,35.02,0.69)}
+  {pt3dadd(-310.15,73.45,35.02,0.69)}
+  {pt3dadd(-312.19,73.07,33.66,0.69)}
+  {pt3dadd(-312.56,72.53,33.5,0.69)}
+  {pt3dadd(-312.71,72.04,33.28,0.69)}
+  {pt3dadd(-313.26,71.23,32.75,0.69)}
+  {pt3dadd(-313.44,70.96,32.37,0.69)}
+  {pt3dadd(-314,70.15,32.14,0.69)}
+  {pt3dadd(-314.41,69.84,31.99,0.69)}
+  {pt3dadd(-315.77,69.59,31.08,0.69)}
+  {pt3dadd(-315.69,69.13,30.78,0.69)}
+  {pt3dadd(-315.83,68.64,30.55,0.69)}
+  {pt3dadd(-315.7,67.96,30.55,0.69)}
+  {pt3dadd(-315.39,67.54,30.55,0.69)}
+  {pt3dadd(-315.08,67.13,30.55,0.69)}
+  {pt3dadd(-314.91,66.23,30.4,0.69)}
+  {pt3dadd(-315.5,65.64,30.4,0.69)}
+  {pt3dadd(-316.18,65.52,30.4,0.69)}
+  {pt3dadd(-316.6,65.21,30.4,0.69)}
+  {pt3dadd(-317.01,64.89,30.4,0.69)}
+  {pt3dadd(-317.92,64.72,30.4,0.69)}
+  {pt3dadd(-318.33,64.41,29.11,0.69)}
+  {pt3dadd(-318.82,64.55,27.44,0.69)}
+  {pt3dadd(-319.36,64.92,27.75,0.69)}
+  {pt3dadd(-319.45,65.38,27.75,0.69)}
+  {pt3dadd(-319.76,65.79,27.75,0.69)}
+  {pt3dadd(-320.01,67.15,27.75,0.69)}
+  {pt3dadd(-319.74,66.96,27.82,0.69)}
+  {pt3dadd(-319.89,66.47,27.98,0.69)}
+  {pt3dadd(-320.03,65.97,28.35,0.69)}
+  {pt3dadd(-320.21,65.7,28.35,0.69)}
+  {pt3dadd(-320.36,65.21,29.64,0.69)}
+  {pt3dadd(-321.99,65.14,28.73,0.69)}
+  {pt3dadd(-322.3,65.55,29.19,0.69)}
+  {pt3dadd(-322.57,65.73,26.61,0.69)}
+  {pt3dadd(-322.88,66.14,25.93,0.69)}
+  {pt3dadd(-324.47,65.85,24.04,0.69)}
+  {pt3dadd(-325.24,66.17,23.96,0.69)}
+  {pt3dadd(-325.5,66.36,23.13,0.69)}
+  {pt3dadd(-325.8,67.94,22.45,0.69)}
+  {pt3dadd(-326.34,68.31,22.45,0.69)}
+  {pt3dadd(-326.61,68.5,21.46,0.69)}
+  {pt3dadd(-326.95,70.31,21.46,0.69)}
+  {pt3dadd(-326.76,70.58,21.46,0.69)}
+  {pt3dadd(-329.29,71.46,21.47,0.23)}
+  {pt3dadd(-331.12,72.88,21.47,0.23)}
+  {pt3dadd(-330.67,73,21.47,0.23)}
+  {pt3dadd(-330.23,73.12,21.47,0.23)}
+  {pt3dadd(-329.88,72.74,21.47,0.23)}
+  {pt3dadd(-329.76,72.29,21.47,0.23)}
+  {pt3dadd(-330.21,72.17,21.47,0.23)}
+  {pt3dadd(-330.59,71.82,21.47,0.23)}
+  {pt3dadd(-331.26,71.64,22.23,0.23)}
+  {pt3dadd(-331.87,71.24,17.07,0.23)}
+  {pt3dadd(-332.31,71.12,16.32,0.23)}
+  {pt3dadd(-332.88,71.44,16.32,0.23)}
+  {pt3dadd(-333.22,71.82,16.32,0.23)}
+  {pt3dadd(-333.95,71.87,16.09,0.23)}
+  {pt3dadd(-335.03,72.29,15.86,0.23)}
+  {pt3dadd(-336.14,71.99,15.86,0.23)}
+  {pt3dadd(-336.53,71.64,15.71,0.23)}
+  {pt3dadd(-336.89,72.98,15.71,0.23)}
+  {pt3dadd(-337.17,73.14,15.71,0.23)}
+  {pt3dadd(-337.29,73.59,15.71,0.23)}
+  {pt3dadd(-337.64,73.97,15.71,0.23)}
+  {pt3dadd(-337.98,74.36,15.63,0.23)}
+  {pt3dadd(-338.33,74.74,15.63,0.23)}
+  {pt3dadd(-338.83,74.84,13.97,0.23)}
+  {pt3dadd(-341.02,74.96,13.51,0.23)}
+  {pt3dadd(-346.53,75.86,8.89,0.23)}
+  {pt3dadd(-347.07,77.86,8.89,0.23)}
+  {pt3dadd(-347.07,77.86,8.67,0.23)}
+  {pt3dadd(-350.8,79.24,5.86,0.23)}
+  {pt3dadd(-353.96,80.29,3.67,0.23)}
+  {pt3dadd(-353.96,80.29,2.76,0.23)}
+  {pt3dadd(-354.77,78.87,-0.65,0.23)}
+  {pt3dadd(-355.86,80.25,-0.88,0.23)}
+  {pt3dadd(-357.38,80.55,-1.48,0.23)}
+  {pt3dadd(-356.67,78.83,-3.15,0.23)}
+  {pt3dadd(-356.67,78.83,-3.45,0.23)}
+  {pt3dadd(-355.7,77.9,-3.68,0.23)}
+  {pt3dadd(-355.7,77.9,-3.83,0.23)}
+}
+
+proc creat6 () {
+  {dendrite[27] connect dendrite[29](0), 1}
+  {access dendrite[29]}
+  {pt3dclear()}
+  {pt3dadd(-308.39,76.6,37.22,0.69)}
+  {pt3dadd(-316.25,78.42,37.22,0.69)}
+
+  {dendrite[29] connect dendrite[30](0), 1}
+  {access dendrite[30]}
+  {pt3dclear()}
+  {pt3dadd(-316.25,78.42,37.22,0.69)}
+  {pt3dadd(-324.22,78.3,36.53,0.46)}
+  {pt3dadd(-324.67,78.22,36.53,0.46)}
+  {pt3dadd(-325.08,77.91,36.53,0.46)}
+  {pt3dadd(-325.27,77.64,36.53,0.46)}
+  {pt3dadd(-328.03,77.36,37.75,0.46)}
+  {pt3dadd(-330.97,77.98,37.06,0.46)}
+  {pt3dadd(-336.99,74.98,38.66,0.46)}
+  {pt3dadd(-343.91,75.56,40.32,0.46)}
+  {pt3dadd(-349.49,75.22,43.81,0.46)}
+  {pt3dadd(-349.49,75.22,43.88,0.46)}
+  {pt3dadd(-356.21,77.25,44.64,0.46)}
+  {pt3dadd(-358.75,77.01,44.72,0.46)}
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+  {pt3dadd(-257.53,-25.48,67.75,0.46)}
+  {pt3dadd(-257.63,-26.2,67.98,0.46)}
+  {pt3dadd(-257.51,-26.88,68.36,0.46)}
+  {pt3dadd(-257.61,-27.6,68.58,0.46)}
+  {pt3dadd(-257.39,-28.74,69.27,0.46)}
+  {pt3dadd(-257.54,-29.24,69.42,0.46)}
+  {pt3dadd(-257.45,-29.69,69.42,0.46)}
+  {pt3dadd(-257.59,-30.19,69.42,0.46)}
+  {pt3dadd(-257.51,-30.64,69.42,0.46)}
+  {pt3dadd(-257.65,-31.13,69.42,0.46)}
+  {pt3dadd(-257.27,-33.18,69.42,0.46)}
+  {pt3dadd(-257,-33.36,69.42,0.46)}
+  {pt3dadd(-256.69,-33.77,69.8,0.46)}
+  {pt3dadd(-256.38,-34.18,70.02,0.46)}
+  {pt3dadd(-256.11,-34.37,70.02,0.46)}
+  {pt3dadd(-256.29,-34.64,70.02,0.46)}
+  {pt3dadd(-258.17,-39.7,70.25,0.46)}
+  {pt3dadd(-258.54,-40.24,70.25,0.46)}
+  {pt3dadd(-259.05,-41.27,70.25,0.46)}
+  {pt3dadd(-259.19,-41.77,70.25,0.46)}
+  {pt3dadd(-258.43,-45.85,70.25,0.46)}
+  {pt3dadd(-258.57,-46.35,70.25,0.46)}
+  {pt3dadd(-259.35,-47.2,70.25,0.46)}
+  {pt3dadd(-259.72,-47.74,70.25,0.46)}
+  {pt3dadd(-259.9,-48.01,70.25,0.46)}
+  {pt3dadd(-259.69,-49.14,70.25,0.46)}
+  {pt3dadd(-259.15,-49.51,70.25,0.46)}
+  {pt3dadd(-258.88,-49.69,70.25,0.46)}
+  {pt3dadd(-258.38,-49.84,70.25,0.46)}
+  {pt3dadd(-258.3,-50.29,70.25,0.46)}
+  {pt3dadd(-259.12,-50.91,70.25,0.46)}
+  {pt3dadd(-259.27,-51.41,70.25,0.46)}
+  {pt3dadd(-260.17,-51.58,70.33,0.46)}
+  {pt3dadd(-260.32,-52.07,70.33,0.46)}
+  {pt3dadd(-260.81,-51.93,70.33,0.46)}
+  {pt3dadd(-261.22,-52.24,70.33,0.46)}
+  {pt3dadd(-261.01,-53.38,70.33,0.46)}
+  {pt3dadd(-261.75,-54.46,73.13,0.46)}
+  {pt3dadd(-262.16,-54.77,73.13,0.46)}
+  {pt3dadd(-262.35,-55.04,73.13,0.46)}
+  {pt3dadd(-262.49,-55.53,73.13,0.46)}
+  {pt3dadd(-262.63,-56.03,73.13,0.46)}
+  {pt3dadd(-263.04,-56.34,73.13,0.46)}
+  {pt3dadd(-263.23,-56.61,73.13,0.46)}
+}
+
+proc creat7 () {
+  {dendrite[35] connect dendrite[37](0), 1}
+  {access dendrite[37]}
+  {pt3dclear()}
+  {pt3dadd(-257.08,30.95,54.57,0.46)}
+  {pt3dadd(-255.53,23.96,56.84,0.46)}
+  {pt3dadd(-255.53,23.96,56.99,0.46)}
+  {pt3dadd(-255.44,20.93,60.71,0.46)}
+  {pt3dadd(-255.44,20.93,60.78,0.46)}
+  {pt3dadd(-256.36,19.58,63.58,0.46)}
+  {pt3dadd(-256.02,17.77,63.58,0.46)}
+  {pt3dadd(-255.52,17.63,63.58,0.46)}
+  {pt3dadd(-254.16,17.88,63.58,0.46)}
+  {pt3dadd(-253.66,17.74,63.58,0.46)}
+  {pt3dadd(-253.81,17.24,63.58,0.46)}
+  {pt3dadd(-253.99,16.97,63.58,0.46)}
+  {pt3dadd(-254.13,16.48,67.45,0.46)}
+  {pt3dadd(-254.54,16.17,67.45,0.46)}
+  {pt3dadd(-254.91,15.63,67.45,0.46)}
+  {pt3dadd(-255.32,15.31,67.45,0.46)}
+  {pt3dadd(-255.51,15.05,67.45,0.46)}
+  {pt3dadd(-255.69,14.78,67.37,0.46)}
+  {pt3dadd(-255.84,14.28,67.37,0.46)}
+  {pt3dadd(-256.02,14.01,67.37,0.46)}
+  {pt3dadd(-256.57,13.2,68.74,0.46)}
+  {pt3dadd(-256.76,12.93,68.74,0.46)}
+  {pt3dadd(-257.17,12.62,69.19,0.46)}
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+  {pt3dadd(-257.54,12.08,70.02,0.46)}
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+  {pt3dadd(-259.91,10.94,71.16,0.46)}
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+  {pt3dadd(-262.07,2.55,75.63,0.46)}
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+  {pt3dadd(-262.67,1.97,76.54,0.46)}
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+  {pt3dadd(-263.53,1.57,76.84,0.46)}
+  {pt3dadd(-264.17,1.22,76.84,0.46)}
+  {pt3dadd(-264.58,0.91,76.84,0.46)}
+  {pt3dadd(-265.18,0.32,76.84,0.46)}
+  {pt3dadd(-265.78,-0.26,76.84,0.46)}
+  {pt3dadd(-266.15,-0.8,76.99,0.46)}
+  {pt3dadd(-266.33,-1.07,76.99,0.46)}
+  {pt3dadd(-266.52,-1.33,77.14,0.46)}
+  {pt3dadd(-266.7,-1.6,77.45,0.46)}
+  {pt3dadd(-267.11,-1.92,77.67,0.46)}
+  {pt3dadd(-267.3,-2.18,78.66,0.46)}
+  {pt3dadd(-267.48,-2.45,79.34,0.46)}
+  {pt3dadd(-267.17,-2.87,81.23,0.46)}
+  {pt3dadd(-267.13,-3.09,81.23,0.46)}
+  {pt3dadd(-267.69,-5.08,81.23,0.46)}
+  {pt3dadd(-269.82,-5.01,81.23,0.46)}
+  {pt3dadd(-270.85,-7.07,81.23,0.46)}
+  {pt3dadd(-270.54,-11.24,81.23,0.46)}
+
+  {dendrite[34] connect dendrite[38](0), 1}
+  {access dendrite[38]}
+  {pt3dclear()}
+  {pt3dadd(-257.88,47.03,49.5,0.92)}
+  {pt3dadd(-255.98,46.92,50.48,0.75)}
+  {pt3dadd(-254.71,46.45,50.48,0.75)}
+  {pt3dadd(-253.72,46.17,50.48,0.75)}
+  {pt3dadd(-251.32,45.91,50.48,0.75)}
+  {pt3dadd(-250.06,45.44,50.48,0.75)}
+  {pt3dadd(-249.56,45.3,50.48,0.75)}
+  {pt3dadd(-248.3,44.83,50.48,0.75)}
+  {pt3dadd(-248.03,44.65,50.48,0.75)}
+
+  {dendrite[38] connect dendrite[39](0), 1}
+  {access dendrite[39]}
+  {pt3dclear()}
+  {pt3dadd(-248.03,44.65,50.48,0.69)}
+  {pt3dadd(-246.42,43.54,51.46,0.80)}
+  {pt3dadd(-246.33,43.09,51.46,0.85)}
+
+  {dendrite[39] connect dendrite[40](0), 1}
+  {access dendrite[40]}
+  {pt3dclear()}
+  {pt3dadd(-246.33,43.09,51.46,0.92)}
+  {pt3dadd(-244.28,40.89,53.13,0.69)}
+  {pt3dadd(-241.65,40.68,54.42,0.46)}
+
+  {dendrite[40] connect dendrite[41](0), 1}
+  {access dendrite[41]}
+  {pt3dclear()}
+  {pt3dadd(-241.65,40.68,54.42,0.46)}
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+  {pt3dadd(-228.47,44.32,52.98,0.69)}
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+  {pt3dadd(-220.88,42.68,53.21,0.69)}
+  {pt3dadd(-220.47,43,53.21,0.69)}
+  {pt3dadd(-220.1,43.53,53.21,0.69)}
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+  {pt3dadd(-217.42,44.27,53.28,0.69)}
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+  {pt3dadd(-105.8,60.65,66.31,0.23)}
+  {pt3dadd(-105.94,60.16,66.31,0.23)}
+  {pt3dadd(-105.86,59.7,66.31,0.23)}
+  {pt3dadd(-105.28,59.11,66.31,0.23)}
+  {pt3dadd(-104.14,59.32,66.31,0.23)}
+  {pt3dadd(-103.73,59.63,66.31,0.23)}
+  {pt3dadd(-103.36,60.17,66.31,0.23)}
+  {pt3dadd(-103.18,60.44,66.31,0.23)}
+  {pt3dadd(-103.43,61.8,66.31,0.23)}
+  {pt3dadd(-103.43,61.8,66.84,0.23)}
+  {pt3dadd(-103.43,61.8,66.92,0.23)}
+  {pt3dadd(-103.74,62.21,68.66,0.23)}
+  {pt3dadd(-103.6,62.71,68.66,0.23)}
+  {pt3dadd(-103.42,62.98,69.04,0.23)}
+  {pt3dadd(-102.78,63.33,69.57,0.23)}
+  {pt3dadd(-101.87,63.5,69.95,0.23)}
+  {pt3dadd(-101.15,63.4,70.1,0.23)}
+  {pt3dadd(-100.15,63.12,70.86,0.23)}
+  {pt3dadd(-99.66,62.98,72.07,0.23)}
+  {pt3dadd(-99.39,62.79,72.07,0.23)}
+  {pt3dadd(-99.12,62.61,72.07,0.23)}
+  {pt3dadd(-98.85,62.42,72.37,0.23)}
+  {pt3dadd(-96.8,62.81,72.98,0.23)}
+  {pt3dadd(-96.12,62.93,73.36,0.23)}
+  {pt3dadd(-95.17,62.88,73.36,0.23)}
+  {pt3dadd(-94.86,62.47,74.04,0.23)}
+  {pt3dadd(-94.32,62.1,74.04,0.23)}
+  {pt3dadd(-94.05,61.91,75.1,0.23)}
+  {pt3dadd(-93.56,61.77,75.48,0.23)}
+  {pt3dadd(-92.88,61.9,76.01,0.23)}
+  {pt3dadd(-92.38,61.76,76.16,0.23)}
+  {pt3dadd(-92.11,61.57,76.23,0.23)}
+  {pt3dadd(-91.84,61.39,76.23,0.23)}
+  {pt3dadd(-91.53,60.98,76.23,0.23)}
+  {pt3dadd(-91.22,60.56,76.23,0.23)}
+  {pt3dadd(-90.9,60.15,76.23,0.23)}
+  {pt3dadd(-90.59,59.74,76.39,0.23)}
+  {pt3dadd(-90.14,59.83,76.39,0.23)}
+  {pt3dadd(-90.27,60.51,76.39,0.23)}
+  {pt3dadd(-90.08,60.78,76.39,0.23)}
+  {pt3dadd(-90.29,61.91,76.46,0.23)}
+  {pt3dadd(-90.56,62.1,76.54,0.23)}
+  {pt3dadd(-90.87,62.51,76.61,0.23)}
+  {pt3dadd(-91.14,62.69,76.61,0.23)}
+  {pt3dadd(-91.64,62.83,76.61,0.23)}
+  {pt3dadd(-92.14,62.97,76.92,0.23)}
+  {pt3dadd(-92.59,62.89,77.22,0.23)}
+}
+
+proc creat8 () {
+  {dendrite[40] connect dendrite[42](0), 1}
+  {access dendrite[42]}
+  {pt3dclear()}
+  {pt3dadd(-241.65,40.68,54.42,0.46)}
+  {pt3dadd(-238.69,38.65,57.37,0.23)}
+  {pt3dadd(-238.69,38.65,55.02,0.23)}
+  {pt3dadd(-238.77,35.35,59.8,0.23)}
+  {pt3dadd(-236.53,33.42,60.78,0.23)}
+  {pt3dadd(-237.06,31.21,64.27,0.23)}
+  {pt3dadd(-233.39,27.9,66.31,0.23)}
+  {pt3dadd(-235.18,26.16,68.28,0.23)}
+  {pt3dadd(-235.01,25.25,68.28,0.23)}
+  {pt3dadd(-233.91,25.69,71.31,0.23)}
+  {pt3dadd(-233.42,25.55,71.31,0.23)}
+  {pt3dadd(-232.92,25.41,71.31,0.23)}
+  {pt3dadd(-232.61,25,71.31,0.23)}
+  {pt3dadd(-232.3,24.58,71.39,0.23)}
+  {pt3dadd(-232.03,24.4,74.64,0.23)}
+  {pt3dadd(-231.53,24.26,74.72,0.23)}
+  {pt3dadd(-231.03,24.12,74.87,0.23)}
+  {pt3dadd(-230.58,24.2,74.95,0.23)}
+  {pt3dadd(-230.08,24.06,75.48,0.23)}
+  {pt3dadd(-229.59,23.92,75.7,0.23)}
+  {pt3dadd(-229.32,23.73,75.7,0.23)}
+  {pt3dadd(-228.86,23.82,76.39,0.23)}
+  {pt3dadd(-228.59,23.63,76.39,0.23)}
+  {pt3dadd(-228.32,23.45,76.69,0.23)}
+  {pt3dadd(-227.83,23.31,76.77,0.23)}
+  {pt3dadd(-227.15,23.44,76.92,0.23)}
+  {pt3dadd(-225.93,23.19,77.3,0.23)}
+  {pt3dadd(-225.25,23.32,77.3,0.23)}
+  {pt3dadd(-225.06,23.59,77.3,0.23)}
+  {pt3dadd(-223.7,23.85,77.3,0.23)}
+  {pt3dadd(-223.2,23.7,77.3,0.23)}
+  {pt3dadd(-222.35,22.92,77.3,0.23)}
+  {pt3dadd(-221.86,22.78,77.83,0.23)}
+  {pt3dadd(-220.91,22.72,78.58,0.23)}
+  {pt3dadd(-217.95,23.28,82.07,0.23)}
+  {pt3dadd(-217.46,23.13,82.14,0.23)}
+  {pt3dadd(-215.19,23.56,83.66,0.23)}
+  {pt3dadd(-214.55,23.91,83.66,0.23)}
+  {pt3dadd(-213.91,24.27,83.73,0.23)}
+  {pt3dadd(-213.5,24.58,83.43,0.23)}
+  {pt3dadd(-213.09,24.89,83.43,0.23)}
+  {pt3dadd(-212.9,25.16,82.83,0.23)}
+  {pt3dadd(-212.45,25.25,82.83,0.23)}
+  {pt3dadd(-212.04,25.56,82.14,0.23)}
+  {pt3dadd(-211.63,25.87,81.76,0.23)}
+  {pt3dadd(-211.21,26.18,81.76,0.23)}
+  {pt3dadd(-210.58,26.54,81.76,0.23)}
+  {pt3dadd(-209.44,26.75,81.76,0.23)}
+  {pt3dadd(-209.03,27.06,81.76,0.23)}
+  {pt3dadd(-206.53,27.53,82.6,0.23)}
+  {pt3dadd(-206.35,27.8,82.75,0.23)}
+  {pt3dadd(-205.94,28.11,83.36,0.23)}
+  {pt3dadd(-205.75,28.38,83.36,0.23)}
+  {pt3dadd(-205.61,28.87,83.36,0.23)}
+  {pt3dadd(-205.2,29.18,83.36,0.23)}
+  {pt3dadd(-204.83,29.72,84.26,0.23)}
+  {pt3dadd(-203.24,30.02,84.04,0.23)}
+
+  {dendrite[39] connect dendrite[43](0), 1}
+  {access dendrite[43]}
+  {pt3dclear()}
+  {pt3dadd(-246.33,43.09,51.46,0.92)}
+  {pt3dadd(-245.38,39.27,57.52,0.46)}
+  {pt3dadd(-245.16,39.32,57.52,0.46)}
+  {pt3dadd(-244.89,39.13,57.52,0.23)}
+  {pt3dadd(-243.89,38.85,57.52,0.23)}
+  {pt3dadd(-243.35,38.48,57.52,0.23)}
+  {pt3dadd(-242.59,38.15,57.52,0.23)}
+  {pt3dadd(-242.28,37.74,57.52,0.23)}
+  {pt3dadd(-241.74,37.37,57.3,0.23)}
+  {pt3dadd(-241.16,36.78,60.25,0.23)}
+  {pt3dadd(-240.89,36.59,60.25,0.23)}
+  {pt3dadd(-240.58,36.18,60.25,0.23)}
+  {pt3dadd(-240.49,35.73,60.25,0.23)}
+  {pt3dadd(-240.22,35.54,60.25,0.23)}
+  {pt3dadd(-239.95,35.36,60.18,0.23)}
+  {pt3dadd(-239.87,34.91,58.66,0.23)}
+  {pt3dadd(-240.83,33.79,62.75,0.23)}
+  {pt3dadd(-240.56,33.6,62.75,0.23)}
+  {pt3dadd(-240.48,33.15,62.75,0.23)}
+  {pt3dadd(-240.17,32.74,62.75,0.23)}
+  {pt3dadd(-240.04,32.06,63.21,0.23)}
+  {pt3dadd(-239.77,31.87,63.58,0.23)}
+  {pt3dadd(-239.13,28.47,66.24,0.23)}
+  {pt3dadd(-239.32,28.2,66.24,0.23)}
+  {pt3dadd(-238.68,24.8,68.58,0.23)}
+  {pt3dadd(-238.86,24.53,68.58,0.23)}
+  {pt3dadd(-239.05,24.26,69.11,0.23)}
+  {pt3dadd(-238.96,23.81,69.64,0.23)}
+  {pt3dadd(-239.92,23.86,74.57,0.23)}
+  {pt3dadd(-240.54,24.69,74.57,0.23)}
+  {pt3dadd(-240.85,25.1,74.57,0.23)}
+  {pt3dadd(-241.15,26.69,74.57,0.23)}
+  {pt3dadd(-241.96,27.24,74.57,0.23)}
+  {pt3dadd(-242.23,27.42,74.57,0.23)}
+  {pt3dadd(-242.72,27.56,74.57,0.23)}
+  {pt3dadd(-243.26,27.93,74.57,0.23)}
+  {pt3dadd(-246.21,27.38,74.57,0.23)}
+  {pt3dadd(-247.3,26.94,74.57,0.23)}
+  {pt3dadd(-247.57,27.13,74.57,0.23)}
+  {pt3dadd(-247.74,28.03,74.57,0.23)}
+
+  {dendrite[38] connect dendrite[44](0), 1}
+  {access dendrite[44]}
+  {pt3dclear()}
+  {pt3dadd(-248.03,44.65,50.48,0.69)}
+  {pt3dadd(-246.81,48.16,53.51,0.46)}
+  {pt3dadd(-246.58,48.21,53.51,0.46)}
+  {pt3dadd(-245.28,51.27,53.51,0.46)}
+  {pt3dadd(-243.96,51.75,53.51,0.46)}
+  {pt3dadd(-241.76,53.8,56.01,0.46)}
+  {pt3dadd(-238.2,56.11,56.01,0.46)}
+  {pt3dadd(-238.05,56.61,56.01,0.46)}
+  {pt3dadd(-237.91,57.11,56.01,0.46)}
+  {pt3dadd(-237.73,57.38,56.01,0.46)}
+  {pt3dadd(-237.9,58.28,56.69,0.46)}
+  {pt3dadd(-237.75,58.78,57.37,0.46)}
+  {pt3dadd(-237.61,59.28,57.68,0.46)}
+  {pt3dadd(-237.02,59.86,57.68,0.46)}
+  {pt3dadd(-236.83,60.13,57.98,0.46)}
+  {pt3dadd(-236.69,60.62,57.98,0.46)}
+  {pt3dadd(-236.55,61.12,60.18,0.46)}
+  {pt3dadd(-236.4,61.61,60.18,0.46)}
+  {pt3dadd(-236.26,62.11,60.18,0.46)}
+  {pt3dadd(-235.85,62.42,60.18,0.46)}
+  {pt3dadd(-235.67,62.69,60.25,0.46)}
+  {pt3dadd(-235.25,63,62.15,0.46)}
+  {pt3dadd(-234.89,63.54,62.75,0.46)}
+  {pt3dadd(-234.74,64.04,62.98,0.46)}
+  {pt3dadd(-234.33,64.35,62.98,0.46)}
+  {pt3dadd(-234.19,64.85,62.98,0.46)}
+  {pt3dadd(-234.05,65.34,62.98,0.46)}
+  {pt3dadd(-233.64,65.65,62.98,0.46)}
+  {pt3dadd(-233.49,66.15,63.81,0.46)}
+  {pt3dadd(-233.12,66.69,64.19,0.46)}
+  {pt3dadd(-231.19,68.93,64.19,0.46)}
+  {pt3dadd(-230.51,69.05,64.19,0.46)}
+  {pt3dadd(-230.33,69.32,64.19,0.46)}
+  {pt3dadd(-230.14,69.59,64.19,0.23)}
+  {pt3dadd(-227.73,70.51,64.19,0.23)}
+  {pt3dadd(-226.68,71.18,65.25,0.23)}
+  {pt3dadd(-226.54,71.68,65.33,0.23)}
+  {pt3dadd(-226.35,71.95,65.78,0.23)}
+  {pt3dadd(-225.53,72.57,66.99,0.23)}
+  {pt3dadd(-225.35,72.84,67.3,0.23)}
+  {pt3dadd(-225.21,73.33,67.83,0.23)}
+  {pt3dadd(-225.02,73.6,68.81,0.23)}
+  {pt3dadd(-224.28,74.68,69.11,0.23)}
+  {pt3dadd(-224.1,74.95,69.27,0.23)}
+  {pt3dadd(-223.54,75.76,69.34,0.23)}
+  {pt3dadd(-223.13,76.07,69.34,0.23)}
+  {pt3dadd(-222.72,76.38,69.34,0.23)}
+  {pt3dadd(-222.08,76.74,70.78,0.23)}
+  {pt3dadd(-221.03,77.4,70.86,0.23)}
+  {pt3dadd(-220.89,77.9,70.86,0.23)}
+  {pt3dadd(-220.71,78.17,70.86,0.23)}
+  {pt3dadd(-220.56,78.66,70.86,0.23)}
+  {pt3dadd(-220.65,79.12,70.86,0.23)}
+  {pt3dadd(-220.51,79.61,70.86,0.23)}
+  {pt3dadd(-219.6,79.78,70.86,0.23)}
+  {pt3dadd(-218.87,79.68,71.16,0.23)}
+  {pt3dadd(-218.38,79.54,72.07,0.23)}
+  {pt3dadd(-216.79,79.84,72.75,0.23)}
+  {pt3dadd(-215.53,79.37,73.2,0.23)}
+  {pt3dadd(-215.07,79.46,73.2,0.23)}
+  {pt3dadd(-214.7,79.99,73.2,0.23)}
+  {pt3dadd(-214.52,80.26,73.28,0.23)}
+  {pt3dadd(-214.11,80.58,73.36,0.23)}
+  {pt3dadd(-213.74,81.11,73.51,0.23)}
+  {pt3dadd(-213.6,81.61,73.58,0.23)}
+  {pt3dadd(-213.45,82.11,73.66,0.23)}
+  {pt3dadd(-213.31,82.6,73.81,0.23)}
+  {pt3dadd(-213.13,82.87,77.45,0.23)}
+  {pt3dadd(-212.98,83.37,73.66,0.23)}
+  {pt3dadd(-213.07,83.82,73.66,0.23)}
+  {pt3dadd(-212.89,84.09,73.66,0.23)}
+  {pt3dadd(-212.97,84.54,73.66,0.23)}
+  {pt3dadd(-212.83,85.04,73.66,0.23)}
+  {pt3dadd(-212.42,85.35,75.17,0.23)}
+  {pt3dadd(-210.37,85.73,77.6,0.23)}
+  {pt3dadd(-210.23,86.23,77.9,0.23)}
+  {pt3dadd(-210.05,86.5,77.9,0.23)}
+  {pt3dadd(-209.72,87.27,77.98,0.23)}
+  {pt3dadd(-209.54,87.54,77.98,0.23)}
+  {pt3dadd(-209.71,88.44,77.98,0.23)}
+  {pt3dadd(-209.97,88.63,78.43,0.23)}
+  {pt3dadd(-211.11,88.41,78.43,0.23)}
+
+  {dendrite[33] connect dendrite[45](0), 1}
+  {access dendrite[45]}
+  {pt3dclear()}
+  {pt3dadd(-275.26,55.1,44.94,2.31)}
+  {pt3dadd(-271.61,56.96,44.64,1.39)}
+  {pt3dadd(-266.37,59.11,44.49,1.39)}
+  {pt3dadd(-263.49,61.3,44.49,1.15)}
+  {pt3dadd(-256.99,62.98,44.49,1.15)}
+  {pt3dadd(-250.27,64.71,44.72,1.15)}
+
+  {dendrite[45] connect dendrite[46](0), 1}
+  {access dendrite[46]}
+  {pt3dclear()}
+  {pt3dadd(-250.27,64.71,44.72,1.15)}
+  {pt3dadd(-245.49,66.78,43.2,1.15)}
+  {pt3dadd(-241.3,68.27,43.2,0.92)}
+  {pt3dadd(-235.33,71.5,43.2,0.92)}
+  {pt3dadd(-235.55,71.45,43.2,0.92)}
+  {pt3dadd(-229.14,73.59,43.13,0.92)}
+  {pt3dadd(-222.6,75.05,43.13,0.92)}
+  {pt3dadd(-214.74,76.99,43.13,0.92)}
+  {pt3dadd(-214.92,76.72,43.13,0.92)}
+
+  {dendrite[46] connect dendrite[47](0), 1}
+  {access dendrite[47]}
+  {pt3dclear()}
+  {pt3dadd(-214.92,76.72,43.13,0.92)}
+  {pt3dadd(-210.19,71.5,44.19,0.69)}
+  {pt3dadd(-209.31,65.56,44.19,0.69)}
+  {pt3dadd(-205.3,64.2,45.32,0.69)}
+  {pt3dadd(-202.25,57.96,49.03,0.69)}
+  {pt3dadd(-206.06,50.09,44.5,0.46)}
+  {pt3dadd(-205.85,48.95,44.5,0.46)}
+  {pt3dadd(-204.17,48.8,44.5,0.23)}
+  {pt3dadd(-204.17,48.8,44.5,0.23)}
+  {pt3dadd(-203.72,48.88,44.5,0.23)}
+  {pt3dadd(-203.45,48.7,44.5,0.23)}
+  {pt3dadd(-203.41,48.47,44.5,0.23)}
+  {pt3dadd(-201.96,48.27,44.5,0.23)}
+  {pt3dadd(-201.75,47.14,44.5,0.23)}
+  {pt3dadd(-201.93,46.87,44.5,0.23)}
+  {pt3dadd(-201.59,45.05,44.5,0.23)}
+  {pt3dadd(-200.52,44.32,44.5,0.23)}
+  {pt3dadd(-200.29,44.36,44.5,0.23)}
+  {pt3dadd(-199.79,44.22,44.5,0.23)}
+  {pt3dadd(-199.75,43.99,44.5,0.23)}
+  {pt3dadd(-199.25,43.85,44.5,0.23)}
+  {pt3dadd(-199.13,43.17,44.5,0.23)}
+  {pt3dadd(-199.35,43.13,44.5,0.23)}
+  {pt3dadd(-199.31,42.9,44.5,0.23)}
+  {pt3dadd(-199.5,42.63,44.5,0.23)}
+  {pt3dadd(-199.45,42.4,44.5,0.23)}
+  {pt3dadd(-199.6,41.91,44.5,0.23)}
+  {pt3dadd(-199.51,41.45,44.5,0.23)}
+  {pt3dadd(-199.69,41.18,44.5,0.23)}
+  {pt3dadd(-199.36,39.37,44.5,0.23)}
+  {pt3dadd(-199.09,39.18,44.5,0.23)}
+  {pt3dadd(-198.77,38.77,44.5,0.23)}
+  {pt3dadd(-198.28,38.63,44.5,0.23)}
+  {pt3dadd(-198.24,38.4,44.5,0.23)}
+  {pt3dadd(-197.74,38.26,44.5,0.23)}
+  {pt3dadd(-197.47,38.08,44.5,0.23)}
+  {pt3dadd(-197.24,38.12,44.5,0.23)}
+  {pt3dadd(-196.44,33.81,44.5,0.23)}
+  {pt3dadd(-195.85,33.22,44.5,0.23)}
+  {pt3dadd(-195.77,32.76,44.5,0.23)}
+  {pt3dadd(-195.5,32.58,44.5,0.23)}
+  {pt3dadd(-195.19,32.17,44.5,0.23)}
+  {pt3dadd(-195.15,31.94,44.5,0.23)}
+  {pt3dadd(-194.83,31.53,44.5,0.23)}
+  {pt3dadd(-194.56,31.34,44.5,0.23)}
+  {pt3dadd(-194.52,31.12,44.5,0.23)}
+  {pt3dadd(-194.25,30.93,44.5,0.23)}
+  {pt3dadd(-194.21,30.71,44.5,0.23)}
+  {pt3dadd(-193.9,30.29,44.5,0.23)}
+  {pt3dadd(-193.67,30.34,44.5,0.23)}
+  {pt3dadd(-193.63,30.11,44.5,0.23)}
+  {pt3dadd(-193.36,29.93,44.5,0.23)}
+  {pt3dadd(-193.32,29.7,44.5,0.23)}
+  {pt3dadd(-193.05,29.51,44.5,0.23)}
+  {pt3dadd(-192.74,29.1,44.5,0.23)}
+  {pt3dadd(-192.23,26.38,44.5,0.23)}
+  {pt3dadd(-191.69,26.01,44.5,0.23)}
+  {pt3dadd(-191.65,25.79,44.5,0.23)}
+  {pt3dadd(-191.38,25.6,44.5,0.23)}
+  {pt3dadd(-191.33,25.37,44.5,0.23)}
+  {pt3dadd(-191.06,25.19,44.5,0.23)}
+  {pt3dadd(-191.02,24.96,44.5,0.23)}
+  {pt3dadd(-190.75,24.78,44.5,0.23)}
+  {pt3dadd(-190.44,24.37,44.5,0.23)}
+  {pt3dadd(-190.36,23.91,44.5,0.23)}
+  {pt3dadd(-190.13,23.96,44.5,0.23)}
+  {pt3dadd(-190.04,23.5,44.5,0.23)}
+  {pt3dadd(-189.77,23.32,44.5,0.23)}
+  {pt3dadd(-189.48,21.73,44.5,0.23)}
+  {pt3dadd(-189.66,21.46,44.5,0.23)}
+  {pt3dadd(-189.89,21.42,44.5,0.23)}
+  {pt3dadd(-189.8,20.97,44.5,0.23)}
+  {pt3dadd(-190.03,20.92,44.5,0.23)}
+  {pt3dadd(-189.82,19.79,44.5,0.23)}
+  {pt3dadd(-189.55,19.6,44.5,0.23)}
+  {pt3dadd(-189.51,19.38,44.5,0.23)}
+  {pt3dadd(-189.24,19.19,44.5,0.23)}
+  {pt3dadd(-189.19,18.97,44.5,0.23)}
+  {pt3dadd(-188.97,19.01,44.5,0.23)}
+  {pt3dadd(-188.63,17.19,44.5,0.23)}
+  {pt3dadd(-188.81,16.92,44.5,0.23)}
+  {pt3dadd(-189,16.66,44.5,0.23)}
+  {pt3dadd(-188.96,16.43,44.5,0.23)}
+  {pt3dadd(-189.18,16.39,44.5,0.23)}
+  {pt3dadd(-189.74,15.58,44.5,0.23)}
+  {pt3dadd(-189.96,15.54,44.5,0.23)}
+  {pt3dadd(-190.74,14.69,44.5,0.23)}
+  {pt3dadd(-190.93,14.42,44.5,0.23)}
+  {pt3dadd(-191.16,14.37,44.5,0.23)}
+  {pt3dadd(-191.34,14.1,44.5,0.23)}
+  {pt3dadd(-191.3,13.88,44.5,0.23)}
+  {pt3dadd(-190.99,13.47,44.5,0.23)}
+  {pt3dadd(-190.72,13.28,44.5,0.23)}
+  {pt3dadd(-190.67,13.06,44.5,0.23)}
+  {pt3dadd(-190.4,12.87,44.5,0.23)}
+  {pt3dadd(-190.36,12.64,44.5,0.23)}
+  {pt3dadd(-190.09,12.46,44.5,0.23)}
+  {pt3dadd(-190.05,12.23,44.5,0.23)}
+  {pt3dadd(-189.55,12.09,44.5,0.23)}
+  {pt3dadd(-189.51,11.86,44.5,0.23)}
+  {pt3dadd(-189.28,11.91,44.5,0.23)}
+  {pt3dadd(-189.24,11.68,44.5,0.23)}
+  {pt3dadd(-188.43,11.13,44.5,0.23)}
+  {pt3dadd(-188.22,9.99,44.5,0.23)}
+  {pt3dadd(-188.41,9.72,44.5,0.23)}
+  {pt3dadd(-188.36,9.5,44.5,0.23)}
+  {pt3dadd(-188.55,9.23,44.5,0.23)}
+  {pt3dadd(-188.51,9,44.5,0.23)}
+  {pt3dadd(-188.87,8.46,44.5,0.23)}
+  {pt3dadd(-188.83,8.24,44.5,0.23)}
+  {pt3dadd(-189.2,7.7,44.5,0.23)}
+  {pt3dadd(-189.16,7.47,44.5,0.23)}
+  {pt3dadd(-189.53,6.93,44.5,0.23)}
+  {pt3dadd(-189.44,6.48,44.5,0.23)}
+  {pt3dadd(-189.67,6.43,44.5,0.23)}
+  {pt3dadd(-189.58,5.98,44.5,0.23)}
+  {pt3dadd(-189.77,5.71,44.5,0.23)}
+  {pt3dadd(-189.68,5.26,44.5,0.23)}
+  {pt3dadd(-189.91,5.22,44.5,0.23)}
+  {pt3dadd(-189.78,4.54,44.5,0.23)}
+}
+
+proc creat9 () {
+  {dendrite[46] connect dendrite[48](0), 1}
+  {access dendrite[48]}
+  {pt3dclear()}
+  {pt3dadd(-214.92,76.72,43.13,0.92)}
+  {pt3dadd(-211.87,78,43.13,0.92)}
+  {pt3dadd(-196.65,82.02,40.85,0.92)}
+  {pt3dadd(-188.29,83.82,39.94,0.92)}
+  {pt3dadd(-188.06,83.86,39.94,0.92)}
+  {pt3dadd(-179.21,85.52,39.94,0.92)}
+  {pt3dadd(-168.67,88.19,38.5,0.92)}
+  {pt3dadd(-162.77,89.3,36.69,0.92)}
+  {pt3dadd(-162.77,89.3,36.38,0.92)}
+  {pt3dadd(-153.03,88.77,35.1,0.92)}
+  {pt3dadd(-144.34,88.75,35.1,0.92)}
+  {pt3dadd(-142.35,88.19,33.2,0.92)}
+
+  {dendrite[48] connect dendrite[49](0), 1}
+  {access dendrite[49]}
+  {pt3dclear()}
+  {pt3dadd(-142.35,88.19,33.2,0.92)}
+  {pt3dadd(-136.96,88.25,33.2,0.92)}
+  {pt3dadd(-133.56,88.89,33.2,0.92)}
+  {pt3dadd(-129.5,87.3,33.13,0.92)}
+  {pt3dadd(-121.46,85.76,33.13,0.92)}
+  {pt3dadd(-117.36,83.94,35.55,0.92)}
+  {pt3dadd(-113.33,83.75,35.55,0.92)}
+  {pt3dadd(-110.12,82.95,34.34,0.92)}
+  {pt3dadd(-104.64,83.33,37.14,0.92)}
+  {pt3dadd(-101.66,82.48,37.14,0.92)}
+  {pt3dadd(-98.4,82.62,36.76,0.92)}
+  {pt3dadd(-93.73,82.79,36.69,0.92)}
+  {pt3dadd(-89.1,80.6,36.31,0.92)}
+  {pt3dadd(-84.54,78.87,36.16,0.92)}
+  {pt3dadd(-79.11,78.72,34.72,0.69)}
+  {pt3dadd(-76.2,79.49,34.72,0.69)}
+  {pt3dadd(-71.24,78.08,33.2,0.69)}
+  {pt3dadd(-65.16,78.27,33.2,0.69)}
+  {pt3dadd(-58.91,78.74,31.84,0.69)}
+  {pt3dadd(-50.28,76.6,31.84,0.69)}
+  {pt3dadd(-45.69,78.4,31.46,0.69)}
+  {pt3dadd(-40,80.63,31.46,0.69)}
+  {pt3dadd(-37.06,83.77,31.46,0.69)}
+  {pt3dadd(-34.92,84.87,31.31,0.69)}
+  {pt3dadd(-31.84,84.74,31.31,0.69)}
+
+  {dendrite[49] connect dendrite[50](0), 1}
+  {access dendrite[50]}
+  {pt3dclear()}
+  {pt3dadd(-31.84,84.74,31.31,0.69)}
+  {pt3dadd(-28.1,87.19,29.88,0.46)}
+  {pt3dadd(-27.39,89.89,29.88,0.46)}
+  {pt3dadd(-19.17,92.49,30.03,0.46)}
+  {pt3dadd(-18.98,92.75,30.03,0.46)}
+  {pt3dadd(-17.36,95.61,32.3,0.46)}
+  {pt3dadd(-14.02,95.25,32.38,0.46)}
+  {pt3dadd(-9.45,98.61,33.06,0.46)}
+  {pt3dadd(-9.45,98.61,32.98,0.46)}
+  {pt3dadd(-4.19,102.1,34.88,0.46)}
+  {pt3dadd(2.74,105.4,37.15,0.46)}
+  {pt3dadd(2.7,105.63,37.15,0.46)}
+  {pt3dadd(10.61,110.04,37.83,0.46)}
+  {pt3dadd(17.63,110.09,37.83,0.46)}
+  {pt3dadd(26.71,109.08,37.83,0.46)}
+  {pt3dadd(26.94,109.12,37.83,0.46)}
+  {pt3dadd(36.13,108.83,38.81,0.46)}
+  {pt3dadd(36.36,108.87,38.81,0.46)}
+  {pt3dadd(42.08,111.03,39.5,0.46)}
+  {pt3dadd(46.79,116.3,38.21,0.46)}
+  {pt3dadd(48.97,119.23,38.44,0.46)}
+  {pt3dadd(51.69,121.11,37.15,0.23)}
+  {pt3dadd(51.65,121.34,37.15,0.23)}
+  {pt3dadd(54.79,123.52,36.84,0.23)}
+  {pt3dadd(54.75,123.75,36.84,0.23)}
+  {pt3dadd(55.81,124.4,35.94,0.23)}
+  {pt3dadd(59.23,124.99,35.94,0.23)}
+  {pt3dadd(59.86,126.74,35.94,0.23)}
+  {pt3dadd(62.18,126.9,33.81,0.23)}
+  {pt3dadd(62.4,126.94,33.81,0.23)}
+  {pt3dadd(66.23,127.84,32.15,0.23)}
+  {pt3dadd(71.49,131.32,33.89,0.23)}
+  {pt3dadd(78.9,135.88,31.09,0.23)}
+  {pt3dadd(81.78,136.85,30.33,0.23)}
+  {pt3dadd(81.78,136.85,31.85,0.23)}
+  {pt3dadd(83.06,138.94,29.65,0.23)}
+  {pt3dadd(87.97,143.07,29.88,0.23)}
+  {pt3dadd(89.57,143.34,29.88,0.23)}
+  {pt3dadd(90.7,144.95,29.88,0.23)}
+  {pt3dadd(95.27,148.31,25.48,0.23)}
+  {pt3dadd(95.27,148.31,26.77,0.23)}
+  {pt3dadd(102.66,151.7,26.16,0.23)}
+  {pt3dadd(102.62,151.92,26.16,0.23)}
+  {pt3dadd(106.7,155.44,26.16,0.23)}
+  {pt3dadd(106.92,155.48,26.16,0.23)}
+  {pt3dadd(111.57,165.18,25.86,0.23)}
+  {pt3dadd(113.61,168.34,25.86,0.23)}
+  {pt3dadd(120.28,176.78,24.88,0.23)}
+  {pt3dadd(122.05,185.52,22.98,0.23)}
+  {pt3dadd(122.01,185.74,22.98,0.23)}
+  {pt3dadd(123.82,188.87,24.57,0.23)}
+  {pt3dadd(123.82,188.87,24.65,0.23)}
+  {pt3dadd(123.97,194.75,23.13,0.23)}
+  {pt3dadd(125.1,197.75,23.13,0.23)}
+  {pt3dadd(125.35,200.37,23.13,0.23)}
+  {pt3dadd(126.16,203.79,23.13,0.23)}
+  {pt3dadd(126.12,204.02,23.13,0.23)}
+  {pt3dadd(125.53,207.43,23.13,0.23)}
+  {pt3dadd(125.7,210.5,23.13,0.23)}
+  {pt3dadd(126.13,214.07,24.19,0.23)}
+  {pt3dadd(126.13,214.07,23.13,0.23)}
+  {pt3dadd(127.18,216.12,22.38,0.23)}
+  {pt3dadd(126.7,220.25,22.38,0.23)}
+  {pt3dadd(117.13,227.59,27.38,0.23)}
+  {pt3dadd(117.29,227.87,27.38,0.23)}
+  {pt3dadd(117.33,228.6,27.38,0.23)}
+  {pt3dadd(117.03,229.71,27.38,0.23)}
+  {pt3dadd(117.13,230.22,27.22,0.23)}
+  {pt3dadd(117.01,230.66,27.07,0.23)}
+  {pt3dadd(117.17,230.95,26.54,0.23)}
+  {pt3dadd(117.05,231.39,25.79,0.23)}
+  {pt3dadd(117.15,231.9,25.18,0.23)}
+  {pt3dadd(116.79,233.24,23.36,0.23)}
+  {pt3dadd(116.5,233.4,25.41,0.23)}
+  {pt3dadd(116.22,233.56,25.41,0.23)}
+  {pt3dadd(115.93,233.72,25.41,0.23)}
+  {pt3dadd(115.59,234.11,25.41,0.23)}
+  {pt3dadd(115.58,236.93,22.45,0.23)}
+  {pt3dadd(114.42,241.16,23.51,0.23)}
+  {pt3dadd(114.14,241.32,23.51,0.23)}
+  {pt3dadd(114.02,241.77,23.51,0.23)}
+  {pt3dadd(114.18,242.05,23.51,0.23)}
+  {pt3dadd(114.28,242.56,23.51,0.23)}
+  {pt3dadd(114.45,242.84,23.51,0.23)}
+  {pt3dadd(114.61,243.13,23.51,0.23)}
+  {pt3dadd(114,245.35,22.23,0.23)}
+  {pt3dadd(114.16,245.64,22.3,0.23)}
+  {pt3dadd(114.26,246.14,22.45,0.23)}
+  {pt3dadd(114.37,246.65,22.45,0.23)}
+  {pt3dadd(113.64,249.32,23.36,0.23)}
+  {pt3dadd(113.9,250.11,23.36,0.23)}
+  {pt3dadd(114,250.61,23.36,0.23)}
+  {pt3dadd(114.11,251.12,23.36,0.23)}
+  {pt3dadd(114.21,251.63,23.36,0.23)}
+  {pt3dadd(114.69,252.48,23.36,0.23)}
+  {pt3dadd(115.12,253.55,23.36,0.23)}
+  {pt3dadd(115.38,254.34,23.36,0.23)}
+  {pt3dadd(115.49,254.84,23.36,0.23)}
+  {pt3dadd(115.81,255.41,23.36,0.23)}
+  {pt3dadd(115.97,255.69,22.98,0.23)}
+  {pt3dadd(116.01,256.42,20.03,0.23)}
+  {pt3dadd(116.66,257.56,20.03,0.23)}
+  {pt3dadd(116.92,258.35,20.03,0.23)}
+  {pt3dadd(117.31,258.69,19.35,0.23)}
+  {pt3dadd(117.41,259.2,19.35,0.23)}
+  {pt3dadd(117.51,259.7,19.42,0.23)}
+  {pt3dadd(117.61,260.21,19.73,0.23)}
+  {pt3dadd(117.66,260.94,19.73,0.23)}
+  {pt3dadd(117.76,261.44,20.1,0.23)}
+  {pt3dadd(117.86,261.95,20.1,0.23)}
+  {pt3dadd(117.9,262.68,20.33,0.23)}
+  {pt3dadd(117.94,263.41,20.56,0.23)}
+  {pt3dadd(117.76,264.07,20.56,0.23)}
+  {pt3dadd(117.86,264.58,20.86,0.23)}
+  {pt3dadd(117.74,265.03,21.09,0.23)}
+  {pt3dadd(118.04,266.54,21.39,0.23)}
+  {pt3dadd(118.31,267.33,21.85,0.23)}
+  {pt3dadd(118.13,268,22,0.23)}
+  {pt3dadd(118.39,268.79,22,0.23)}
+  {pt3dadd(118.21,269.46,22,0.23)}
+  {pt3dadd(118.25,270.19,22,0.23)}
+  {pt3dadd(118.35,270.69,22,0.23)}
+  {pt3dadd(116.83,276.26,22.6,0.23)}
+  {pt3dadd(117.16,276.83,23.82,0.23)}
+  {pt3dadd(117.26,277.33,23.82,0.23)}
+  {pt3dadd(117.36,277.84,23.82,0.23)}
+  {pt3dadd(117.85,278.69,23.82,0.23)}
+  {pt3dadd(118.27,279.76,23.82,0.23)}
+  {pt3dadd(118.64,281.06,23.82,0.23)}
+  {pt3dadd(118.8,281.34,23.89,0.23)}
+  {pt3dadd(119.13,281.91,24.19,0.23)}
+  {pt3dadd(119.43,283.42,24.57,0.23)}
+  {pt3dadd(120.57,288.04,25.18,0.23)}
+  {pt3dadd(118.67,290.63,27,0.23)}
+
+  {dendrite[49] connect dendrite[51](0), 1}
+  {access dendrite[51]}
+  {pt3dclear()}
+  {pt3dadd(-31.84,84.74,31.31,0.69)}
+  {pt3dadd(-27.83,87.01,25.5,0.46)}
+  {pt3dadd(-24.38,87.37,25.5,0.46)}
+  {pt3dadd(-24.15,87.41,25.5,0.46)}
+  {pt3dadd(-18.66,89.53,25.5,0.46)}
+  {pt3dadd(-18.43,89.57,25.5,0.46)}
+  {pt3dadd(-15.21,91.3,24.06,0.46)}
+  {pt3dadd(-11.19,92.46,24.06,0.46)}
+  {pt3dadd(-7.03,94.11,22.85,0.46)}
+  {pt3dadd(-7.07,94.34,22.85,0.46)}
+  {pt3dadd(-2.11,96.84,22.01,0.46)}
+  {pt3dadd(0.54,96.78,23.36,0.23)}
+  {pt3dadd(8.76,99.02,21.32,0.23)}
+  {pt3dadd(8.85,98.8,21.32,0.23)}
+  {pt3dadd(12.43,101.45,21.32,0.23)}
+  {pt3dadd(11.49,102.82,21.32,0.23)}
+  {pt3dadd(15.6,105.66,21.32,0.23)}
+  {pt3dadd(16.69,107.45,21.32,0.23)}
+  {pt3dadd(16.65,106.19,20.63,0.23)}
+  {pt3dadd(17.52,107.95,17.98,0.23)}
+  {pt3dadd(20.34,109.78,17.38,0.23)}
+  {pt3dadd(20.52,111.48,16.32,0.23)}
+  {pt3dadd(21.55,112.76,16.32,0.23)}
+  {pt3dadd(22.3,112.73,15.71,0.23)}
+  {pt3dadd(24.03,112.75,15.56,0.23)}
+  {pt3dadd(24.66,113.85,15.56,0.23)}
+  {pt3dadd(27.1,114.62,15.56,0.23)}
+  {pt3dadd(28.67,116.43,14.8,0.23)}
+  {pt3dadd(30.85,118,13.89,0.23)}
+  {pt3dadd(31.71,118.44,12.98,0.23)}
+  {pt3dadd(33.82,119.56,12.76,0.23)}
+  {pt3dadd(36.04,122.43,12.76,0.23)}
+  {pt3dadd(40.16,124.03,12.23,0.23)}
+  {pt3dadd(43.04,124.95,11.32,0.23)}
+  {pt3dadd(45.46,125.74,11.32,0.23)}
+  {pt3dadd(48.73,127.01,10.56,0.23)}
+  {pt3dadd(51.88,127.73,9.35,0.23)}
+  {pt3dadd(55.35,128.26,9.35,0.23)}
+  {pt3dadd(58.22,127.65,7.83,0.23)}
+  {pt3dadd(60.31,127.24,7.76,0.23)}
+  {pt3dadd(62.16,126.83,7.76,0.23)}
+  {pt3dadd(63.66,126.76,7.76,0.23)}
+  {pt3dadd(66.46,126.41,7.76,0.23)}
+  {pt3dadd(68.26,126.63,7.61,0.23)}
+  {pt3dadd(71.95,127.53,0.71,0.23)}
+  {pt3dadd(75.08,130.02,0.71,0.23)}
+
+  {dendrite[49] connect dendrite[52](0), 1}
+  {access dendrite[52]}
+  {pt3dclear()}
+  {pt3dadd(-31.84,84.74,31.31,0.69)}
+  {pt3dadd(-29.71,84.67,31.31,0.46)}
+  {pt3dadd(-27.52,85.55,31.31,0.46)}
+  {pt3dadd(-26.84,85.68,31.31,0.46)}
+  {pt3dadd(-26.66,85.95,31.31,0.46)}
+  {pt3dadd(-26.47,86.22,31.31,0.46)}
+  {pt3dadd(-24.43,86.6,31.23,0.46)}
+  {pt3dadd(-23.44,86.31,31.23,0.46)}
+  {pt3dadd(-22.94,86.17,31.08,0.46)}
+  {pt3dadd(-22.63,85.76,30.93,0.46)}
+  {pt3dadd(-22.13,85.62,30.93,0.46)}
+  {pt3dadd(-21.64,85.48,30.93,0.46)}
+  {pt3dadd(-20.91,85.38,30.93,0.46)}
+  {pt3dadd(-20.42,85.24,33.05,0.46)}
+  {pt3dadd(-19.74,85.36,33.05,0.46)}
+  {pt3dadd(-19.24,85.22,33.05,0.46)}
+  {pt3dadd(-18.33,85.39,33.05,0.46)}
+  {pt3dadd(-17.83,85.25,33.05,0.46)}
+  {pt3dadd(-17.15,85.38,33.13,0.46)}
+  {pt3dadd(-16.43,85.28,33.13,0.46)}
+  {pt3dadd(-15.93,85.14,33.13,0.46)}
+  {pt3dadd(-15.66,84.95,33.13,0.46)}
+  {pt3dadd(-15.17,84.81,33.13,0.46)}
+  {pt3dadd(-14.67,84.67,32.97,0.46)}
+  {pt3dadd(-14.17,84.53,32.97,0.46)}
+  {pt3dadd(-13.41,84.2,32.6,0.46)}
+  {pt3dadd(-12.96,84.28,31.91,0.46)}
+  {pt3dadd(-12.69,84.1,31.91,0.46)}
+  {pt3dadd(-11.74,84.04,31.91,0.46)}
+  {pt3dadd(-11.01,83.94,31.91,0.46)}
+  {pt3dadd(-10.52,83.8,31.91,0.46)}
+  {pt3dadd(-10.02,83.66,32.9,0.46)}
+  {pt3dadd(-9.56,83.74,32.9,0.46)}
+  {pt3dadd(-8.34,83.5,32.9,0.46)}
+  {pt3dadd(-7.58,83.18,32.9,0.46)}
+  {pt3dadd(-6.81,82.85,32.9,0.46)}
+  {pt3dadd(-6.09,82.75,32.9,0.46)}
+  {pt3dadd(-5.59,82.61,32.9,0.46)}
+  {pt3dadd(-5.1,82.47,32.9,0.46)}
+  {pt3dadd(-4.83,82.28,32.9,0.46)}
+  {pt3dadd(-3.92,82.45,32.9,0.46)}
+  {pt3dadd(-1.31,82.46,32.9,0.46)}
+  {pt3dadd(-0.54,82.14,32.9,0.46)}
+  {pt3dadd(0.53,81.4,32.9,0.46)}
+  {pt3dadd(0.99,81.48,32.9,0.46)}
+  {pt3dadd(1.48,81.34,32.9,0.46)}
+  {pt3dadd(1.94,81.43,32.9,0.46)}
+  {pt3dadd(2.43,81.29,32.9,0.46)}
+  {pt3dadd(4.02,81.58,32.75,0.46)}
+  {pt3dadd(4.21,81.85,32.75,0.46)}
+  {pt3dadd(4.39,82.12,32.07,0.46)}
+  {pt3dadd(4.8,82.43,32.07,0.46)}
+  {pt3dadd(4.95,82.93,32.07,0.46)}
+  {pt3dadd(5.13,83.2,31.84,0.46)}
+  {pt3dadd(5.54,83.51,31.84,0.46)}
+  {pt3dadd(6.18,83.87,31.84,0.46)}
+  {pt3dadd(6.59,84.18,31.84,0.46)}
+  {pt3dadd(12.95,85.37,31.23,0.46)}
+  {pt3dadd(13.5,86.17,30.93,0.46)}
+  {pt3dadd(14.1,86.76,30.93,0.46)}
+  {pt3dadd(14.51,87.07,30.7,0.46)}
+  {pt3dadd(14.92,87.38,30.7,0.46)}
+  {pt3dadd(15.38,87.46,30.63,0.46)}
+  {pt3dadd(15.79,87.78,30.32,0.46)}
+}
+
+proc creat10 () {
+  {dendrite[52] connect dendrite[53](0), 1}
+  {access dendrite[53]}
+  {pt3dclear()}
+  {pt3dadd(15.79,87.78,30.32,0.46)}
+  {pt3dadd(31.48,101.53,28.59,0.46)}
+  {pt3dadd(33.92,103.44,27.82,0.46)}
+  {pt3dadd(33.92,103.44,27.69,0.46)}
+  {pt3dadd(35.4,106.57,21.04,0.46)}
+  {pt3dadd(36.26,106.98,16.56,0.46)}
+  {pt3dadd(36.57,107.75,16.38,0.46)}
+  {pt3dadd(37.2,108.12,15.98,0.46)}
+  {pt3dadd(37.74,108.93,15.71,0.46)}
+  {pt3dadd(37.92,109.21,15.71,0.46)}
+  {pt3dadd(38.06,109.7,15.41,0.46)}
+  {pt3dadd(38.19,110.2,15.23,0.46)}
+  {pt3dadd(38.6,110.52,15.23,0.46)}
+  {pt3dadd(39,110.84,14.97,0.46)}
+  {pt3dadd(39.68,110.98,14.42,0.46)}
+  {pt3dadd(40.09,111.3,14.42,0.46)}
+  {pt3dadd(40.63,112.11,13.48,0.46)}
+  {pt3dadd(40.81,112.39,13.42,0.46)}
+  {pt3dadd(41.26,112.48,13.42,0.46)}
+  {pt3dadd(41.39,112.98,13.42,0.46)}
+  {pt3dadd(41.53,113.48,13.42,0.46)}
+  {pt3dadd(41.93,113.8,12.91,0.46)}
+  {pt3dadd(42.25,114.57,12.91,0.46)}
+  {pt3dadd(42.65,114.88,11.57,0.46)}
+  {pt3dadd(42.97,115.66,11.29,0.46)}
+  {pt3dadd(43.33,116.2,11.2,0.46)}
+  {pt3dadd(43.37,117.15,11.1,0.46)}
+  {pt3dadd(43.5,117.65,10.67,0.46)}
+  {pt3dadd(43.91,117.97,10.67,0.46)}
+  {pt3dadd(44.09,118.24,10.2,0.46)}
+  {pt3dadd(44.5,118.56,9.67,0.46)}
+  {pt3dadd(44.9,118.88,9.45,0.46)}
+  {pt3dadd(44.99,119.6,9.45,0.46)}
+  {pt3dadd(45.35,120.15,8.21,0.46)}
+  {pt3dadd(45.44,120.87,7.51,0.46)}
+  {pt3dadd(45.62,121.14,7.51,0.46)}
+  {pt3dadd(45.75,121.64,7.09,0.46)}
+  {pt3dadd(45.93,121.91,7.09,0.46)}
+  {pt3dadd(46.34,122.23,6.24,0.46)}
+  {pt3dadd(46.52,122.51,6.24,0.46)}
+  {pt3dadd(46.7,122.78,6.24,0.46)}
+  {pt3dadd(47.1,123.1,6.24,0.46)}
+  {pt3dadd(47.01,123.55,6.04,0.46)}
+  {pt3dadd(47.05,124.5,5.62,0.46)}
+  {pt3dadd(46.59,126.76,4.79,0.46)}
+  {pt3dadd(46.73,127.26,4.45,0.46)}
+  {pt3dadd(46.86,127.76,4.45,0.46)}
+  {pt3dadd(46.99,128.26,4.44,0.46)}
+  {pt3dadd(47.89,129.62,4.44,0.46)}
+  {pt3dadd(48.07,129.89,4.26,0.46)}
+  {pt3dadd(48.21,130.39,3.89,0.46)}
+  {pt3dadd(48.34,130.89,3.55,0.46)}
+  {pt3dadd(48.7,131.43,3.15,0.46)}
+  {pt3dadd(48.83,131.93,3.15,0.46)}
+  {pt3dadd(49.19,132.47,3.15,0.46)}
+  {pt3dadd(49.33,132.97,2.36,0.46)}
+  {pt3dadd(49.51,133.24,2.09,0.46)}
+  {pt3dadd(49.6,133.97,2.09,0.46)}
+  {pt3dadd(49.91,134.74,2.09,0.46)}
+  {pt3dadd(50.04,135.24,2.09,0.46)}
+  {pt3dadd(50.18,135.74,2.09,0.46)}
+  {pt3dadd(50.31,136.24,2.09,0.46)}
+  {pt3dadd(50.81,137.28,2.09,0.46)}
+  {pt3dadd(50.94,137.78,2.09,0.46)}
+  {pt3dadd(51.25,138.55,2.03,0.46)}
+  {pt3dadd(51.43,138.82,2.03,0.46)}
+  {pt3dadd(51.57,139.32,1.83,0.46)}
+  {pt3dadd(51.88,140.09,1.83,0.46)}
+  {pt3dadd(52.24,140.63,1.55,0.46)}
+  {pt3dadd(52.55,141.4,1.55,0.46)}
+  {pt3dadd(52.69,141.9,1.55,0.46)}
+  {pt3dadd(52.87,142.18,1.55,0.46)}
+  {pt3dadd(53.27,142.49,1.55,0.46)}
+  {pt3dadd(53.81,143.31,1.55,0.46)}
+  {pt3dadd(54.4,143.9,1.55,0.46)}
+  {pt3dadd(54.53,144.4,1.55,0.46)}
+  {pt3dadd(54.94,144.72,1.55,0.46)}
+  {pt3dadd(56.38,146.9,1.55,0.46)}
+  {pt3dadd(56.97,147.49,1.55,0.46)}
+  {pt3dadd(57.33,148.03,1.55,0.46)}
+  {pt3dadd(57.51,148.31,1.55,0.46)}
+  {pt3dadd(58.09,148.9,1.55,0.46)}
+  {pt3dadd(58.68,149.49,1.55,0.46)}
+  {pt3dadd(59.4,150.58,1.55,0.46)}
+  {pt3dadd(59.81,150.9,1.55,0.46)}
+  {pt3dadd(60.17,151.44,1.55,0.46)}
+  {pt3dadd(60.71,152.26,1.55,0.46)}
+  {pt3dadd(61.02,153.03,1.53,0.46)}
+  {pt3dadd(61.15,153.53,1.53,0.46)}
+  {pt3dadd(61.65,154.57,1.53,0.46)}
+  {pt3dadd(61.96,155.34,1.51,0.46)}
+  {pt3dadd(62.37,155.66,1.51,0.46)}
+  {pt3dadd(62.73,156.2,1.51,0.46)}
+  {pt3dadd(63.99,156.93,1.51,0.46)}
+  {pt3dadd(64.17,157.21,1.51,0.46)}
+  {pt3dadd(64.4,158.43,-0.27,0.46)}
+  {pt3dadd(63.84,161.14,-0.27,0.46)}
+  {pt3dadd(63.93,161.87,-0.27,0.46)}
+  {pt3dadd(64.06,162.37,-0.3,0.46)}
+  {pt3dadd(63.97,162.82,-0.3,0.46)}
+  {pt3dadd(64.33,163.36,-0.47,0.46)}
+  {pt3dadd(64.46,163.86,-0.48,0.46)}
+  {pt3dadd(64.55,164.59,-0.48,0.46)}
+  {pt3dadd(64.69,165.08,-0.48,0.46)}
+  {pt3dadd(64.87,165.36,-0.48,0.46)}
+  {pt3dadd(65,165.85,-0.62,0.46)}
+  {pt3dadd(65.18,166.13,-0.85,0.46)}
+  {pt3dadd(65.31,166.63,-0.85,0.46)}
+  {pt3dadd(65.45,167.12,-1.06,0.46)}
+  {pt3dadd(65.58,167.62,-1.06,0.46)}
+  {pt3dadd(65.76,167.89,-1.58,0.46)}
+  {pt3dadd(65.44,169.48,-1.97,0.46)}
+  {pt3dadd(65.57,169.98,-2.11,0.46)}
+  {pt3dadd(65.43,170.65,-2.17,0.46)}
+  {pt3dadd(65.61,170.93,-2.18,0.46)}
+  {pt3dadd(65.75,171.42,-2.18,0.46)}
+  {pt3dadd(65.88,171.92,-2.18,0.46)}
+  {pt3dadd(66.42,172.74,-2.18,0.46)}
+  {pt3dadd(66.6,173.01,-2.18,0.46)}
+  {pt3dadd(67.5,173.2,-2.82,0.46)}
+  {pt3dadd(67.86,173.74,-2.82,0.46)}
+  {pt3dadd(68,174.24,-2.85,0.46)}
+  {pt3dadd(68.18,174.51,-2.85,0.46)}
+  {pt3dadd(68.09,174.97,-3.64,0.46)}
+  {pt3dadd(67.45,175.78,-3.85,0.46)}
+  {pt3dadd(67.13,176.18,-4.05,0.46)}
+  {pt3dadd(66.81,176.59,-4.05,0.46)}
+  {pt3dadd(66.45,177.22,-4.21,0.46)}
+  {pt3dadd(66.35,177.67,-5.18,0.46)}
+
+  {dendrite[52] connect dendrite[54](0), 1}
+  {access dendrite[54]}
+  {pt3dclear()}
+  {pt3dadd(15.79,87.78,30.32,0.46)}
+  {pt3dadd(19.19,88.41,29.41,0.46)}
+  {pt3dadd(19.83,88.77,29.26,0.46)}
+  {pt3dadd(25.73,89.87,29.26,0.46)}
+  {pt3dadd(26.91,89.86,29.26,0.46)}
+  {pt3dadd(27.63,89.76,29.26,0.46)}
+  {pt3dadd(29.22,90.05,29.26,0.46)}
+  {pt3dadd(30.4,90.04,29.26,0.46)}
+  {pt3dadd(31.08,90.17,29.26,0.46)}
+  {pt3dadd(31.81,90.07,29.19,0.46)}
+  {pt3dadd(32.94,90.28,29.11,0.46)}
+  {pt3dadd(33.66,90.18,29.11,0.46)}
+  {pt3dadd(34.39,90.08,29.11,0.46)}
+  {pt3dadd(35.75,90.34,29.11,0.46)}
+  {pt3dadd(36.25,90.19,29.11,0.46)}
+  {pt3dadd(37.2,90.14,29.11,0.46)}
+  {pt3dadd(38.6,90.17,29.11,0.46)}
+  {pt3dadd(44.5,91.27,28.81,0.46)}
+  {pt3dadd(45.27,90.94,28.81,0.46)}
+  {pt3dadd(46.76,90.52,28.81,0.46)}
+  {pt3dadd(48.12,90.77,28.81,0.46)}
+  {pt3dadd(49.3,90.76,28.81,0.46)}
+  {pt3dadd(50.43,90.97,28.81,0.46)}
+  {pt3dadd(51.07,91.32,28.81,0.46)}
+  {pt3dadd(57.88,92.6,28.28,0.46)}
+  {pt3dadd(58.83,92.54,28.28,0.46)}
+  {pt3dadd(59.56,92.44,28.2,0.46)}
+  {pt3dadd(60.05,92.3,28.2,0.46)}
+  {pt3dadd(60.73,92.43,27.82,0.46)}
+  {pt3dadd(61.23,92.29,27.82,0.46)}
+  {pt3dadd(66,93.18,27.67,0.46)}
+  {pt3dadd(66.64,93.53,27.67,0.46)}
+  {pt3dadd(69.36,94.04,27.52,0.46)}
+  {pt3dadd(69.77,94.35,27.44,0.46)}
+  {pt3dadd(70.68,94.52,27.44,0.46)}
+  {pt3dadd(71.54,94.92,27.37,0.46)}
+  {pt3dadd(72.18,95.27,27.37,0.46)}
+  {pt3dadd(74.23,95.66,26.91,0.46)}
+  {pt3dadd(74.86,96.01,26.91,0.46)}
+  {pt3dadd(75.96,96.45,26.91,0.46)}
+  {pt3dadd(76.6,96.8,26.69,0.46)}
+  {pt3dadd(82.73,97.95,26.46,0.46)}
+  {pt3dadd(83.59,98.35,26.46,0.46)}
+  {pt3dadd(89.04,99.37,26.46,0.46)}
+  {pt3dadd(89.54,99.22,26.46,0.46)}
+  {pt3dadd(90.22,99.35,26.38,0.46)}
+  {pt3dadd(90.94,99.25,26.38,0.46)}
+  {pt3dadd(91.89,99.2,26.31,0.46)}
+  {pt3dadd(92.84,99.14,26.23,0.46)}
+  {pt3dadd(93.11,98.95,26.23,0.46)}
+  {pt3dadd(94.02,99.12,26.23,0.46)}
+  {pt3dadd(94.29,98.94,26.23,0.46)}
+  {pt3dadd(95.42,99.15,26.23,0.46)}
+  {pt3dadd(95.69,98.97,26.23,0.46)}
+  {pt3dadd(99.55,99.69,26.23,0.46)}
+  {pt3dadd(103.19,100.6,26.23,0.46)}
+  {pt3dadd(103.43,101.82,29.04,0.46)}
+  {pt3dadd(103.43,101.82,29.34,0.46)}
+  {pt3dadd(110.53,104.08,29.34,0.46)}
+  {pt3dadd(113.52,104.41,31,0.46)}
+  {pt3dadd(113.97,104.49,31,0.46)}
+  {pt3dadd(114.16,104.76,31,0.46)}
+  {pt3dadd(114.53,105.3,31,0.46)}
+  {pt3dadd(115.12,105.88,31,0.46)}
+  {pt3dadd(115.54,106.19,31,0.46)}
+  {pt3dadd(115.72,106.46,31,0.46)}
+  {pt3dadd(116.4,106.59,30.7,0.46)}
+  {pt3dadd(117.45,107.26,30.7,0.46)}
+  {pt3dadd(119.04,107.55,30.63,0.46)}
+  {pt3dadd(120.36,108.04,31.38,0.46)}
+  {pt3dadd(121,108.39,32.07,0.46)}
+  {pt3dadd(126.22,109.37,32.75,0.46)}
+  {pt3dadd(127.17,109.31,32.75,0.46)}
+  {pt3dadd(127.89,109.21,32.75,0.46)}
+  {pt3dadd(128.39,109.07,33.81,0.46)}
+  {pt3dadd(129.11,108.97,33.81,0.46)}
+  {pt3dadd(129.84,108.87,34.64,0.46)}
+  {pt3dadd(130.33,108.73,35.32,0.46)}
+  {pt3dadd(131.06,108.63,35.32,0.46)}
+  {pt3dadd(132.23,108.61,35.32,0.46)}
+  {pt3dadd(133,108.29,36.08,0.46)}
+  {pt3dadd(134.22,108.05,36.08,0.46)}
+  {pt3dadd(134.94,107.95,36.53,0.46)}
+  {pt3dadd(135.75,107.39,36.53,0.46)}
+  {pt3dadd(136.02,107.21,36.76,0.46)}
+  {pt3dadd(136.29,107.02,36.91,0.46)}
+  {pt3dadd(137.01,106.93,37.14,0.46)}
+  {pt3dadd(137.51,106.78,37.14,0.46)}
+  {pt3dadd(138.23,106.68,37.29,0.46)}
+  {pt3dadd(139.72,106.26,37.29,0.46)}
+  {pt3dadd(140.94,106.02,37.6,0.46)}
+  {pt3dadd(141.44,105.87,37.75,0.46)}
+  {pt3dadd(141.75,105.46,37.9,0.46)}
+  {pt3dadd(142.25,105.32,37.9,0.46)}
+  {pt3dadd(142.74,105.18,38.28,0.46)}
+  {pt3dadd(143.01,105,38.28,0.46)}
+  {pt3dadd(144.01,104.71,38.66,0.46)}
+  {pt3dadd(144.5,104.57,38.66,0.46)}
+  {pt3dadd(145.5,104.29,38.73,0.46)}
+  {pt3dadd(145.95,104.37,38.88,0.46)}
+  {pt3dadd(146.22,104.19,38.88,0.46)}
+  {pt3dadd(146.99,103.86,39.34,0.46)}
+  {pt3dadd(148.6,102.75,39.41,0.46)}
+  {pt3dadd(149.37,102.43,39.79,0.46)}
+  {pt3dadd(149.91,102.06,39.79,0.46)}
+  {pt3dadd(150.18,101.88,39.94,0.46)}
+  {pt3dadd(150.94,101.55,39.94,0.46)}
+  {pt3dadd(151.44,101.41,40.17,0.46)}
+  {pt3dadd(152.47,100.9,40.47,0.46)}
+  {pt3dadd(152.97,100.75,40.47,0.46)}
+  {pt3dadd(153.24,100.57,40.63,0.46)}
+  {pt3dadd(153.74,100.43,41.31,0.46)}
+  {pt3dadd(154.28,100.06,41.31,0.46)}
+  {pt3dadd(154.73,100.14,41.84,0.46)}
+  {pt3dadd(155.04,99.73,41.99,0.46)}
+  {pt3dadd(155.54,99.59,41.99,0.46)}
+  {pt3dadd(156.03,99.45,42.29,0.46)}
+  {pt3dadd(156.53,99.31,42.97,0.46)}
+  {pt3dadd(159.03,99.77,44.56,0.46)}
+  {pt3dadd(159.44,100.09,44.87,0.46)}
+  {pt3dadd(159.89,100.17,45.09,0.46)}
+  {pt3dadd(160.08,100.44,45.09,0.46)}
+  {pt3dadd(160.72,100.79,45.17,0.46)}
+  {pt3dadd(161.13,101.11,45.25,0.46)}
+  {pt3dadd(161.54,101.42,45.25,0.46)}
+  {pt3dadd(163.36,101.76,45.7,0.46)}
+
+  {dendrite[48] connect dendrite[55](0), 1}
+  {access dendrite[55]}
+  {pt3dclear()}
+  {pt3dadd(-142.35,88.19,33.2,0.92)}
+  {pt3dadd(-138.64,90.09,33.81,0.92)}
+  {pt3dadd(-133.86,92.16,32.52,0.92)}
+  {pt3dadd(-131.36,92.63,31.38,0.92)}
+  {pt3dadd(-129.01,92.6,30.4,0.92)}
+  {pt3dadd(-128.64,93.14,30.4,0.92)}
+  {pt3dadd(-128.45,93.41,30.4,0.92)}
+  {pt3dadd(-126,94.1,30.4,0.92)}
+  {pt3dadd(-123.18,95.33,30.4,0.92)}
+  {pt3dadd(-122.25,96.68,29.19,0.92)}
+  {pt3dadd(-121.12,96.89,29.19,0.92)}
+  {pt3dadd(-120.71,97.21,29.19,0.92)}
+  {pt3dadd(-116.62,97.97,27.07,0.92)}
+  {pt3dadd(-115.75,98.37,27.07,0.92)}
+  {pt3dadd(-114.85,98.54,27.07,0.92)}
+  {pt3dadd(-113.98,98.93,27.07,0.92)}
+  {pt3dadd(-110.58,99.57,27.07,0.92)}
+  {pt3dadd(-110.08,99.43,27.07,0.92)}
+  {pt3dadd(-109.63,99.51,27.07,0.92)}
+  {pt3dadd(-109.36,99.33,27.07,0.92)}
+  {pt3dadd(-108.86,99.19,27.07,0.92)}
+  {pt3dadd(-108.59,99,26.54,0.92)}
+
+  {dendrite[55] connect dendrite[56](0), 1}
+  {access dendrite[56]}
+  {pt3dclear()}
+  {pt3dadd(-108.59,99,26.54,0.92)}
+  {pt3dadd(-104.78,100.31,22.98,0.69)}
+  {pt3dadd(-103.77,102.74,22.98,0.69)}
+  {pt3dadd(-101.86,102.78,22.98,0.69)}
+  {pt3dadd(-100.54,104.1,22.98,0.69)}
+  {pt3dadd(-99.21,104.46,22.98,0.69)}
+  {pt3dadd(-98.64,104.14,19.65,0.69)}
+  {pt3dadd(-98.42,104.2,19.65,0.69)}
+  {pt3dadd(-97.57,106.34,18.66,0.69)}
+  {pt3dadd(-97.57,106.34,16.54,0.69)}
+  {pt3dadd(-96.86,107.26,16.17,0.69)}
+  {pt3dadd(-96.86,107.26,16.09,0.69)}
+  {pt3dadd(-95.64,108.06,13.89,0.69)}
+  {pt3dadd(-95.64,108.06,13.74,0.69)}
+  {pt3dadd(-92.85,108.35,13.06,0.69)}
+  {pt3dadd(-91.49,107.76,12.53,0.69)}
+  {pt3dadd(-90.03,107.68,12.45,0.69)}
+  {pt3dadd(-89.3,107.64,12.45,0.69)}
+  {pt3dadd(-87.95,107.05,10.71,0.69)}
+  {pt3dadd(-87.95,107.05,10.56,0.69)}
+  {pt3dadd(-86.1,107.31,10.56,0.69)}
+  {pt3dadd(-84.4,106.34,10.56,0.46)}
+  {pt3dadd(-84.46,106.56,10.56,0.46)}
+  {pt3dadd(-82.62,106.83,10.56,0.23)}
+  {pt3dadd(-78.55,105.07,8.74,0.23)}
+  {pt3dadd(-78.55,105.07,8.67,0.23)}
+  {pt3dadd(-75.33,103.79,7.45,0.23)}
+  {pt3dadd(-73,102.27,6.01,0.23)}
+  {pt3dadd(-70.85,101.42,7.08,0.23)}
+}
+
+proc creat11 () {
+  {dendrite[55] connect dendrite[57](0), 1}
+  {access dendrite[57]}
+  {pt3dclear()}
+  {pt3dadd(-108.59,99,26.54,0.92)}
+  {pt3dadd(-104.5,99.77,25.1,0.92)}
+  {pt3dadd(-104.13,100.31,25.1,0.92)}
+  {pt3dadd(-103.68,100.39,25.02,0.92)}
+  {pt3dadd(-103.27,100.7,25.02,0.92)}
+  {pt3dadd(-103.08,100.97,24.79,0.92)}
+  {pt3dadd(-99.72,101.84,24.79,0.69)}
+  {pt3dadd(-99.49,101.88,24.79,0.69)}
+  {pt3dadd(-98.73,101.55,24.79,0.69)}
+  {pt3dadd(-98.46,101.37,24.79,0.69)}
+  {pt3dadd(-97.74,101.27,24.79,0.69)}
+  {pt3dadd(-96.56,101.25,24.79,0.69)}
+  {pt3dadd(-95.79,100.93,24.79,0.69)}
+  {pt3dadd(-95.3,100.79,24.79,0.69)}
+  {pt3dadd(-94.39,100.95,24.79,0.69)}
+  {pt3dadd(-93.89,100.81,24.79,0.69)}
+  {pt3dadd(-91.39,101.28,24.79,0.69)}
+  {pt3dadd(-90.67,101.18,24.79,0.69)}
+  {pt3dadd(-90.17,101.04,24.79,0.69)}
+  {pt3dadd(-88.83,100.12,24.79,0.69)}
+  {pt3dadd(-88.56,99.93,24.79,0.69)}
+  {pt3dadd(-88.06,99.79,24.64,0.69)}
+  {pt3dadd(-87.57,99.65,24.64,0.69)}
+  {pt3dadd(-87.07,99.51,24.49,0.69)}
+  {pt3dadd(-85.93,99.72,24.19,0.69)}
+  {pt3dadd(-85.44,99.58,24.19,0.69)}
+  {pt3dadd(-83.85,99.87,23.96,0.69)}
+  {pt3dadd(-83.12,99.78,23.81,0.69)}
+  {pt3dadd(-82.44,99.9,23.73,0.69)}
+  {pt3dadd(-81.95,99.76,23.73,0.69)}
+  {pt3dadd(-81.27,99.89,23.73,0.69)}
+  {pt3dadd(-80.77,99.75,23.66,0.69)}
+  {pt3dadd(-80.32,99.83,23.66,0.69)}
+  {pt3dadd(-79.82,99.69,23.66,0.69)}
+  {pt3dadd(-79.1,99.59,23.66,0.69)}
+  {pt3dadd(-78.6,99.45,23.66,0.69)}
+  {pt3dadd(-78.15,99.53,23.66,0.69)}
+  {pt3dadd(-77.65,99.39,23.66,0.69)}
+  {pt3dadd(-77.38,99.21,23.66,0.69)}
+  {pt3dadd(-77.11,99.02,23.66,0.69)}
+  {pt3dadd(-76.84,98.84,23.66,0.69)}
+  {pt3dadd(-75.99,98.06,23.66,0.69)}
+  {pt3dadd(-75.18,97.51,23.66,0.69)}
+  {pt3dadd(-74.46,97.41,23.66,0.69)}
+  {pt3dadd(-74.19,97.22,23.66,0.69)}
+  {pt3dadd(-73.69,97.08,23.66,0.69)}
+  {pt3dadd(-70.97,97.59,23.66,0.69)}
+  {pt3dadd(-70.47,97.45,23.66,0.69)}
+  {pt3dadd(-70.2,97.26,23.66,0.69)}
+  {pt3dadd(-69.48,97.16,23.66,0.69)}
+  {pt3dadd(-68.98,97.02,23.66,0.69)}
+  {pt3dadd(-68.71,96.84,23.66,0.69)}
+  {pt3dadd(-68.4,96.43,23.66,0.69)}
+  {pt3dadd(-68.05,95.79,23.66,0.69)}
+  {pt3dadd(-68.23,95.52,23.66,0.69)}
+  {pt3dadd(-68.42,95.25,23.66,0.69)}
+  {pt3dadd(-68.83,94.94,23.66,0.69)}
+  {pt3dadd(-69.24,94.63,23.66,0.69)}
+  {pt3dadd(-70.15,94.46,23.66,0.69)}
+  {pt3dadd(-65.8,96.16,23.66,0.69)}
+  {pt3dadd(-64.06,98.54,23.66,0.69)}
+  {pt3dadd(-61.32,101.68,21.92,0.69)}
+  {pt3dadd(-59.6,105.02,21.16,0.69)}
+  {pt3dadd(-56.52,107.77,21.16,0.69)}
+  {pt3dadd(-47.46,111.43,19.88,0.69)}
+  {pt3dadd(-41.97,111.49,18.97,0.69)}
+  {pt3dadd(-42.03,111.72,18.97,0.69)}
+  {pt3dadd(-38.81,110.44,18.97,0.69)}
+  {pt3dadd(-38.59,110.5,18.97,0.69)}
+  {pt3dadd(-34.15,110.04,17.53,0.69)}
+  {pt3dadd(-30.1,109.23,17,0.69)}
+  {pt3dadd(-28.71,109.37,17,0.69)}
+  {pt3dadd(-20.96,110,14.95,0.69)}
+  {pt3dadd(-16.69,109.25,12.83,0.69)}
+  {pt3dadd(-16.69,109.25,12.76,0.69)}
+  {pt3dadd(-14.98,110.91,8.51,0.69)}
+  {pt3dadd(-14.98,110.91,8.29,0.69)}
+  {pt3dadd(-10.75,112.07,6.17,0.69)}
+  {pt3dadd(-10.51,113.81,3.82,0.69)}
+  {pt3dadd(-6.62,112.71,3.44,0.69)}
+  {pt3dadd(-3.94,113.44,2,0.69)}
+  {pt3dadd(-3.94,113.44,1.92,0.69)}
+  {pt3dadd(-2.48,115.99,-0.2,0.69)}
+  {pt3dadd(-2.02,117.79,-1.11,0.69)}
+  {pt3dadd(-0.24,118.28,-2.09,0.69)}
+
+  {dendrite[45] connect dendrite[58](0), 1}
+  {access dendrite[58]}
+  {pt3dclear()}
+  {pt3dadd(-250.27,64.71,44.72,1.15)}
+  {pt3dadd(-250.06,67.82,39.03,0.69)}
+  {pt3dadd(-250.06,67.82,38.2,0.69)}
+  {pt3dadd(-248.67,70.43,37.9,0.46)}
+  {pt3dadd(-248.34,71.2,35.7,0.46)}
+  {pt3dadd(-248.47,71.88,35.47,0.46)}
+  {pt3dadd(-248.32,72.37,35.4,0.46)}
+  {pt3dadd(-248.18,72.87,35.4,0.46)}
+  {pt3dadd(-248.27,73.32,35.25,0.46)}
+  {pt3dadd(-247.9,73.86,34.72,0.46)}
+  {pt3dadd(-247.71,74.13,34.34,0.46)}
+  {pt3dadd(-247.3,74.44,33.81,0.46)}
+  {pt3dadd(-247.16,74.94,33.81,0.46)}
+  {pt3dadd(-246.75,75.25,33.81,0.46)}
+  {pt3dadd(-246.83,75.71,33.2,0.46)}
+  {pt3dadd(-246.69,76.2,32.9,0.46)}
+  {pt3dadd(-246.51,76.47,32.67,0.46)}
+  {pt3dadd(-246.36,76.97,32.67,0.46)}
+  {pt3dadd(-246.62,78.33,32.22,0.46)}
+  {pt3dadd(-247.7,79.07,32.22,0.46)}
+  {pt3dadd(-248.28,79.66,32.22,0.46)}
+  {pt3dadd(-247.18,80.1,32.22,0.46)}
+  {pt3dadd(-246.5,80.23,32.22,0.46)}
+  {pt3dadd(-246.59,80.68,32.14,0.46)}
+  {pt3dadd(-246.45,81.18,30.17,0.46)}
+  {pt3dadd(-246.3,81.67,30.17,0.46)}
+  {pt3dadd(-246.16,82.17,29.72,0.46)}
+  {pt3dadd(-246.1,83.12,29.64,0.46)}
+  {pt3dadd(-245.96,83.62,29.04,0.46)}
+  {pt3dadd(-245.82,84.11,29.04,0.46)}
+  {pt3dadd(-245.68,84.61,28.73,0.46)}
+  {pt3dadd(-245.76,85.06,28.51,0.46)}
+  {pt3dadd(-245.39,85.6,28.28,0.46)}
+  {pt3dadd(-245.25,86.1,27.29,0.46)}
+  {pt3dadd(-244.84,86.41,27.29,0.46)}
+  {pt3dadd(-244.39,86.49,27.29,0.46)}
+  {pt3dadd(-243.89,86.35,27.29,0.46)}
+  {pt3dadd(-243.17,86.25,27.29,0.46)}
+  {pt3dadd(-242.94,86.29,25.17,0.46)}
+  {pt3dadd(-242.26,86.42,24.26,0.46)}
+  {pt3dadd(-242.07,86.69,24.11,0.46)}
+  {pt3dadd(-242.24,87.6,24.11,0.46)}
+  {pt3dadd(-242.1,88.09,24.11,0.46)}
+  {pt3dadd(-241.96,88.59,24.11,0.46)}
+  {pt3dadd(-242.04,89.04,24.11,0.46)}
+  {pt3dadd(-241.86,89.31,24.04,0.46)}
+  {pt3dadd(-241.72,89.81,23.66,0.46)}
+  {pt3dadd(-241.8,90.26,23.28,0.46)}
+  {pt3dadd(-241.62,90.53,23.2,0.46)}
+  {pt3dadd(-241.43,90.8,23.2,0.46)}
+  {pt3dadd(-241.29,91.3,22.82,0.46)}
+  {pt3dadd(-240.88,91.61,22.82,0.46)}
+  {pt3dadd(-240.47,91.92,22.6,0.46)}
+  {pt3dadd(-240.06,92.23,22.6,0.46)}
+  {pt3dadd(-239.42,92.59,22.6,0.46)}
+  {pt3dadd(-238.51,92.76,21.99,0.46)}
+  {pt3dadd(-238.37,93.25,21.99,0.46)}
+  {pt3dadd(-237.96,93.56,21.99,0.46)}
+  {pt3dadd(-236.82,93.78,21.99,0.46)}
+  {pt3dadd(-236.18,94.13,21.99,0.46)}
+  {pt3dadd(-234.59,94.43,21.99,0.46)}
+  {pt3dadd(-231.01,95.95,22,0.23)}
+  {pt3dadd(-230.63,96.3,22,0.23)}
+  {pt3dadd(-230.24,96.64,22,0.23)}
+  {pt3dadd(-229.76,97.49,22,0.23)}
+  {pt3dadd(-229.59,97.78,22,0.23)}
+  {pt3dadd(-229.27,98.34,22.07,0.23)}
+  {pt3dadd(-229.11,98.63,22.15,0.23)}
+  {pt3dadd(-228.94,98.91,22.15,0.23)}
+  {pt3dadd(-228.34,99.31,22.23,0.23)}
+  {pt3dadd(-227.67,99.5,23.21,0.23)}
+  {pt3dadd(-226.61,100.02,21.77,0.23)}
+  {pt3dadd(-225.4,100.83,21.77,0.23)}
+  {pt3dadd(-225.24,101.12,21.09,0.23)}
+  {pt3dadd(-224.18,101.64,21.09,0.23)}
+  {pt3dadd(-222.97,102.45,20.48,0.23)}
+  {pt3dadd(-222.7,103.24,20.33,0.23)}
+  {pt3dadd(-221.99,104.15,19.95,0.23)}
+  {pt3dadd(-221.67,104.72,19.95,0.23)}
+  {pt3dadd(-221.51,105,19.73,0.23)}
+  {pt3dadd(-220.62,105.24,19.57,0.23)}
+  {pt3dadd(-219.79,105.71,18.44,0.23)}
+  {pt3dadd(-218.96,106.17,18.44,0.23)}
+  {pt3dadd(-217.86,107.43,18.06,0.23)}
+  {pt3dadd(-217.6,108.22,18.06,0.23)}
+  {pt3dadd(-217.43,108.5,17.38,0.23)}
+  {pt3dadd(-217.27,108.79,17.23,0.23)}
+  {pt3dadd(-216.73,109.41,16.92,0.23)}
+  {pt3dadd(-216.28,109.53,16.92,0.23)}
+  {pt3dadd(-216.18,110.04,16.39,0.23)}
+  {pt3dadd(-216.3,110.49,16.17,0.23)}
+  {pt3dadd(-215.81,111.34,16.01,0.23)}
+  {pt3dadd(-215.65,111.62,16.01,0.23)}
+  {pt3dadd(-215.16,112.47,16.01,0.23)}
+  {pt3dadd(-214.62,113.1,13.97,0.23)}
+  {pt3dadd(-214.29,113.66,13.59,0.23)}
+  {pt3dadd(-214.41,114.11,13.51,0.23)}
+  {pt3dadd(-214.25,114.39,13.44,0.23)}
+  {pt3dadd(-214.15,114.9,13.29,0.23)}
+  {pt3dadd(-213.18,116.6,13.29,0.23)}
+  {pt3dadd(-212.29,116.84,13.29,0.23)}
+  {pt3dadd(-212.12,117.12,13.29,0.23)}
+  {pt3dadd(-211.68,117.24,13.29,0.23)}
+  {pt3dadd(-209.87,119.41,13.29,0.23)}
+  {pt3dadd(-209.71,119.69,13.29,0.23)}
+  {pt3dadd(-208.82,119.94,13.29,0.23)}
+  {pt3dadd(-208.44,120.28,13.29,0.23)}
+  {pt3dadd(-208.27,120.56,13.29,0.23)}
+  {pt3dadd(-207.89,120.91,13.29,0.23)}
+  {pt3dadd(-207.73,121.19,13.29,0.23)}
+  {pt3dadd(-207.46,121.98,13.29,0.23)}
+  {pt3dadd(-206.98,122.83,10.79,0.23)}
+  {pt3dadd(-206.59,123.17,10.79,0.23)}
+  {pt3dadd(-206.21,123.52,10.79,0.23)}
+  {pt3dadd(-205.15,124.04,10.79,0.23)}
+  {pt3dadd(-204.54,124.45,10.79,0.23)}
+  {pt3dadd(-202.98,124.87,9.2,0.23)}
+  {pt3dadd(-202.21,125.56,9.2,0.23)}
+  {pt3dadd(-201.57,126.7,8.44,0.23)}
+  {pt3dadd(-200.8,127.38,8.44,0.23)}
+  {pt3dadd(-200.41,127.73,8.44,0.23)}
+  {pt3dadd(-200.03,128.07,8.44,0.23)}
+  {pt3dadd(-199.7,128.64,8.44,0.23)}
+  {pt3dadd(-199.15,129.27,8.44,0.23)}
+  {pt3dadd(-198.26,129.51,7.83,0.23)}
+  {pt3dadd(-197.19,129.08,6.24,0.23)}
+  {pt3dadd(-196.52,129.26,5.41,0.23)}
+  {pt3dadd(-193.34,129.89,5.26,0.23)}
+  {pt3dadd(-192.83,129.79,3.89,0.23)}
+  {pt3dadd(-192.23,130.2,3.36,0.23)}
+  {pt3dadd(-191.4,130.66,2.15,0.23)}
+  {pt3dadd(-188.94,131.33,0.48,0.23)}
+  {pt3dadd(-188.84,131.83,0.26,0.23)}
+  {pt3dadd(-188.68,132.12,-0.05,0.23)}
+  {pt3dadd(-188.8,132.56,-0.27,0.23)}
+  {pt3dadd(-188.64,132.85,-0.42,0.23)}
+  {pt3dadd(-188.54,133.35,-0.42,0.23)}
+  {pt3dadd(-188.38,133.64,-0.5,0.23)}
+  {pt3dadd(-187.71,133.82,-0.5,0.23)}
+  {pt3dadd(-187.61,134.32,-0.58,0.23)}
+  {pt3dadd(-187.44,134.61,-0.58,0.23)}
+  {pt3dadd(-187.28,134.89,-0.58,0.23)}
+
+  {dendrite[32] connect dendrite[59](0), 1}
+  {access dendrite[59]}
+  {pt3dclear()}
+  {pt3dadd(-285.14,53.96,44.94,3)}
+  {pt3dadd(-281.6,59.34,36.23,0.46)}
+  {pt3dadd(-280.24,59.6,36.23,0.46)}
+  {pt3dadd(-279.74,59.45,36.23,0.46)}
+  {pt3dadd(-277.37,59.63,31.91,0.46)}
+
+  {dendrite[59] connect dendrite[60](0), 1}
+  {access dendrite[60]}
+  {pt3dclear()}
+  {pt3dadd(-277.37,59.63,31.91,0.46)}
+  {pt3dadd(-278.25,58.06,34.03,0.69)}
+  {pt3dadd(-275.52,55.99,32.29,0.69)}
+  {pt3dadd(-274.48,55.48,32.29,0.69)}
+  {pt3dadd(-274.21,55.29,32.29,0.69)}
+  {pt3dadd(-273.4,54.74,32.14,0.69)}
+  {pt3dadd(-273.13,54.55,31.76,0.69)}
+  {pt3dadd(-272.92,53.42,30.47,0.69)}
+  {pt3dadd(-273.02,52.7,30.47,0.69)}
+  {pt3dadd(-273.21,52.43,30.02,0.69)}
+  {pt3dadd(-273.39,52.16,30.02,0.69)}
+  {pt3dadd(-273.31,51.71,30.02,0.69)}
+  {pt3dadd(-273.63,50.94,30.02,0.69)}
+  {pt3dadd(-273.38,49.58,29.64,0.69)}
+  {pt3dadd(-272.84,49.21,29.64,0.69)}
+  {pt3dadd(-272.39,49.3,29.64,0.69)}
+  {pt3dadd(-272.07,48.88,29.64,0.69)}
+  {pt3dadd(-271.58,48.74,29.64,0.69)}
+  {pt3dadd(-271.27,48.33,29.64,0.69)}
+  {pt3dadd(-271.14,47.65,29.64,0.69)}
+  {pt3dadd(-271.24,46.93,29.64,0.69)}
+  {pt3dadd(-271.38,46.43,29.64,0.69)}
+  {pt3dadd(-271.52,45.94,29.64,0.69)}
+  {pt3dadd(-271.66,45.44,29.41,0.69)}
+  {pt3dadd(-272.03,44.9,28.96,0.69)}
+  {pt3dadd(-272.22,44.63,28.43,0.69)}
+  {pt3dadd(-272.36,44.14,27.9,0.69)}
+  {pt3dadd(-272.54,43.87,27.9,0.69)}
+  {pt3dadd(-272.73,43.6,27.6,0.69)}
+  {pt3dadd(-273.1,43.06,27.44,0.69)}
+  {pt3dadd(-273.24,42.56,27.37,0.69)}
+  {pt3dadd(-273.38,42.07,27.37,0.69)}
+  {pt3dadd(-273.79,41.75,27.29,0.69)}
+  {pt3dadd(-273.94,41.26,27.22,0.69)}
+  {pt3dadd(-273.77,40.35,27.07,0.69)}
+  {pt3dadd(-273.91,39.85,26.84,0.69)}
+  {pt3dadd(-273.4,37.13,26.08,0.69)}
+  {pt3dadd(-273.54,36.64,25.85,0.69)}
+  {pt3dadd(-273.73,36.37,25.78,0.69)}
+  {pt3dadd(-273.3,34.1,25.48,0.69)}
+  {pt3dadd(-273.44,33.6,25.17,0.69)}
+  {pt3dadd(-273.63,33.33,25.02,0.69)}
+  {pt3dadd(-273.81,33.06,25.02,0.69)}
+  {pt3dadd(-274,32.79,25.02,0.69)}
+  {pt3dadd(-274.37,32.26,25.02,0.69)}
+  {pt3dadd(-274.55,31.99,25.02,0.69)}
+  {pt3dadd(-274.69,31.49,25.02,0.69)}
+  {pt3dadd(-274.52,30.58,24.64,0.69)}
+  {pt3dadd(-274.67,30.09,24.64,0.69)}
+  {pt3dadd(-274.33,28.27,24.26,0.69)}
+  {pt3dadd(-274.01,27.86,24.26,0.69)}
+  {pt3dadd(-273.7,27.45,24.26,0.69)}
+  {pt3dadd(-273.53,26.54,24.19,0.69)}
+  {pt3dadd(-273.94,26.23,24.19,0.69)}
+  {pt3dadd(-274.36,25.92,24.04,0.69)}
+  {pt3dadd(-274.81,25.83,23.96,0.69)}
+  {pt3dadd(-275.22,25.52,23.96,0.69)}
+  {pt3dadd(-275.63,25.21,23.96,0.69)}
+  {pt3dadd(-275.82,24.94,23.35,0.69)}
+  {pt3dadd(-276.56,23.86,22.9,0.69)}
+  {pt3dadd(-276.74,23.59,22.9,0.69)}
+  {pt3dadd(-276.92,23.33,22.82,0.69)}
+  {pt3dadd(-277.11,23.06,22.37,0.69)}
+  {pt3dadd(-277.52,22.74,22.22,0.69)}
+  {pt3dadd(-277.66,22.25,22.22,0.69)}
+  {pt3dadd(-277.76,21.53,22.22,0.69)}
+  {pt3dadd(-278.93,18.47,24.04,0.23)}
+  {pt3dadd(-279.82,18.22,24.04,0.23)}
+  {pt3dadd(-280.2,17.88,24.04,0.23)}
+  {pt3dadd(-282.21,17.33,24.04,0.23)}
+  {pt3dadd(-285.35,14.8,20.71,0.23)}
+  {pt3dadd(-285.17,14.14,20.41,0.23)}
+  {pt3dadd(-285.43,13.35,19.5,0.23)}
+  {pt3dadd(-286.26,12.88,19.5,0.23)}
+  {pt3dadd(-286.43,12.6,19.5,0.23)}
+  {pt3dadd(-286.81,12.25,19.5,0.23)}
+  {pt3dadd(-287.26,12.13,19.5,0.23)}
+  {pt3dadd(-287.82,12.46,19.42,0.23)}
+  {pt3dadd(-288.11,12.62,19.04,0.23)}
+  {pt3dadd(-289.46,13.2,15.86,0.23)}
+  {pt3dadd(-288.76,11.48,15.86,0.23)}
+  {pt3dadd(-289.36,11.08,15.86,0.23)}
+  {pt3dadd(-289.59,11.02,15.86,0.23)}
+  {pt3dadd(-288.66,9.36,14.35,0.23)}
+  {pt3dadd(-288.66,9.36,14.04,0.23)}
+  {pt3dadd(-289.18,7.78,11.32,0.23)}
+  {pt3dadd(-289.45,6.99,11.32,0.23)}
+  {pt3dadd(-289.83,6.65,11.32,0.23)}
+  {pt3dadd(-290.22,6.3,11.32,0.23)}
+  {pt3dadd(-290.66,6.18,9.95,0.23)}
+  {pt3dadd(-291.76,4.93,9.35,0.23)}
+  {pt3dadd(-291.96,3.91,9.35,0.23)}
+  {pt3dadd(-292.06,3.41,5.03,0.23)}
+  {pt3dadd(-292.26,2.4,5.03,0.23)}
+  {pt3dadd(-291.9,1.06,3.29,0.23)}
+  {pt3dadd(-291.39,0.96,2.38,0.23)}
+  {pt3dadd(-290.89,0.86,2.38,0.23)}
+  {pt3dadd(-290.6,0.7,2.38,0.23)}
+  {pt3dadd(-290.18,-0.86,0.79,0.23)}
+  {pt3dadd(-289.9,-1.02,0.71,0.23)}
+  {pt3dadd(-289.45,-0.9,0.79,0.23)}
+  {pt3dadd(-289.17,-1.06,0.79,0.23)}
+  {pt3dadd(-288.93,-1.96,0.86,0.23)}
+  {pt3dadd(-289.03,-2.46,0.86,0.23)}
+  {pt3dadd(-289.13,-2.97,0.86,0.23)}
+  {pt3dadd(-289.17,-3.7,0.86,0.23)}
+  {pt3dadd(-289.33,-3.98,0.71,0.23)}
+  {pt3dadd(-289.82,-4.83,0.18,0.23)}
+  {pt3dadd(-289.37,-4.71,0.18,0.23)}
+  {pt3dadd(-289.09,-4.87,0.18,0.23)}
+  {pt3dadd(-288.64,-4.75,0.18,0.23)}
+  {pt3dadd(-288.36,-4.91,0.18,0.23)}
+  {pt3dadd(-288.08,-5.07,0.18,0.23)}
+  {pt3dadd(-287.71,-6.41,-0.88,0.23)}
+  {pt3dadd(-288.16,-6.53,-0.95,0.23)}
+  {pt3dadd(-288.32,-6.81,-0.95,0.23)}
+  {pt3dadd(-288.77,-6.93,-1.18,0.23)}
+  {pt3dadd(-288.93,-7.22,-1.18,0.23)}
+}
+
+proc creat12 () {
+  {dendrite[59] connect dendrite[61](0), 1}
+  {access dendrite[61]}
+  {pt3dclear()}
+  {pt3dadd(-277.37,59.63,31.91,0.46)}
+  {pt3dadd(-277.27,60.35,31.91,0.69)}
+  {pt3dadd(-276.59,60.48,31.91,0.69)}
+  {pt3dadd(-276.05,60.11,31.91,0.69)}
+  {pt3dadd(-275.33,60.01,31.91,0.69)}
+  {pt3dadd(-274.88,60.1,31.91,0.69)}
+  {pt3dadd(-274.73,60.59,31.91,0.69)}
+  {pt3dadd(-274.86,61.27,31.91,0.69)}
+  {pt3dadd(-275.07,62.41,31.91,0.69)}
+  {pt3dadd(-275.24,63.32,31.91,0.69)}
+  {pt3dadd(-275.06,63.59,31.91,0.69)}
+  {pt3dadd(-274.69,64.12,31.91,0.69)}
+  {pt3dadd(-274.5,64.39,31.91,0.69)}
+  {pt3dadd(-274.09,64.71,31.91,0.69)}
+
+  {dendrite[61] connect dendrite[62](0), 1}
+  {access dendrite[62]}
+  {pt3dclear()}
+  {pt3dadd(-274.09,64.71,31.91,0.69)}
+  {pt3dadd(-274.62,66.25,31.91,0.69)}
+  {pt3dadd(-275.3,66.12,31.91,0.69)}
+  {pt3dadd(-275.48,65.85,31.91,0.69)}
+  {pt3dadd(-275.81,65.09,31.91,0.69)}
+  {pt3dadd(-276.22,64.78,31.46,0.69)}
+  {pt3dadd(-276.86,64.42,31.46,0.69)}
+  {pt3dadd(-277.09,64.38,30.17,0.69)}
+  {pt3dadd(-277.36,64.56,30.17,0.69)}
+  {pt3dadd(-277.67,64.98,30.17,0.69)}
+  {pt3dadd(-277.97,66.56,30.1,0.69)}
+  {pt3dadd(-278.23,66.75,30.1,0.69)}
+  {pt3dadd(-278.5,66.93,29.19,0.69)}
+  {pt3dadd(-279.23,67.03,28.88,0.69)}
+  {pt3dadd(-282.86,66.35,26.54,0.69)}
+  {pt3dadd(-283.44,66.95,26.38,0.69)}
+  {pt3dadd(-283.75,67.36,26.23,0.69)}
+  {pt3dadd(-284.02,67.54,26.23,0.69)}
+  {pt3dadd(-284.33,67.95,25.48,0.69)}
+  {pt3dadd(-284.69,68.59,24.79,0.69)}
+  {pt3dadd(-285,69,24.64,0.69)}
+  {pt3dadd(-285.27,69.19,24.57,0.69)}
+  {pt3dadd(-286.04,69.51,23.81,0.69)}
+  {pt3dadd(-286.31,69.7,23.2,0.69)}
+  {pt3dadd(-286.99,69.57,23.2,0.69)}
+  {pt3dadd(-287.48,69.71,22.6,0.69)}
+  {pt3dadd(-289.75,69.29,20.02,0.69)}
+  {pt3dadd(-290.87,70.25,21.76,0.69)}
+  {pt3dadd(-291.45,70.85,20.4,0.69)}
+  {pt3dadd(-291.95,70.99,19.95,0.69)}
+  {pt3dadd(-292.67,71.09,19.95,0.69)}
+  {pt3dadd(-294.72,70.71,20.25,0.69)}
+  {pt3dadd(-295.03,71.12,20.25,0.69)}
+  {pt3dadd(-295.88,71.9,20.25,0.69)}
+  {pt3dadd(-296.15,72.08,20.25,0.69)}
+  {pt3dadd(-297.23,71.92,25.41,0.23)}
+  {pt3dadd(-299.69,68.62,25.41,0.23)}
+  {pt3dadd(-299.75,68.84,25.41,0.23)}
+  {pt3dadd(-304.1,73.4,25.41,0.23)}
+  {pt3dadd(-304.16,73.62,25.41,0.23)}
+  {pt3dadd(-305.33,76.17,25.41,0.23)}
+  {pt3dadd(-306.04,77.89,25.41,0.23)}
+  {pt3dadd(-306.22,78.56,25.41,0.23)}
+  {pt3dadd(-306.06,78.84,25.41,0.23)}
+  {pt3dadd(-306.73,81.29,25.41,0.23)}
+  {pt3dadd(-306.95,81.23,25.41,0.23)}
+  {pt3dadd(-307.01,81.46,25.41,0.23)}
+  {pt3dadd(-307.36,81.84,25.41,0.23)}
+  {pt3dadd(-307.92,82.16,25.41,0.23)}
+  {pt3dadd(-307.98,82.39,25.41,0.23)}
+  {pt3dadd(-308.33,82.77,25.41,0.23)}
+  {pt3dadd(-309.74,83.58,25.41,0.23)}
+  {pt3dadd(-309.81,83.8,25.41,0.23)}
+  {pt3dadd(-311.51,84.78,25.41,0.23)}
+  {pt3dadd(-311.73,84.71,25.41,0.23)}
+  {pt3dadd(-312.58,85.2,25.41,0.23)}
+  {pt3dadd(-313.59,85.4,25.41,0.23)}
+  {pt3dadd(-313.88,85.57,25.41,0.23)}
+  {pt3dadd(-314.38,85.67,25.41,0.23)}
+  {pt3dadd(-314.6,85.61,25.41,0.23)}
+  {pt3dadd(-315.17,85.93,25.41,0.23)}
+  {pt3dadd(-316.18,86.13,25.41,0.23)}
+  {pt3dadd(-316.47,86.29,25.41,0.23)}
+  {pt3dadd(-316.69,86.23,25.41,0.23)}
+  {pt3dadd(-318.21,86.54,25.41,0.23)}
+  {pt3dadd(-318.94,86.58,25.41,0.23)}
+  {pt3dadd(-320.34,86.44,25.41,0.23)}
+  {pt3dadd(-320.78,86.32,25.41,0.23)}
+  {pt3dadd(-321.57,86.58,25.41,0.23)}
+  {pt3dadd(-323.31,86.82,25.41,0.23)}
+  {pt3dadd(-323.37,87.04,25.41,0.23)}
+  {pt3dadd(-323.72,87.43,25.41,0.23)}
+  {pt3dadd(-324,87.59,25.41,0.23)}
+  {pt3dadd(-324.12,88.04,25.41,0.23)}
+  {pt3dadd(-324.35,87.98,25.41,0.23)}
+  {pt3dadd(-324.69,88.36,25.41,0.23)}
+  {pt3dadd(-324.81,88.81,25.41,0.23)}
+  {pt3dadd(-325.09,88.97,25.41,0.23)}
+  {pt3dadd(-325.15,89.19,25.41,0.23)}
+  {pt3dadd(-325.44,89.35,25.41,0.23)}
+  {pt3dadd(-325.5,89.57,25.41,0.23)}
+  {pt3dadd(-325.78,89.74,25.41,0.23)}
+  {pt3dadd(-325.84,89.96,25.41,0.23)}
+  {pt3dadd(-326.19,90.34,25.41,0.23)}
+  {pt3dadd(-326.31,90.79,25.41,0.23)}
+  {pt3dadd(-326.53,90.73,25.41,0.23)}
+  {pt3dadd(-326.65,91.17,25.41,0.23)}
+  {pt3dadd(-326.88,91.11,25.41,0.23)}
+  {pt3dadd(-327.22,91.5,25.41,0.23)}
+  {pt3dadd(-328.23,91.7,25.41,0.23)}
+  {pt3dadd(-328.45,91.64,25.41,0.23)}
+  {pt3dadd(-328.74,91.8,25.41,0.23)}
+  {pt3dadd(-328.96,91.74,25.41,0.23)}
+  {pt3dadd(-329.47,91.84,25.41,0.23)}
+  {pt3dadd(-329.91,91.72,25.41,0.23)}
+  {pt3dadd(-330.2,91.88,25.41,0.23)}
+  {pt3dadd(-331.21,92.09,25.41,0.23)}
+  {pt3dadd(-331.55,92.47,25.41,0.23)}
+  {pt3dadd(-332,92.35,25.41,0.23)}
+  {pt3dadd(-332.28,92.51,25.41,0.23)}
+  {pt3dadd(-332.34,92.73,25.41,0.23)}
+  {pt3dadd(-332.69,93.12,25.41,0.23)}
+  {pt3dadd(-332.97,93.28,25.41,0.23)}
+  {pt3dadd(-333.31,93.66,25.41,0.23)}
+  {pt3dadd(-333.54,93.6,25.41,0.23)}
+  {pt3dadd(-333.6,93.83,25.41,0.23)}
+  {pt3dadd(-333.88,93.99,25.41,0.23)}
+  {pt3dadd(-334.23,94.37,25.41,0.23)}
+  {pt3dadd(-334.45,94.31,25.41,0.23)}
+  {pt3dadd(-335.36,95.02,25.41,0.23)}
+  {pt3dadd(-335.58,94.96,25.41,0.23)}
+  {pt3dadd(-335.64,95.18,25.41,0.23)}
+  {pt3dadd(-336.49,95.67,25.41,0.23)}
+  {pt3dadd(-336.72,95.61,25.41,0.23)}
+
+  {dendrite[61] connect dendrite[63](0), 1}
+  {access dendrite[63]}
+  {pt3dclear()}
+  {pt3dadd(-274.09,64.71,31.91,0.69)}
+  {pt3dadd(-272.79,67.77,32.6,0.69)}
+  {pt3dadd(-273.21,70.04,31.16,0.69)}
+  {pt3dadd(-273.69,71.35,31.16,0.46)}
+  {pt3dadd(-274.05,71.99,31.16,0.46)}
+  {pt3dadd(-274.56,74.71,30.4,0.46)}
+  {pt3dadd(-274.41,75.21,30.4,0.46)}
+  {pt3dadd(-274.54,75.89,30.32,0.46)}
+  {pt3dadd(-274.36,76.16,30.32,0.46)}
+  {pt3dadd(-274.17,76.43,30.32,0.46)}
+  {pt3dadd(-273.99,76.7,30.32,0.46)}
+  {pt3dadd(-273.85,77.19,30.32,0.46)}
+  {pt3dadd(-273.66,77.46,30.32,0.46)}
+  {pt3dadd(-272.98,77.59,30.32,0.46)}
+  {pt3dadd(-272.57,77.9,30.32,0.46)}
+  {pt3dadd(-272.11,77.99,30.32,0.46)}
+  {pt3dadd(-271.7,78.3,31,0.46)}
+  {pt3dadd(-271.06,78.65,31.23,0.46)}
+  {pt3dadd(-270.65,78.97,27.22,0.46)}
+  {pt3dadd(-270.14,80,27.22,0.46)}
+  {pt3dadd(-269.77,80.54,27.22,0.46)}
+  {pt3dadd(-269.86,80.99,27.22,0.46)}
+  {pt3dadd(-269.71,81.49,27.22,0.46)}
+  {pt3dadd(-269.57,81.98,27.22,0.46)}
+  {pt3dadd(-269.29,82.98,27.14,0.46)}
+  {pt3dadd(-268.59,84.28,27.14,0.46)}
+  {pt3dadd(-268.18,84.59,27.14,0.46)}
+  {pt3dadd(-268,84.86,27.14,0.46)}
+  {pt3dadd(-267.58,85.17,27.14,0.46)}
+  {pt3dadd(-267.17,85.49,27.14,0.46)}
+  {pt3dadd(-266.53,85.84,25.85,0.46)}
+  {pt3dadd(-265.94,86.42,25.85,0.46)}
+  {pt3dadd(-265.34,87,25.78,0.46)}
+  {pt3dadd(-264.93,87.31,25.78,0.46)}
+  {pt3dadd(-264.19,88.39,25.78,0.46)}
+  {pt3dadd(-264.01,88.66,25.78,0.46)}
+  {pt3dadd(-263.18,89.28,25.02,0.46)}
+  {pt3dadd(-261.99,90.45,25.02,0.46)}
+  {pt3dadd(-261.4,91.03,23.13,0.46)}
+  {pt3dadd(-260.49,91.2,23.13,0.46)}
+  {pt3dadd(-259.62,91.59,23.13,0.46)}
+  {pt3dadd(-259.21,91.91,23.13,0.46)}
+  {pt3dadd(-258.12,92.34,23.13,0.46)}
+  {pt3dadd(-255.85,92.77,23.13,0.46)}
+  {pt3dadd(-255.64,91.63,23.13,0.46)}
+  {pt3dadd(-255.14,91.49,23.13,0.46)}
+  {pt3dadd(-254.64,91.35,23.13,0.46)}
+  {pt3dadd(-254.15,91.21,23.13,0.46)}
+  {pt3dadd(-253.65,91.07,23.13,0.46)}
+  {pt3dadd(-249.4,95.96,23.13,0.23)}
+  {pt3dadd(-244.06,100.05,21.69,0.23)}
+  {pt3dadd(-239.6,103.89,21.69,0.23)}
+  {pt3dadd(-231.57,111.34,20.86,0.23)}
+  {pt3dadd(-228.03,115.89,20.1,0.23)}
+  {pt3dadd(-224.4,121.9,18.89,0.23)}
+  {pt3dadd(-222.63,125.97,17.15,0.23)}
+  {pt3dadd(-218.82,128.68,17.15,0.23)}
+  {pt3dadd(-217.73,132.57,14.88,0.23)}
+  {pt3dadd(-216,135.91,14.88,0.23)}
+  {pt3dadd(-212.46,140.46,13.29,0.23)}
+  {pt3dadd(-210.21,145.38,13.29,0.23)}
+  {pt3dadd(-208.93,148.6,13.29,0.23)}
+  {pt3dadd(-204.07,155.2,14.42,0.23)}
+  {pt3dadd(-203.74,155.77,14.42,0.23)}
+  {pt3dadd(-202.47,156.35,14.42,0.23)}
+  {pt3dadd(-201.98,157.2,14.42,0.23)}
+  {pt3dadd(-201.94,157.93,14.42,0.23)}
+  {pt3dadd(-201.9,158.66,14.42,0.23)}
+  {pt3dadd(-201.8,159.17,14.42,0.23)}
+  {pt3dadd(-201.69,159.67,14.42,0.23)}
+  {pt3dadd(-201.81,160.12,14.42,0.23)}
+  {pt3dadd(-201.71,160.62,14.42,0.23)}
+  {pt3dadd(-201.61,161.13,14.42,0.23)}
+  {pt3dadd(-201.45,161.41,14.42,0.23)}
+  {pt3dadd(-201.13,161.98,14.42,0.23)}
+  {pt3dadd(-200.96,162.26,14.42,0.23)}
+  {pt3dadd(-200.29,162.45,14.42,0.23)}
+  {pt3dadd(-199.91,162.79,14.42,0.23)}
+  {pt3dadd(-199.24,162.97,14.42,0.23)}
+  {pt3dadd(-198.63,163.38,14.42,0.23)}
+  {pt3dadd(-197.64,164.13,14.42,0.23)}
+  {pt3dadd(-196.81,164.59,14.42,0.23)}
+  {pt3dadd(-196.36,164.71,14.42,0.23)}
+  {pt3dadd(-196.2,165,14.42,0.23)}
+  {pt3dadd(-196.04,165.28,14.42,0.23)}
+
+  {dendrite[63] connect dendrite[64](0), 1}
+  {access dendrite[64]}
+  {pt3dclear()}
+  {pt3dadd(-196.04,165.28,14.42,0.23)}
+  {pt3dadd(-195.47,167.59,14.42,0.23)}
+  {pt3dadd(-194.98,168.44,14.42,0.23)}
+  {pt3dadd(-194.76,168.5,14.42,0.23)}
+
+  {dendrite[63] connect dendrite[65](0), 1}
+  {access dendrite[65]}
+  {pt3dclear()}
+  {pt3dadd(-196.04,165.28,14.42,0.23)}
+  {pt3dadd(-192.45,167.93,14.42,0.23)}
+  {pt3dadd(-192.76,169.04,14.42,0.23)}
+  {pt3dadd(-192.82,169.27,14.42,0.23)}
+}
+endtemplate E21
diff --git a/netpyne/tutorials/cells/izhi2003Wrapper.py b/netpyne/tutorials/cells/izhi2003Wrapper.py
new file mode 100644
index 000000000..ae489501f
--- /dev/null
+++ b/netpyne/tutorials/cells/izhi2003Wrapper.py
@@ -0,0 +1,71 @@
+"""
+IZHI
+
+Python wrappers for the different celltypes of Izhikevich neuron.
+Equations and parameter values taken from
+  Izhikevich EM (2007). "Dynamical systems in neuroscience", MIT Press
+Equation for synaptic inputs taken from
+  Izhikevich EM, Edelman GM (2008). "Large-scale model of mammalian thalamocortical systems." PNAS 105(9) 3593-3598.
+
+Cell types available are based on Izhikevich, 2007 book:
+    1. RS - Layer 5 regular spiking pyramidal cell (fig 8.12 from 2007 book)
+    2. IB - Layer 5 intrinsically bursting cell (fig 8.19 from 2007 book)
+    3. CH - Cat primary visual cortex chattering cell (fig8.23 from 2007 book)
+    4. LTS - Rat barrel cortex Low-threshold  spiking interneuron (fig8.25 from 2007 book)
+    5. FS - Rat visual cortex layer 5 fast-spiking interneuron (fig8.27 from 2007 book)
+    6. TC - Cat dorsal LGN thalamocortical (TC) cell (fig8.31 from 2007 book)
+    7. RTN - Rat reticular thalamic nucleus (RTN) cell  (fig8.32 from 2007 book)
+"""
+
+import collections
+from neuron import h
+dummy = h.Section()
+type2003 = collections.OrderedDict([
+  #                                 a         b     c         d
+ ('tonic spiking'               , (0.02   ,  0.2 , -65.0 ,   6.0)) ,
+ ('mixed mode'                  , (0.02   ,  0.2 , -55.0 ,   4.0)) ,
+ ('spike latency'               , (0.02   ,  0.2 , -65.0 ,   6.0)) ,
+ ('rebound spike'               , (0.03   , 0.25 , -60.0 ,   4.0)) ,
+ ('Depolarizing afterpotential' , (1.0    ,  0.2 , -60.0 , -21.0)) ,
+ ('phasic spiking'              , (0.02   , 0.25 , -65.0 ,   6.0)) ,
+ ('spike frequency adaptation'  , (0.01   ,  0.2 , -65.0 ,   8.0)) ,
+ ('subthreshold oscillations'   , (0.05   , 0.26 , -60.0 ,   0.0)) ,
+ ('rebound burst'               , (0.03   , 0.25 , -52.0 ,   0.0)) ,
+ ('accomodation'                , (0.02   ,  1.0 , -55.0 ,   4.0)) ,
+ ('tonic bursting'              , (0.02   ,  0.2 , -50.0 ,   2.0)) ,
+ ('Class 1'                     , (0.02   , -0.1 , -55.0 ,   6.0)) ,
+ ('resonator'                   , (0.1    , 0.26 , -60.0 ,  -1.0)) ,
+ ('threshold variability'       , (0.03   , 0.25 , -60.0 ,   4.0)) ,
+ ('inhibition-induced spiking'  , (-0.02  , -1.0 , -60.0 ,   8.0)) ,
+ ('phasic bursting'             , (0.02   , 0.25 , -55.0 ,  0.05)) ,
+ ('Class 2'                     , (0.2    , 0.26 , -65.0 ,   0.0)) ,
+ ('integrator'                  , (0.02   , -0.1 , -55.0 ,   6.0)) ,
+ ('bistability'                 , (0.1    , 0.26 , -60.0 ,   0.0)) ,
+ ('inhibition-induced bursting' , (-0.026 , -1.0 , -45.0 ,  -2.0))])
+
+# class of basic Izhikevich neuron based on parameters in type2003
+class IzhiCell ():
+  '''Create an izhikevich cell based on 2007 parameterization using either izhi2007.mod (no hosting section) or izhi2007b.mod (v in created section)
+  If host is omitted or None, this will be a section-based version that uses Izhi2007b with state vars v, u where v is the section voltage
+  If host is given then this will be a shared unused section that simply houses an Izhi2007 using state vars V and u
+  Note: Capacitance 'C' differs from sec.cm which will be 1; vr is RMP; vt is threshold; vpeak is peak voltage
+'''
+
+  def __init__ (self, type='tonic spiking', host=None, cellid=-1):
+    self.type=type
+    if host is None:  # need to set up a sec for this
+      self.sec=h.Section(name='izhi2003'+type+str(cellid))
+      self.sec.L, self.sec.diam = 6.3, 5 # empirically tuned
+      self.izh = h.Izhi2003b(0.5, sec=self.sec)
+    else:
+      self.sec = dummy
+      self.izh = h.Izhi2003a(0.5, sec=self.sec) # Create a new u,V 2007 neuron at location 0.5 (doesn't matter where)
+
+    self.izh.a,self.izh.b,self.izh.c,self.izh.d = type2003[type]
+    self.izh.Iin = 0
+
+  def init (self): self.sec(0.5).v = self.vinit
+
+  def reparam (self, type='tonic spiking', cellid=-1):
+    self.type=type
+    self.izh.a,self.izh.b,self.izh.c,self.izh.d = type2003[type]
diff --git a/netpyne/tutorials/cells/izhi2007Wrapper.py b/netpyne/tutorials/cells/izhi2007Wrapper.py
new file mode 100644
index 000000000..61678649b
--- /dev/null
+++ b/netpyne/tutorials/cells/izhi2007Wrapper.py
@@ -0,0 +1,62 @@
+"""
+IZHI
+
+Python wrappers for the different celltypes of Izhikevich neuron.
+Equations and parameter values taken from
+  Izhikevich EM (2007). "Dynamical systems in neuroscience", MIT Press
+Equation for synaptic inputs taken from
+  Izhikevich EM, Edelman GM (2008). "Large-scale model of mammalian thalamocortical systems." PNAS 105(9) 3593-3598.
+
+Cell types available are based on Izhikevich, 2007 book:
+    1. RS - Layer 5 regular spiking pyramidal cell (fig 8.12 from 2007 book)
+    2. IB - Layer 5 intrinsically bursting cell (fig 8.19 from 2007 book)
+    3. CH - Cat primary visual cortex chattering cell (fig8.23 from 2007 book)
+    4. LTS - Rat barrel cortex Low-threshold  spiking interneuron (fig8.25 from 2007 book)
+    5. FS - Rat visual cortex layer 5 fast-spiking interneuron (fig8.27 from 2007 book)
+    6. TC - Cat dorsal LGN thalamocortical (TC) cell (fig8.31 from 2007 book)
+    7. RTN - Rat reticular thalamic nucleus (RTN) cell  (fig8.32 from 2007 book)
+"""
+
+import collections
+from neuron import h
+dummy = h.Section()
+type2007 = collections.OrderedDict([
+  #              C    k     vr  vt vpeak   a      b   c    d  celltype
+  ('RS',        (1, 0.7,  -60, -40, 35, 0.03,   -2, -50,  100,  1)),
+  ('IB',        (1.5, 1.2,  -75, -45, 50, 0.01,   5, -56,  130,   2)),
+  ('CH',        (0.5,  1.5,  -60, -40, 25, 0.03,   1, -40,  150,   3)),
+  ('LTS',       (1, 1.0,  -56, -42, 40, 0.03,   8, -53,   20,   4)),
+  ('FS',        (0.2,  1.0,  -55, -40, 25, 0.2,   -2, -45,  -55,   5)),
+  ('TC',        (2.0, 1.6,  -60, -50, 35, 0.01,  15, -60,   10,   6)),
+  ('TC_burst',  (2.0, 1.6,  -60, -50, 35, 0.01,  15, -60,   10,   6)),
+  ('RTN',       (0.4,  0.25, -65, -45,  0, 0.015, 10, -55,   50,   7)),
+  ('RTN_burst', (0.4,  0.25, -65, -45,  0, 0.015, 10, -55,   50,   7))])
+
+# class of basic Izhikevich neuron based on parameters in type2007
+class IzhiCell ():
+  '''Create an izhikevich cell based on 2007 parameterization using either izhi2007.mod (no hosting section) or izhi2007b.mod (v in created section)
+  If host is omitted or None, this will be a section-based version that uses Izhi2007b with state vars v, u where v is the section voltage
+  If host is given then this will be a shared unused section that simply houses an Izhi2007 using state vars V and u
+  Note: Capacitance 'C' differs from sec.cm which will be 1; vr is RMP; vt is threshold; vpeak is peak voltage
+'''
+
+  def __init__ (self, type='RS', host=None, cellid=-1):
+    self.type=type
+    if host is None:  # need to set up a sec for this
+      self.sec=h.Section(name='izhi2007'+type+str(cellid))
+      self.sec.L, self.sec.diam, self.sec.cm = 10, 10, 31.831 # empirically tuned
+      self.izh = h.Izhi2007b(0.5, sec=self.sec)
+      self.vinit = -60
+    else:
+      self.sec = dummy
+      self.izh = h.Izhi2007a(0.5, sec=self.sec) # Create a new u,V 2007 neuron at location 0.5 (doesn't matter where)
+
+    self.izh.C,self.izh.k,self.izh.vr,self.izh.vt,self.izh.vpeak,self.izh.a,self.izh.b,self.izh.c,self.izh.d,self.izh.celltype = type2007[type]
+    self.izh.cellid   = cellid # Cell ID for keeping track which cell this is
+
+  def init (self): self.sec(0.5).v = self.vinit
+
+  def reparam (self, type='RS', cellid=-1):
+    self.type=type
+    self.izh.C,self.izh.k,self.izh.vr,self.izh.vt,self.izh.vpeak,self.izh.a,self.izh.b,self.izh.c,self.izh.d,self.izh.celltype = type2007[type]
+    self.izh.cellid = cellid # Cell ID for keeping track which cell this is
diff --git a/netpyne/tutorials/cells/mainen.py b/netpyne/tutorials/cells/mainen.py
new file mode 100644
index 000000000..80fd05e07
--- /dev/null
+++ b/netpyne/tutorials/cells/mainen.py
@@ -0,0 +1,82 @@
+# translated from /u/samn/npredict/geom_mainen.hoc // $Id: geom_mainen.hoc,v 1.3 2007/04/16 14:42:33 samn Exp $
+from neuron import h
+from math import pi
+
+class PYR2:
+  def __init__ (self,ID=0,ty=0,col=0,rho=165.0,kappa=10.0,soma_pas=False):
+    self.ID=ID
+    self.ty=ty
+    self.col=col
+    self.soma_pas = soma_pas
+    self.soma = soma = h.Section(name='soma',cell=self)
+    self.dend = dend = h.Section(name='dend',cell=self)
+    self.dend.connect(self.soma,0.5,0) #   connect dend(0), soma(0.5)
+    self.rho = rho # dendritic to axo-somatic area ratio
+    self.kappa = kappa # coupling resistance (Mohm)
+    for sec in [self.soma,self.dend]:
+      sec.insert('k_ion')
+      sec.insert('na_ion')
+      sec.insert('ca_ion')
+      sec.ek = -90 # K+ current reversal potential (mV)
+      sec.ena = 60 # Na+ current reversal potential (mV)
+      sec.eca = 140 # Ca2+ current reversal potential (mV)
+      h.ion_style("ca_ion",0,1,0,0,0) # using an ohmic current rather than GHK equation
+      sec.Ra=100
+    self.initsoma()
+    self.initdend()
+
+  def initsoma (self):
+    soma = self.soma
+    soma.nseg = 1
+    soma.diam = 10.0/pi
+    soma.L = 10
+    soma.cm = 0.75
+    soma.insert('naz') # naz.mod
+    soma.insert('kv') # kv.mod
+    soma.gmax_naz = 30e3
+    soma.gmax_kv = 1.5e3
+    if self.soma_pas:
+      soma.insert('pas')
+      soma.e_pas=-70
+      soma.g_pas=1/3e4
+
+  def initdend (self):
+    dend = self.dend
+    dend.nseg = 1
+    dend.diam = 10.0/pi
+    self.config()
+    dend.cm = 0.75
+    dend.insert('naz') # naz.mod
+    dend.insert('km') # km.mod
+    dend.insert('kca') # kca.mod
+    dend.insert('Nca') # Nca.mod
+    dend.insert('cadad') # cadad.mod
+    dend.insert('pas')
+    dend.eca=140
+    h.ion_style("ca_ion",0,1,0,0,0) # already called before
+    dend.e_pas = -70 # only dendrite has leak conductance - why?
+    dend.g_pas = 1/3e4 # only dendrite has leak conductance
+    dend.gmax_naz=15
+    dend.gmax_Nca = 0.3 # high voltage-activated Ca^2+
+    dend.gmax_km = 0.1 # slow voltage-dependent non-inactivating K+
+    dend.gmax_kca = 3  # slow Ca^2+-activated K+
+
+  def config (self):
+    self.dend. L  = self.rho*self.soma.L # dend area is axon area multiplied by rho
+    self.dend.Ra = self.dend.Ra*self.kappa/self.dend(0.5).ri() # axial resistivity is adjusted to achieve
+
+  # resets cell to default values
+  def todefault(self):
+    self.rho = 165
+    self.kappa = 10
+    self.config()
+    self.soma.gmax_naz = 30e3
+    self.soma.gmax_kv = 1.5e3
+    self.dend.g_pas = 1/3e4
+    self.dend.gmax_naz = 15
+    self.dend.gmax_Nca = 0.3
+    self.dend.gmax_km = 0.1
+    self.dend.gmax_kca = 3
+    self.soma.ek = dend.ek = -90
+    self.soma.ena = dend.ena = 60
+    self.soma.eca = dend.eca = 140
diff --git a/netpyne/tutorials/cells/pyr3_traub.hoc b/netpyne/tutorials/cells/pyr3_traub.hoc
new file mode 100755
index 000000000..6c44cb93e
--- /dev/null
+++ b/netpyne/tutorials/cells/pyr3_traub.hoc
@@ -0,0 +1,605 @@
+/*
+
+This port was made from the FORTRAN code into the NEURON enviroment based on 
+
+	Traub RD, Buhl EH, Gloveli T, Whittington MA. Fast Rhythmic Bursting Can Be Induced in Layer 2/3 Cortical Neurons by Enhancing Persistent Na(+) Conductance or by Blocking BK Channels.J Neurophysiol. 2003 Feb;89(2):909-21.
+
+This port was made by Roger D Traub and Maciej Lazarewicz (mlazarew@seas.upenn.edu)
+
+Thanks to Ashlen P Reid for help with porting a morphology of the cell.
+
+*/
+
+
+begintemplate pyr3
+
+	public comp, Level1, Level2, Level3,  Level4, Level5, Level6, Level7, Level8, Level9,  Level10, Level11, Level12, Dendrites, Basal, Oblique, Prox, Dist, SD, Soma, inj1_, inj2_
+
+	create  comp[75]
+	create  aux10to13[4], aux69, aux38, aux2to9[8]
+
+	objref all, Dist, Oblique, Basal, Soma, Axon, Dendrites, SD, Prox
+	objref Aux
+	objref Level0, Level1, Level2, Level3, Level4, Level5, Level6, Level7, Level8, Level9, Level10, Level11, Level12
+	objref inj1_, inj2_
+
+	proc init() {
+
+		//titlePrint()
+
+		create  comp[75]
+		create  aux10to13[4], aux69, aux38, aux2to9[8]
+
+		comp[0] delete_section()		
+
+		objref all, Dist, Oblique, Basal, Soma, Axon, Dendrites, SD, Prox
+		objref  Aux
+		objref Level0, Level1, Level2, Level3, Level4, Level5, Level6, Level7, Level8, Level9, Level10, Level11, Level12
+		objref inj1_, inj2_
+
+		shape()
+
+		geom()
+	
+
+		//if( name_declared("method") != 5 ) method = 1
+
+		//if( method == 1 ) spinecorr()
+
+		//setupfig( $1 )
+
+		set_active()
+
+		//if( method == 2 ) spinecorr()
+
+	}
+
+	proc titlePrint() {
+
+		print ""
+		print "-----"
+		print ""
+		print "Layer 2/3 Cortical Neurons Model based on Traub RD (2003)"
+		print ""
+		print "-----"
+	}
+
+	// 21 - figure 2, current injection 1.5 nA
+	// 22 - figure 2, current injection 2.5 nA
+	// 41 - figure 4, current injection 2.5 nA
+	// 42 - figure 4, current injection 1.5 nA
+	// 43 - figure 4, current injection 1.1 nA
+	// 44 - figure 4, current injection 0.6 nA
+	// 51 - figure 5, current injection 0.75 nA
+	// 52 - figure 5, current injection 1.3 nA
+	// 61 - figure 6, current injection 0.6 nA
+	// 62 - figure 6, current injection 1.3 nA
+	// 71 - figure 7, uper
+	// 72 - figure 7, middle
+	// 73 - figure 7, bottom
+
+	proc setupfig() {
+
+		fig = $1
+		if ( fig == -1 ) comp[1] {
+			dnap   = 0.0   
+			dkc    = 1.6	
+		}
+
+		if ( fig == -2 ) comp[1] {
+			dnap    = 1.25  
+			dkc     = 1.0
+		}
+
+		if ( fig == 21 || fig == 22 ) comp[43] {
+			g_pas     = g_pas + 2.5/area(0.5)   // in the paper is 20 nS, here is 25 nS
+			inj1_     = new IClamp(0.5)
+			inj1_.dur = 150
+			inj1_.del = 0
+			inj1_.amp = -0.15	
+
+			inj2_     = new IClamp(0.5)
+			inj2_.dur = 800
+			inj2_.del = 150
+
+			if( fig == 21 )	inj2_.amp = 1.5
+			if( fig == 22 )	inj2_.amp = 2.5
+
+			dnap   = 0.0
+			dkc    = 1.6
+		}
+
+
+		if ( fig == 41 ||  fig == 42 || fig == 43 || fig == 44 ) comp[1] {
+			inj1_     = new IClamp(0.5)
+			inj1_.dur = 150
+			inj1_.del = 0
+			inj1_.amp = -0.15	
+
+			inj2_     = new IClamp(0.5)
+			inj2_.dur = 250
+			inj2_.del = 150
+
+			if( fig == 41 )	inj2_.amp = 2.5
+			if( fig == 42 )	inj2_.amp = 1.5
+			if( fig == 43 )	inj2_.amp = 1.1
+			if( fig == 44 )	inj2_.amp = 0.6
+
+			dnap   = 0.0
+			dkc    = 1.3
+		}
+
+		if ( fig == 51 || fig == 52 ) comp[1] {
+			inj1_     = new IClamp(0.5)
+			inj1_.dur = 150
+			inj1_.del = 0
+			inj1_.amp = -0.15	
+
+			inj2_     = new IClamp(0.5)
+			inj2_.dur = 1000
+			inj2_.del = 150
+
+			if( fig == 51 )	inj2_.amp = 0.75
+			if( fig == 52 )	inj2_.amp = 1.3
+
+			dnap = 0
+			dkc  = 1.6
+		}
+
+		if ( fig == 61 || fig == 62 ) comp[1] {
+			inj1_     = new IClamp(0.5)
+			inj1_.dur = 150
+			inj1_.del = 0
+			inj1_.amp = -0.15	
+
+			inj2_     = new IClamp(0.5)
+			inj2_.dur = 2500
+			inj2_.del = 150
+
+			if( fig == 61 )	inj2_.amp = 0.6
+			if( fig == 62 )	inj2_.amp = 1.3
+
+			dnap   = 0.7
+			dkc    = 1.6
+		}
+
+		if ( fig == 71 || fig == 72 || fig == 73 ) comp[1] {
+			inj1_     = new IClamp(0.5)
+			inj1_.dur = 150
+			inj1_.del = 0
+			inj1_.amp = -0.15	
+
+			inj2_     = new IClamp(0.5)
+			inj2_.dur = 2500
+			inj2_.del = 150
+			inj2_.amp = 0.7
+
+			if( fig == 71 )	dnap = 0
+			if( fig == 72 )	dnap = 0.7
+			if( fig == 73 )	dnap = 1.0
+
+			dkc    = 1.6
+		}
+	}
+
+	proc spinecorr() {
+
+		forsec Dendrites {
+			if (method == 1) {
+				L  = L * 2
+				Ra = Ra / 2
+			}
+
+			if (method == 2) {
+				g_pas	  = g_pas     * 2 
+				cm   	  = cm        * 2
+				phi_cad   = phi_cad   / 2
+
+				gbar_naf  = gbar_naf  * 2
+				gbar_nap  = gbar_nap  * 2
+				gbar_kdr  = gbar_kdr  * 2
+				gbar_ka   = gbar_ka   * 2
+				gbar_kc   = gbar_kc   * 2
+				gbar_kahp = gbar_kahp * 2
+				gbar_k2   = gbar_k2   * 2
+				gbar_km2   = gbar_km2   * 2
+				gbar_cat  = gbar_cat  * 2
+				gbar_cal  = gbar_cal  * 2
+				gbar_ar   = gbar_ar   * 2
+			}
+		}
+
+		print "Spine correction with method: ", method
+	}
+
+	proc set_active() {
+
+		forsec Dendrites {
+			insert cad
+			insert naf 
+			insert nap 
+			insert kdr 
+			insert ka 
+			insert kc
+			insert kahp 
+			insert k2 
+			insert km2 
+			insert cat 
+			insert cal 
+			insert ar
+		}
+
+		forsec Soma {
+			insert cad
+			insert naf 
+			insert nap 
+			insert kdr 
+			insert ka 
+			insert kc
+			insert kahp 
+			insert k2 
+			insert km2 
+			insert cat 
+			insert cal 
+			insert ar
+		}
+
+		forsec Axon {
+			insert naf 
+			insert kdr 
+			insert ka 
+			insert k2 
+
+			gbar_naf  = 400e-3 
+			gbar_kdr  = 400e-3
+			gbar_ka   = 2e-3
+			gbar_k2   = 0.1e-3
+		}
+		
+		comp[1] ceiling_cad = 1000
+
+		forsec Soma {
+			phi_cad   = 52 / 2e-3
+			beta_cad  = 1 / 100	// in the paper beta = 50 [ms]
+
+			gbar_naf  = 150e-3 * 1.25
+			gbar_nap  = dnap * 0.0032 * gbar_naf 
+			gbar_kdr  = 125e-3
+			gbar_ka   = 30e-3
+			gbar_kc   = dkc * 7.5e-3 // in tha paper 'dkc * 12e-3'
+			gbar_kahp = 0.1e-3
+			gbar_k2   = 0.1e-3
+			gbar_km2   = 2.5 * 1.5e-3 * 2
+			gbar_cat  = 0.1e-3
+			gbar_cal  = 0.5e-3
+			gbar_ar   = 0.25e-3
+		}
+
+		forsec Dendrites {
+			phi_cad   = 52 / 2e-3
+			beta_cad  = 1 / 20
+
+			gbar_naf  = 6.25e-3 
+			gbar_nap  = dnap * 0.0032 * gbar_naf 
+			gbar_kdr  = 0
+			gbar_ka   = 2e-3
+			gbar_kc   = 0
+			gbar_kahp = 0.1e-3
+			gbar_k2   = 0.1e-3
+			gbar_km2   = 2.5 * 1.5e-3 * 2
+			gbar_cat  = 0.1e-3
+			gbar_cal  = 0.5e-3
+			gbar_ar   = 0.25e-3
+		}
+
+		forsec Prox {	
+			gbar_naf  = 75e-3 * 1.25
+			gbar_nap  = dnap * 0.0032 * gbar_naf 
+			gbar_kdr  = 75e-3 * 1.25
+			gbar_kc   = dkc * 7.5e-3 // in tha paper 'dkc * 12e-3'
+		}
+
+		forsec Dist {
+			gbar_cal  = 3e-3
+		}
+
+		comp[38] {
+			gbar_ka   = 30e-3
+			gbar_naf  = 125e-3 
+			gbar_nap  = dnap * 0.0032 * gbar_naf // in the FORTRAN code 0.004
+			gbar_kdr  = 125e-3       // in tha paper '75e-3 * 1.25'
+			gbar_kc   = dkc * 7.5e-3 // in tha paper 'dkc * 12e-3'
+		}
+
+		forsec Axon {
+			ena = 50
+			ek  = -95
+		}
+
+		forsec Dendrites {	
+			ena = 50
+			ek  = -95
+			eca = 125
+		}
+
+		forsec Soma {	
+			ena = 50
+			ek  = -95
+			eca = 125
+		}
+
+	}
+
+	proc geom() {
+
+		//Apical
+
+		for i=61,68 connect comp[i](0),      comp[i-8](1)
+		for i=53,60 connect comp[i](0),      comp[i-8](1)
+		for i=49,52 connect comp[i](0),      comp[44](1)
+		for i=45,48 connect comp[i](0),      comp[43](1)
+		for i=43,44 connect comp[i](0),      comp[i-2](1)
+		for i=41,42 connect comp[i](0),      comp[40](1)
+
+		connect             comp[40](0),     comp[39](1)
+		connect 	    comp[39](0),     comp[38](1)
+//		connect 	    comp[38](0),     comp[1](1)
+		connect 	    comp[38](0),     aux38(1)
+		connect		    aux38(0),        comp[1](0.5)
+
+		// Oblique apical
+
+		for i=0,3 connect   aux10to13[i](0), comp[38](0.5)
+		for i=0,3 connect   comp[i+10](0),   aux10to13[i](1)
+//		for i=0,3 connect   comp[i+10](0),   comp[38](0.5)
+		for i=0,3 connect   comp[i+22](0),   comp[i+10](1)
+		for i=0,3 connect   comp[i+34](0),   comp[i+22](1)
+
+		// Basal
+
+		for i=0,7 connect   aux2to9[i](0),   comp[1](0.5)
+		for i=0,7 connect   comp[i+2](0),    aux2to9[i](1)
+//		for i=0,7 connect   comp[i+2](0),    comp[1](0)
+		for i=0,7 connect   comp[i+14](0),   comp[i+2](1)
+		for i=0,7 connect   comp[i+26](0),   comp[i+14](1)
+
+		// Axon
+		connect             aux69(0),        comp[1](0.5)
+		connect             comp[69](0),     aux69(1)
+//		connect             comp[69](0),     comp[1](0.5)
+		connect             comp[70](0),     comp[69](1)
+		for i=0,1 connect   comp[71+i*2](0), comp[70](1)
+		for i=0,1 connect   comp[72+i*2](0), comp[71+i*2](1)
+
+		Level0 = new SectionList()
+		 for i=69,74 comp[i] Level0.append()    
+
+		Level1 = new SectionList()
+		comp[1] Level1.append()    
+
+		Level2 = new SectionList()
+		 for i=2,13 comp[i] Level2.append()    
+
+		Level3 = new SectionList()
+		 for i=14,25 comp[i] Level3.append()
+
+		Level4 = new SectionList()
+		 for i=26,37 comp[i] Level4.append()
+
+		Level5 = new SectionList()
+		 comp[38] Level5.append()
+
+		Level6 = new SectionList()
+		 comp[39] Level6.append()
+
+		Level7 = new SectionList()
+		 comp[40] Level7.append()
+
+		Level8 = new SectionList()
+		 for i=41,42 comp[i] Level8.append()
+
+		Level9 = new SectionList()
+		 for i=43,44 comp[i] Level9.append()
+
+		Level10 = new SectionList()
+		 for i=45,52 comp[i] Level10.append()
+
+		Level11 = new SectionList()
+		 for i=53,60 comp[i] Level11.append()
+
+		Level12 = new SectionList()
+		 for i=60,68 comp[i] Level12.append()
+
+		all = new SectionList()
+		 for i=1,74 comp[i] all.append()
+
+		Axon = new SectionList()
+		 for i=69,74 comp[i] Axon.append()
+
+		Dendrites = new SectionList()
+		 for i=2,68 comp[i] Dendrites.append()
+
+		SD = new SectionList()
+		 for i=1,68 comp[i] SD.append()
+
+		Dist = new SectionList()
+		 forsec Level10 Dist.append()
+		 forsec Level11 Dist.append()
+		 forsec Level12 Dist.append()
+
+		Basal = new SectionList()
+		 for i=2,9 comp[i] Basal.append()
+		 for i=14,21 comp[i] Basal.append()
+		 for i=26,33 comp[i] Basal.append()
+
+		Oblique = new SectionList()
+		 for i=10,13 comp[i] Oblique.append()
+		 for i=22,25 comp[i] Oblique.append()
+		 for i=34,37 comp[i] Oblique.append()
+
+		Prox = new SectionList()
+		 forsec Level2 Prox.append()
+		 forsec Level6 Prox.append()
+
+		Soma = new SectionList()
+		 comp[1] Soma.append()
+
+		Aux = new SectionList()
+		 for i=0,3 aux10to13[i] Aux.append()
+		 aux69 Aux.append()
+		 aux38 Aux.append()
+		 for i=0,7 aux2to9[i] Aux.append()
+
+		forsec Dist 		{  		diam = 1.6  	}
+		forsec Oblique 		{  		diam = 1  	}
+		forsec Basal 		{  		diam = 1  	}
+		forsec Soma 		{  L = 15  	diam = 16  	}
+		forsec Dendrites 	{  L = 50  			}
+		forsec Aux 	 	{  L = 15 / 2  	diam = 16 	}
+		for i=0,3 aux10to13[i]	{  L = 50 / 2  	diam = 8 	}
+
+		comp[38]    		{ 		diam = 8  	}
+		comp[39] 		{  		diam = 8 * 0.9 	}
+		comp[40] 		{  		diam = 8 * 0.8 	}
+		forsec Level8 		{  		diam = 4  	}
+		forsec Level9 		{  		diam = 4  	}
+
+		comp[69]    		{  L = 25  	diam = 1.8 	}
+		comp[70]    		{  L = 50  	diam = 1.4 	}
+		for i=71,74 comp[i] 	{  L = 50  	diam = 1  	}
+
+		forsec Aux {
+			Ra = 250
+			cm = 0
+		}	
+
+		forsec Soma {
+		  Ra = 250
+		  cm = 0.9
+		  insert pas
+		    g_pas = 2e-05
+		    e_pas = -70
+		}
+
+		forsec Axon {
+		  Ra = 100
+		  cm = 0.9
+		  insert pas
+		    g_pas = 0.001
+		    e_pas = -70
+		}
+
+		forsec Dendrites {
+		  Ra = 250
+		  cm = 0.9
+		  insert pas
+		    g_pas = 2e-05
+		    e_pas = -70
+		}
+
+		access comp[1]
+	}
+
+	proc shape() {
+
+		aux10to13[0] 	{pt3dclear() pt3dadd(-134, -14, 0, 1) pt3dadd(-104, -14, 0, 1)}
+		aux10to13[1] 	{pt3dclear() pt3dadd(-134, -14, 0, 1) pt3dadd(-104, -14, 0, 1)}
+		aux10to13[2] 	{pt3dclear() pt3dadd(-134, -14, 0, 1) pt3dadd(-104, -14, 0, 1)}
+		aux10to13[3] 	{pt3dclear() pt3dadd(-134, -14, 0, 1) pt3dadd(-104, -14, 0, 1)}
+		aux69		{pt3dclear() pt3dadd(-149, -14, 0, 1) pt3dadd(-134, -14, 0, 1)}
+		aux38		{pt3dclear() pt3dadd(-149, -14, 0, 1) pt3dadd(-134, -14, 0, 1)}
+		aux2to9[0]	{pt3dclear() pt3dadd(-149, -14, 0, 1) pt3dadd(-134, -14, 0, 1)}
+		aux2to9[1]	{pt3dclear() pt3dadd(-149, -14, 0, 1) pt3dadd(-134, -14, 0, 1)}
+		aux2to9[2]	{pt3dclear() pt3dadd(-149, -14, 0, 1) pt3dadd(-134, -14, 0, 1)}
+		aux2to9[3]	{pt3dclear() pt3dadd(-149, -14, 0, 1) pt3dadd(-134, -14, 0, 1)}
+		aux2to9[4]	{pt3dclear() pt3dadd(-149, -14, 0, 1) pt3dadd(-134, -14, 0, 1)}
+		aux2to9[5]	{pt3dclear() pt3dadd(-149, -14, 0, 1) pt3dadd(-134, -14, 0, 1)}
+		aux2to9[6]	{pt3dclear() pt3dadd(-149, -14, 0, 1) pt3dadd(-134, -14, 0, 1)}
+		aux2to9[7]	{pt3dclear() pt3dadd(-149, -14, 0, 1) pt3dadd(-134, -14, 0, 1)}
+
+		comp[1] {pt3dclear() pt3dadd(-149, -14, 0, 1) pt3dadd(-134, -14, 0, 1)}
+		comp[38] {pt3dclear() pt3dadd(-134, -14, 0, 1) pt3dadd(-104, -14, 0, 1)}
+		comp[39] {pt3dclear() pt3dadd(-104, -14, 0, 1) pt3dadd(-74, -14, 0, 1)}
+		comp[40] {pt3dclear() pt3dadd(-74, -14, 0, 1) pt3dadd(-44, -14, 0, 1)}
+
+		comp[41] {pt3dclear() pt3dadd(-44, -14, 0, 1) pt3dadd(-14, 30, 0, 1)}
+		comp[43] {pt3dclear() pt3dadd(-14, 30, 0, 1) pt3dadd(0, 45, 0, 1)}
+
+		comp[45] {pt3dclear() pt3dadd(0, 45, 0, 1) pt3dadd(45, 75, 0, 1)}
+		comp[46] {pt3dclear() pt3dadd(0, 45, 0, 1) pt3dadd(45, 60, 0, 1)}
+		comp[47] {pt3dclear() pt3dadd(0, 45, 0, 1) pt3dadd(45, 30, 0, 1)}
+		comp[48] {pt3dclear() pt3dadd(0, 45, 0, 1) pt3dadd(45, 15, 0, 1)}
+
+		comp[53] {pt3dclear() pt3dadd(45, 75, 0, 1) pt3dadd(75, 75, 0, 1)}
+		comp[54] {pt3dclear() pt3dadd(45, 60, 0, 1) pt3dadd(75, 60, 0, 1)}
+		comp[55] {pt3dclear() pt3dadd(45, 30, 0, 1) pt3dadd(75, 30, 0, 1)}
+		comp[56] {pt3dclear() pt3dadd(45, 15, 0, 1) pt3dadd(75, 15, 0, 1)}
+
+		comp[61] {pt3dclear() pt3dadd(75, 75, 0, 1) pt3dadd(90, 90, 0, 1)}
+		comp[62] {pt3dclear() pt3dadd(75, 60, 0, 1) pt3dadd(90, 75, 0, 1)}
+		comp[63] {pt3dclear() pt3dadd(75, 30, 0, 1) pt3dadd(90, 15, 0, 1)}
+		comp[64] {pt3dclear() pt3dadd(75, 15, 0, 1) pt3dadd(90, 0, 0, 1)}
+
+		comp[42] {pt3dclear() pt3dadd(-44, -14, 0, 1) pt3dadd(-14, -59, 0, 1)}
+		comp[44] {pt3dclear() pt3dadd(-14, -59, 0, 1) pt3dadd(0, -74, 0, 1)}
+
+		comp[49] {pt3dclear() pt3dadd(0, -74, 0, 1) pt3dadd(45, -44, 0, 1)}
+		comp[50] {pt3dclear() pt3dadd(0, -74, 0, 1) pt3dadd(45, -59, 0, 1)}
+		comp[51] {pt3dclear() pt3dadd(0, -74, 0, 1) pt3dadd(45, -89, 0, 1)}
+		comp[52] {pt3dclear() pt3dadd(0, -74, 0, 1) pt3dadd(45, -104, 0, 1)}
+
+		comp[57] {pt3dclear() pt3dadd(45, -44, 0, 1) pt3dadd(75, -44, 0, 1)}
+		comp[58] {pt3dclear() pt3dadd(45, -59, 0, 1) pt3dadd(75, -59, 0, 1)}
+		comp[59] {pt3dclear() pt3dadd(45, -89, 0, 1) pt3dadd(75, -89, 0, 1)}
+		comp[60] {pt3dclear() pt3dadd(45, -104, 0, 1) pt3dadd(75, -104, 0, 1)}
+
+		comp[65] {pt3dclear() pt3dadd(75, -44, 0, 1) pt3dadd(90, -29, 0, 1)}
+		comp[66] {pt3dclear() pt3dadd(75, -59, 0, 1) pt3dadd(90, -44, 0, 1)}
+		comp[67] {pt3dclear() pt3dadd(75, -89, 0, 1) pt3dadd(90, -104, 0, 1)}
+		comp[68] {pt3dclear() pt3dadd(75, -104, 0, 1) pt3dadd(90, -119, 0, 1)}
+
+		comp[10] {pt3dclear() pt3dadd(-104, -14, 0, 1) pt3dadd(-119, 0, 0, 1)}
+		comp[22] {pt3dclear() pt3dadd(-119, 0, 0, 1) pt3dadd(-119, 30, 0, 1)}
+		comp[34] {pt3dclear() pt3dadd(-119, 30, 0, 1) pt3dadd(-119, 60, 0, 1)}
+		comp[11] {pt3dclear() pt3dadd(-104, -14, 0, 1) pt3dadd(-89, 0, 0, 1)}
+		comp[23] {pt3dclear() pt3dadd(-89, 0, 0, 1) pt3dadd(-89, 30, 0, 1)}
+		comp[35] {pt3dclear() pt3dadd(-89, 30, 0, 1) pt3dadd(-89, 60, 0, 1)}
+		comp[12] {pt3dclear() pt3dadd(-104, -14, 0, 1) pt3dadd(-119, -29, 0, 1)}
+		comp[24] {pt3dclear() pt3dadd(-119, -29, 0, 1) pt3dadd(-119, -59, 0, 1)}
+		comp[36] {pt3dclear() pt3dadd(-119, -59, 0, 1) pt3dadd(-119, -89, 0, 1)}
+		comp[13] {pt3dclear() pt3dadd(-104, -14, 0, 1) pt3dadd(-89, -29, 0, 1)}
+		comp[25] {pt3dclear() pt3dadd(-89, -29, 0, 1) pt3dadd(-89, -59, 0, 1)}
+		comp[37] {pt3dclear() pt3dadd(-89, -59, 0, 1) pt3dadd(-89, -89, 0, 1)}
+		comp[2] {pt3dclear() pt3dadd(-149, -14, 0, 1) pt3dadd(-164, 30, 0, 1)}
+		comp[14] {pt3dclear() pt3dadd(-164, 30, 0, 1) pt3dadd(-179, 45, 0, 1)}
+		comp[26] {pt3dclear() pt3dadd(-179, 45, 0, 1) pt3dadd(-194, 60, 0, 1)}
+		comp[3] {pt3dclear() pt3dadd(-149, -14, 0, 1) pt3dadd(-179, 30, 0, 1)}
+		comp[15] {pt3dclear() pt3dadd(-179, 30, 0, 1) pt3dadd(-194, 45, 0, 1)}
+		comp[27] {pt3dclear() pt3dadd(-194, 45, 0, 1) pt3dadd(-209, 60, 0, 1)}
+		comp[4] {pt3dclear() pt3dadd(-149, -14, 0, 1) pt3dadd(-194, 15, 0, 1)}
+		comp[16] {pt3dclear() pt3dadd(-194, 15, 0, 1) pt3dadd(-209, 30, 0, 1)}
+		comp[28] {pt3dclear() pt3dadd(-209, 30, 0, 1) pt3dadd(-224, 45, 0, 1)}
+		comp[5] {pt3dclear() pt3dadd(-149, -14, 0, 1) pt3dadd(-194, 0, 0, 1)}
+		comp[17] {pt3dclear() pt3dadd(-194, 0, 0, 1) pt3dadd(-209, 15, 0, 1)}
+		comp[29] {pt3dclear() pt3dadd(-209, 15, 0, 1) pt3dadd(-224, 30, 0, 1)}
+		comp[6] {pt3dclear() pt3dadd(-149, -14, 0, 1) pt3dadd(-194, -29, 0, 1)}
+		comp[18] {pt3dclear() pt3dadd(-194, -29, 0, 1) pt3dadd(-209, -44, 0, 1)}
+		comp[30] {pt3dclear() pt3dadd(-209, -44, 0, 1) pt3dadd(-224, -59, 0, 1)}
+		comp[7] {pt3dclear() pt3dadd(-149, -14, 0, 1) pt3dadd(-164, -59, 0, 1)}
+		comp[19] {pt3dclear() pt3dadd(-164, -59, 0, 1) pt3dadd(-179, -74, 0, 1)}
+		comp[31] {pt3dclear() pt3dadd(-179, -74, 0, 1) pt3dadd(-194, -89, 0, 1)}
+		comp[8] {pt3dclear() pt3dadd(-149, -14, 0, 1) pt3dadd(-194, -44, 0, 1)}
+		comp[20] {pt3dclear() pt3dadd(-194, -44, 0, 1) pt3dadd(-209, -59, 0, 1)}
+		comp[32] {pt3dclear() pt3dadd(-209, -59, 0, 1) pt3dadd(-224, -74, 0, 1)}
+		comp[9] {pt3dclear() pt3dadd(-149, -14, 0, 1) pt3dadd(-179, -59, 0, 1)}
+		comp[21] {pt3dclear() pt3dadd(-179, -59, 0, 1) pt3dadd(-194, -74, 0, 1)}
+		comp[33] {pt3dclear() pt3dadd(-194, -74, 0, 1) pt3dadd(-209, -89, 0, 1)}
+		comp[69] {pt3dclear() pt3dadd(-134, -14, 0, 1) pt3dadd(-134, -44, 0, 1)}
+		comp[70] {pt3dclear() pt3dadd(-134, -44, 0, 1) pt3dadd(-134, -74, 0, 1)}
+		comp[71] {pt3dclear() pt3dadd(-134, -74, 0, 1) pt3dadd(-149, -89, 0, 1)}
+		comp[72] {pt3dclear() pt3dadd(-149, -89, 0, 1) pt3dadd(-149, -129, 0, 1)}
+		comp[73] {pt3dclear() pt3dadd(-134, -74, 0, 1) pt3dadd(-134, -104, 0, 1)}
+		comp[74] {pt3dclear() pt3dadd(-134, -104, 0, 1) pt3dadd(-134, -134, 0, 1)}
+	}
+
+endtemplate pyr3
\ No newline at end of file
diff --git a/netpyne/tutorials/docs/netstruct.png b/netpyne/tutorials/docs/netstruct.png
new file mode 100644
index 000000000..c8df00e0c
Binary files /dev/null and b/netpyne/tutorials/docs/netstruct.png differ
diff --git a/netpyne/tutorials/install_tuts.py b/netpyne/tutorials/install_tuts.py
new file mode 100644
index 000000000..aaa3a3dc1
--- /dev/null
+++ b/netpyne/tutorials/install_tuts.py
@@ -0,0 +1,24 @@
+"""
+Install NetPyNE tutorials
+"""
+
+import os
+
+os.system(
+    "mkdir netpyne_tuts && \
+    cd netpyne_tuts && \
+    export PATH=/bin:/usr/bin && \
+    python3 -m venv env && \
+    source env/bin/activate && \
+    python3 -m pip install --upgrade pip && \
+    python3 -m pip install --upgrade ipython && \
+    python3 -m pip install --upgrade ipykernel && \
+    python3 -m pip install --upgrade jupyter && \
+    ipython kernel install --user --name=env && \
+    python3 -m pip install --upgrade neuron && \
+    git clone --depth 1 https://github.com/suny-downstate-medical-center/netpyne.git && \
+    python3 -m pip install -e netpyne && \
+    cp -r netpyne/netpyne/tutorials . && \
+    cd tutorials && \
+    jupyter notebook"
+)
diff --git a/netpyne/tutorials/netpyne_tut0.sh b/netpyne/tutorials/install_tuts.sh
similarity index 75%
rename from netpyne/tutorials/netpyne_tut0.sh
rename to netpyne/tutorials/install_tuts.sh
index c5233dd74..4afd60461 100644
--- a/netpyne/tutorials/netpyne_tut0.sh
+++ b/netpyne/tutorials/install_tuts.sh
@@ -1,8 +1,7 @@
 #!/bin/bash
 
 # Installing NetPyNE tutorials
-# To make this script executable, enter: chmod u+x netpyne_tut0.sh
-# To execute this script, enter: ./netpyne_tut0.sh
+# To execute this script, enter: sh install_tuts.sh
 
 mkdir netpyne_tuts &&
 cd netpyne_tuts &&
@@ -15,7 +14,7 @@ python3 -m pip install --upgrade ipykernel &&
 python3 -m pip install --upgrade jupyter &&
 ipython kernel install --user --name=env &&
 python3 -m pip install --upgrade neuron &&
-git clone https://github.com/Neurosim-lab/netpyne.git &&
+git clone --depth 1 https://github.com/suny-downstate-medical-center/netpyne.git &&
 python3 -m pip install -e netpyne &&
 cp -r netpyne/netpyne/tutorials . &&
 cd tutorials &&
diff --git a/netpyne/tutorials/mod/A.mod b/netpyne/tutorials/mod/A.mod
new file mode 100644
index 000000000..a7643b1e3
--- /dev/null
+++ b/netpyne/tutorials/mod/A.mod
@@ -0,0 +1,24 @@
+NEURON { SUFFIX A }
+NEURON { USEION k WRITE ik }
+ASSIGNED { ik(mA/cm2) }
+
+PARAMETER {
+  erev = -70 (mV)
+  gmax  = 1.45e-07 (mho/cm2)
+  VhlfMaxm = -24
+  VhlfMaxh = 8
+  slopem = -3.2
+  slopeh = 4.9
+  taum = 82 (ms)
+  tauh = 5 (ms)
+}
+
+INCLUDE "ofc.inc"
+
+PROCEDURE iassign () { i = g*(v-erev) ik=i }
+
+
+
+
+
+
diff --git a/netpyne/tutorials/mod/AMPA.mod b/netpyne/tutorials/mod/AMPA.mod
new file mode 100644
index 000000000..b544430e7
--- /dev/null
+++ b/netpyne/tutorials/mod/AMPA.mod
@@ -0,0 +1,18 @@
+NEURON {  POINT_PROCESS AMPA }
+
+PARAMETER {
+  Cdur	= 1	(ms)		: transmitter duration (rising phase)
+  Alpha	= 1.	(/ms mM)	: forward (binding) rate
+  Beta	= 0.5	(/ms)		: backward (unbinding) rate
+  Erev	= 0	(mV)		: reversal potential
+}
+
+INCLUDE "netcon.inc"
+ 
+:** NMDA
+
+
+
+
+
+
diff --git a/netpyne/tutorials/mod/ElectSyn.mod b/netpyne/tutorials/mod/ElectSyn.mod
new file mode 100644
index 000000000..cd22108f1
--- /dev/null
+++ b/netpyne/tutorials/mod/ElectSyn.mod
@@ -0,0 +1,77 @@
+COMMENT
+
+   **************************************************
+   File generated by: neuroConstruct v1.7.1 
+   **************************************************
+
+   This file holds the implementation in NEURON of the Cell Mechanism:
+   ElectSyn (Type: Gap junction, Model: Template based ChannelML file)
+
+   with parameters: 
+   /channelml/@units = Physiological Units 
+   /channelml/notes = ChannelML file describing a single synaptic mechanism 
+   /channelml/synapse_type/@name = ElectSyn 
+   /channelml/synapse_type/status/@value = stable 
+   /channelml/synapse_type/status/contributor/name = Padraig Gleeson 
+   /channelml/synapse_type/notes = Description of an electrical synapse at a gap junction 
+   /channelml/synapse_type/electrical_syn/@conductance = 5e-8 
+
+// File from which this was generated: /home/padraig/nC_projects/Gaps/cellMechanisms/ElectSyn/ElectSyn.xml
+
+// XSL file with mapping to simulator: /home/padraig/nC_projects/Gaps/cellMechanisms/ElectSyn/ChannelML_v1.8.1_NEURONmod.xsl
+
+ENDCOMMENT
+
+
+?  This is a NEURON mod file generated from a ChannelML file
+
+?  Unit system of original ChannelML file: Physiological Units
+
+COMMENT
+    ChannelML file describing a single synaptic mechanism
+ENDCOMMENT
+
+? Creating synaptic mechanism for an electrical synapse
+    
+
+TITLE Channel: ElectSyn
+
+COMMENT
+    Description of an electrical synapse at a gap junction
+ENDCOMMENT
+
+
+UNITS {
+    (nA) = (nanoamp)
+    (mV) = (millivolt)
+    (uS) = (microsiemens)
+}
+
+    
+NEURON {
+    POINT_PROCESS ElectSyn
+    NONSPECIFIC_CURRENT i
+    RANGE g, i
+    RANGE weight
+    
+    RANGE vpeer     : Using a RANGE variable as opposed to POINTER for parallel mode
+        
+
+}
+
+PARAMETER {
+    v (millivolt)
+    vpeer (millivolt)
+    g = 0.000049999999999999996 (microsiemens)
+    weight = 1
+
+}
+
+
+ASSIGNED {
+    i (nanoamp)
+}
+
+BREAKPOINT {
+    i = weight * g * (v - vpeer)
+} 
diff --git a/netpyne/tutorials/mod/GABAa.mod b/netpyne/tutorials/mod/GABAa.mod
new file mode 100644
index 000000000..b1f18c12c
--- /dev/null
+++ b/netpyne/tutorials/mod/GABAa.mod
@@ -0,0 +1,18 @@
+NEURON {  POINT_PROCESS GABAa }
+
+PARAMETER {
+  Cdur	= 1.08	(ms)		: transmitter duration (rising phase)
+  Alpha	= 1.	(/ms mM)	: forward (binding) rate
+  Beta	= 0.5	(/ms)		: backward (unbinding) rate
+  Erev	= -70	(mV)		: reversal potential
+}
+
+INCLUDE "netcon.inc"
+ 
+:** GABAb
+
+
+
+
+
+
diff --git a/netpyne/tutorials/mod/HCN1.mod b/netpyne/tutorials/mod/HCN1.mod
new file mode 100644
index 000000000..4ed0564dc
--- /dev/null
+++ b/netpyne/tutorials/mod/HCN1.mod
@@ -0,0 +1,67 @@
+: $Id: HCN1.mod,v 1.4 2013/01/02 15:01:55 samn Exp $ 
+
+TITLE HCN1 
+
+UNITS {
+  (mA) = (milliamp)
+  (mV) = (millivolt)
+}
+ 
+NEURON {
+  SUFFIX HCN1
+  NONSPECIFIC_CURRENT ih
+  RANGE gbar, g, e, v50, htau, hinf
+  RANGE gfactor, htaufactor
+}
+ 
+PARAMETER {
+  celsius	(degC)
+  gbar = 0.0001	(mho/cm2)
+  e= -30	(mV)
+  v50= -73	(mV)
+  gfactor = 1
+  htaufactor = 1.0 : 4.78
+}
+ 
+STATE {
+  h
+}
+ 
+ASSIGNED {
+  ih	  (mA/cm2) 
+  hinf
+  htau    (ms)
+  v	  (mV)
+  g       (mho/cm2)
+}
+
+PROCEDURE giassign () { 
+  : ih=g*h*(v-e)*gfactor 
+  g = gbar*h*gfactor
+  ih = g*(v-e)
+}
+ 
+BREAKPOINT {
+  SOLVE states METHOD cnexp
+  giassign()
+}
+ 
+DERIVATIVE states { 
+  rates(v)
+  h'= (hinf- h)/ htau
+}
+
+INITIAL { 
+  rates(v)
+  h = hinf
+  giassign()
+}
+
+PROCEDURE rates(v (mV)) {
+  UNITSOFF
+  : HCN1
+  hinf = 1/(1+exp(0.151*(v-v50)))
+  htau = htaufactor*exp((0.033*(v+75)))/(0.011*(1+exp(0.083*(v+75))))
+  UNITSON
+}
+
diff --git a/netpyne/tutorials/mod/IC.mod b/netpyne/tutorials/mod/IC.mod
new file mode 100644
index 000000000..b489e1b7d
--- /dev/null
+++ b/netpyne/tutorials/mod/IC.mod
@@ -0,0 +1,88 @@
+TITLE Ca-dependent potassium current
+:
+:   Ca++ dependent K+ current IC responsible for 
+:   action potentials AHP's
+:   Differential equations
+:
+:   Model of Yamada, Koch & Adams, in: Methods in Neuronal Modeling,
+:   Ed. by Koch & Segev, MIT press, 1989.
+:
+:   This current models the "fast" IK[Ca]:
+:      - potassium current
+:      - activated by intracellular calcium
+:      - VOLTAGE DEPENDENT
+:
+:   Written by Alain Destexhe, Salk Institute, Sept 18, 1992
+:
+: should be considered 'BK' - fast, big conductance
+
+NEURON {
+	SUFFIX ikc
+	USEION k READ ek WRITE ik
+	USEION ca READ cai
+        RANGE gkbar, ik
+	RANGE m_inf, tau_m
+        RANGE taumin
+        GLOBAL ascale,bscale,vfctr
+}
+
+UNITS {
+	(mA) = (milliamp)
+	(mV) = (millivolt)
+	(molar) = (1/liter)
+	(mM) = (millimolar)
+}
+
+PARAMETER {
+	v		(mV)
+        celsius         (degC)
+	ek		(mV)
+        cai             (mM)
+	gkbar	= .003	(mho/cm2)	: taken from 
+        taumin = 0.1
+        ascale = 250.0
+        bscale = 0.1
+        vfctr = 24.0
+}
+
+STATE {
+	m
+}
+
+INITIAL {
+	evaluate_fct(v,cai)
+	m = m_inf
+}
+
+ASSIGNED {
+	ik	(mA/cm2)
+	m_inf
+	tau_m	(ms)
+}
+
+BREAKPOINT { 
+	SOLVE states METHOD cnexp
+	ik = gkbar * m * (v - ek)
+}
+
+DERIVATIVE states { 
+	evaluate_fct(v,cai)
+	m' = (m_inf - m) / tau_m
+}
+
+UNITSOFF
+PROCEDURE evaluate_fct(v(mV),cai(mM)) {  LOCAL a,b,tadj
+:
+:  activation kinetics of Yamada et al were at 22 deg. C
+:  transformation to 36 deg assuming Q10=3
+:
+	tadj = 3 ^ ((celsius-22.0)/10)
+
+	a = ascale * cai * exp(v/vfctr)
+	b = bscale * exp(-v/vfctr)
+
+	tau_m = 1.0 / (a + b) / tadj
+        if(tau_m < taumin){ tau_m = taumin }
+	m_inf = a / (a + b)
+}
+UNITSON
diff --git a/netpyne/tutorials/mod/IKsin.mod b/netpyne/tutorials/mod/IKsin.mod
new file mode 100755
index 000000000..0bce29e1a
--- /dev/null
+++ b/netpyne/tutorials/mod/IKsin.mod
@@ -0,0 +1,97 @@
+: Slowly inactivating K+ channel
+
+NEURON {
+	SUFFIX IKsin
+	USEION k READ ki, ko WRITE ik
+	RANGE gKsbar, ik, gk
+	
+}
+
+UNITS {
+	(mA) = (milliamp)
+	(mV) = (millivolt)
+        (mM) = (milli/liter)
+	
+}
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+PARAMETER {
+	v (mV)
+	dt (ms)
+	gKsbar= 0.00014 (mho/cm2) <0,1e9>
+	
+}
+
+
+STATE {
+	a b
+}
+
+
+ASSIGNED {
+	ik (mA/cm2)
+	ainf binf
+	atau (ms)
+	btau (ms)
+	gk (mho/cm2)
+	ek  (mV)
+	ki (mM)
+	ko  (mM)
+}
+
+
+
+INITIAL {
+	rate(v)
+	a = ainf
+	b = binf
+}
+
+BREAKPOINT {
+	SOLVE states METHOD cnexp
+		
+	gk = gKsbar * a * b
+	ek = 25 * log(ko/ki)
+	ik = gk*(v-ek)
+	
+}
+
+DERIVATIVE states {
+	rate(v)
+	
+	a' = (ainf-a)/atau
+	b' = (binf-b)/btau
+}
+UNITSOFF
+
+PROCEDURE rate(v (mV)) {LOCAL va, vb, vc, vd
+	
+	
+	va = v + 34
+	vb = v + 65
+	vd = v + 63.6
+	
+
+if (fabs(va)<1e-04){ va = va+0.00001 }
+	   ainf = 1/(1 + exp(-va/6.5))
+	   atau = 10
+	  :atau=6
+	
+
+if (fabs(vb)<1e-04){ vb = vb+0.00001 }
+	   binf = 1/(1 + exp(vb/6.6))
+
+ 
+if (fabs(vd)<1e-04){ vd = vd+0.00001 }
+	   btau = 200 + 3200 / (1 + exp(-vd/4))
+	:btau = 200 + 3200 / (1 + exp(-vd/4))
+}
+
+	
+UNITSON
+
+
+
+
+
+
+
diff --git a/netpyne/tutorials/mod/MyExp2SynBB.mod b/netpyne/tutorials/mod/MyExp2SynBB.mod
new file mode 100644
index 000000000..9a68baef1
--- /dev/null
+++ b/netpyne/tutorials/mod/MyExp2SynBB.mod
@@ -0,0 +1,67 @@
+: $Id: MyExp2SynBB.mod,v 1.4 2010/12/13 21:27:51 samn Exp $ 
+NEURON {
+:  THREADSAFE
+  POINT_PROCESS MyExp2SynBB
+  RANGE tau1, tau2, e, i, g, Vwt, gmax
+  NONSPECIFIC_CURRENT i
+}
+
+UNITS {
+  (nA) = (nanoamp)
+  (mV) = (millivolt)
+  (uS) = (microsiemens)
+}
+
+PARAMETER {
+  tau1=.1 (ms) <1e-9,1e9>
+  tau2 = 10 (ms) <1e-9,1e9>
+  e=0	(mV)
+  gmax = 1e9 (uS)
+  Vwt   = 0 : weight for inputs coming in from vector
+}
+
+ASSIGNED {
+  v (mV)
+  i (nA)
+  g (uS)
+  factor
+  etime (ms)
+}
+
+STATE {
+  A (uS)
+  B (uS)
+}
+
+INITIAL {
+  LOCAL tp
+
+  Vwt = 0    : testing
+
+  if (tau1/tau2 > .9999) {
+    tau1 = .9999*tau2
+  }
+  A = 0
+  B = 0
+  tp = (tau1*tau2)/(tau2 - tau1) * log(tau2/tau1)
+  factor = -exp(-tp/tau1) + exp(-tp/tau2)
+  factor = 1/factor
+}
+
+BREAKPOINT {
+  SOLVE state METHOD cnexp
+  g = B - A
+  if (g>gmax) {g=gmax}: saturation
+  i = g*(v - e)
+}
+
+DERIVATIVE state {
+  A' = -A/tau1
+  B' = -B/tau2
+}
+
+NET_RECEIVE(w (uS)) {LOCAL ww
+  ww=w
+  A = A + ww*factor
+  B = B + ww*factor
+}
diff --git a/netpyne/tutorials/mod/MyExp2SynNMDABB.mod b/netpyne/tutorials/mod/MyExp2SynNMDABB.mod
new file mode 100644
index 000000000..01291643a
--- /dev/null
+++ b/netpyne/tutorials/mod/MyExp2SynNMDABB.mod
@@ -0,0 +1,108 @@
+: $Id: MyExp2SynNMDABB.mod,v 1.4 2010/12/13 21:28:02 samn Exp $ 
+NEURON {
+:  THREADSAFE
+  POINT_PROCESS MyExp2SynNMDABB
+  RANGE tau1, tau2, e, i, iNMDA, s, sNMDA, r, tau1NMDA, tau2NMDA, Vwt, smax, sNMDAmax
+  NONSPECIFIC_CURRENT i, iNMDA
+}
+
+UNITS {
+  (nA) = (nanoamp)
+  (mV) = (millivolt)
+  (uS) = (microsiemens)
+}
+
+PARAMETER {
+  tau1     =   0.1 (ms) <1e-9,1e9>
+  tau2     =  10 (ms) <1e-9,1e9>	
+  tau1NMDA = 15  (ms)
+  tau2NMDA = 150 (ms)
+  e        = 0	(mV)
+  mg       = 1
+  r        = 1
+  smax     = 1e9 (1)
+  sNMDAmax = 1e9 (1)
+  
+  Vwt   = 0 : weight for inputs coming in from vector
+}
+
+ASSIGNED {
+  v       (mV)
+  i       (nA)
+  iNMDA   (nA)
+  s       (1)
+  sNMDA   (1)
+  mgblock (1)
+  factor  (1)
+  factor2 (1)
+	
+  etime (ms)
+}
+
+STATE {
+  A  (1)
+  B  (1)
+  A2 (1)
+  B2 (1)
+}
+
+INITIAL {
+
+  LOCAL tp
+  
+  Vwt = 0 : testing
+
+  if (tau1/tau2 > .9999) {
+    tau1 = .9999*tau2
+  }
+  A = 0
+  B = 0	
+  tp = (tau1*tau2)/(tau2 - tau1) * log(tau2/tau1)
+  factor = -exp(-tp/tau1) + exp(-tp/tau2)
+  factor = 1/factor
+  
+  if (tau1NMDA/tau2NMDA > .9999) {
+    tau1NMDA = .9999*tau2NMDA
+  }
+  A2 = 0
+  B2 = 0	
+  tp = (tau1NMDA*tau2NMDA)/(tau2NMDA - tau1NMDA) * log(tau2NMDA/tau1NMDA)
+  factor2 = -exp(-tp/tau1NMDA) + exp(-tp/tau2NMDA)
+  factor2 = 1/factor2  
+}
+
+BREAKPOINT {
+  SOLVE state METHOD cnexp
+  : Jahr Stevens 1990 J. Neurosci
+  mgblock = 1.0 / (1.0 + 0.28 * exp(-0.062(/mV) * v) )
+  s     = B  - A
+  sNMDA = B2 - A2
+  if (s    >smax)     {s    =smax    }: saturation
+  if (sNMDA>sNMDAmax) {sNMDA=sNMDAmax}: saturation
+  i     = s     * (v - e) 
+  iNMDA = sNMDA * (v - e) * mgblock
+}
+
+DERIVATIVE state {
+  A'  = -A/tau1
+  B'  = -B/tau2	
+  A2' = -A2/tau1NMDA
+  B2' = -B2/tau2NMDA
+}
+
+NET_RECEIVE(w (uS)) {LOCAL ww
+  ww=w
+  :printf("NMDA Spike: %g\n", t)
+  if(r>=0){ : if r>=0, g = AMPA + NMDA*r
+    A  = A  + factor *ww
+    B  = B  + factor *ww
+    A2 = A2 + factor2*ww*r
+    B2 = B2 + factor2*ww*r
+  }else{
+    if(r>-1000){ : if r>-1, g = NMDA*r  
+      A2 = A2 - factor2*ww*r
+      B2 = B2 - factor2*ww*r
+    }
+    : if r<0 and r<>-1, g = 0
+  }
+}
diff --git a/netpyne/tutorials/mod/NMDA.mod b/netpyne/tutorials/mod/NMDA.mod
new file mode 100644
index 000000000..6b8e91884
--- /dev/null
+++ b/netpyne/tutorials/mod/NMDA.mod
@@ -0,0 +1,37 @@
+NEURON{ POINT_PROCESS NMDA
+  RANGE B 
+  GLOBAL mg
+}
+
+PARAMETER {
+  mg        = 1.    (mM)     : external magnesium concentration
+  Cdur	= 1.	(ms)	 : transmitter duration (rising phase)
+  Alpha	= 4.	(/ms mM) : forward (binding) rate
+  Beta	= 0.0067 (/ms)	 : backward (unbinding) rate 1/150
+  Erev	= 0.	(mV)	 : reversal potential
+}
+
+ASSIGNED { B }
+
+INCLUDE "netcon.inc"
+: EXTRA BREAKPOINT MUST BE BELOW THE INCLUDE
+BREAKPOINT {
+  rates(v)
+  g = g * B : g = GMAX * R * B
+  i = i * B : i = g*(v - Erev)
+}
+
+PROCEDURE rates(v(mV)) {
+  TABLE B
+  DEPEND mg
+  FROM -100 TO 80 WITH 180
+  B = 1 / (1 + exp(0.062 (/mV) * -v) * (mg / 3.57 (mM)))
+}
+ 
+:** GABAa
+
+
+
+
+
+
diff --git a/netpyne/tutorials/mod/Nca.mod b/netpyne/tutorials/mod/Nca.mod
new file mode 100644
index 000000000..33add8cd9
--- /dev/null
+++ b/netpyne/tutorials/mod/Nca.mod
@@ -0,0 +1,124 @@
+: $Id: Nca.mod,v 1.7 2004/06/08 21:07:12 billl Exp $
+
+COMMENT
+26 Ago 2002 Modification of original channel to allow variable time step and to correct an initialization error.
+Done by Michael Hines(michael.hines@yale.e) and Ruggero Scorcioni(rscorcio@gmu.edu) at EU Advance Course in Computational Neuroscience. Obidos, Portugal
+
+ca.mod
+Uses fixed eca instead of GHK eqn
+
+HVA Ca current
+Based on Reuveni, Friedman, Amitai and Gutnick (1993) J. Neurosci. 13:
+4609-4621.
+
+Author: Zach Mainen, Salk Institute, 1994, zach@salk.edu
+
+ENDCOMMENT
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+NEURON {
+  SUFFIX Nca
+  USEION ca READ eca WRITE ica
+  RANGE i, m, h, gca, gmax
+  RANGE minf, hinf, mtau, htau
+  GLOBAL q10, temp, tadj, vmin, vmax, vshift
+}
+
+PARAMETER {
+  gmax = 0.1   	(pS/um2)	: 0.12 mho/cm2
+  vshift = 0	(mV)		: voltage shift (affects all)
+
+  cao  = 2.5	(mM)	        : external ca concentration
+  cai		(mM)
+  
+  temp = 23	(degC)		: original temp 
+  q10  = 2.3			: temperature sensitivity
+
+  v 		(mV)
+  dt		(ms)
+  celsius		(degC)
+  vmin = -120	(mV)
+  vmax = 100	(mV)
+}
+
+
+UNITS {
+  (mA) = (milliamp)
+  (mV) = (millivolt)
+  (pS) = (picosiemens)
+  (um) = (micron)
+  FARADAY = (faraday) (coulomb)
+  R = (k-mole) (joule/degC)
+  PI	= (pi) (1)
+} 
+
+ASSIGNED {
+  i 		(mA/cm2)
+  ica 		(mA/cm2)
+  gca		(pS/um2)
+  eca		(mV)
+  minf 		hinf
+  mtau (ms)	htau (ms)
+  tadj
+}
+
+
+STATE { m h }
+
+INITIAL { 
+  tadj = q10^((celsius - temp)/10)
+  rates(v+vshift)
+  m = minf
+  h = hinf
+}
+
+BREAKPOINT {
+  SOLVE states METHOD cnexp
+  gca = tadj*gmax*m*m*h
+  i = (1e-4) * gca * (v - eca)
+  ica = i
+} 
+
+LOCAL mexp, hexp
+
+:PROCEDURE states() {
+  :        rates(v+vshift)      
+  :        m = m + mexp*(minf-m)
+  :        h = h + hexp*(hinf-h)
+  :	VERBATIM
+  :	return 0;
+  :	ENDVERBATIM
+  :}
+
+  DERIVATIVE states {
+    rates(v+vshift)      
+    m' =  (minf-m)/mtau
+    h' =  (hinf-h)/htau
+  }
+
+  PROCEDURE rates(vm) {  
+    LOCAL  a, b
+
+    a = 0.055*(-27 - vm)/(exp((-27-vm)/3.8) - 1)
+    b = 0.94*exp((-75-vm)/17)
+    
+    mtau = 1/tadj/(a+b)
+    minf = a/(a+b)
+
+    :"h" inactivation 
+
+    a = 0.000457*exp((-13-vm)/50)
+    b = 0.0065/(exp((-vm-15)/28) + 1)
+
+    htau = 1/tadj/(a+b)
+    hinf = a/(a+b)
+  }
+
+  FUNCTION efun(z) {
+    if (fabs(z) < 1e-4) {
+      efun = 1 - z/2
+    }else{
+      efun = z/(exp(z) - 1)
+    }
+  }
diff --git a/netpyne/tutorials/mod/OFThpo.mod b/netpyne/tutorials/mod/OFThpo.mod
new file mode 100644
index 000000000..10d89ca3b
--- /dev/null
+++ b/netpyne/tutorials/mod/OFThpo.mod
@@ -0,0 +1,135 @@
+: $Id: OFThpo.mod,v 1.3 2009/03/13 11:57:56 billl Exp $ 
+
+COMMENT
+based on Otto Friesen Neurodynamix model
+spiking portion of cell model
+variant on OFThresh.mod that uses paste-on instead of calculated spike
+ENDCOMMENT
+
+TITLE OF Threshold Spiking
+
+UNITS {
+    (mV) = (millivolt)
+    (nA) = (nanoamp)
+    (uS) = (microsiemens)
+}
+
+NEURON {
+  POINT_PROCESS OFPO
+  USEION other WRITE iother VALENCE 1.0
+
+  RANGE gkbase             : base level of k+ conductance after a spike
+  RANGE gkmin              : min level of k+ conductance can decay to after a spike
+  RANGE vth                : threshold for spike
+  RANGE tauvtha, vthinc    : used for threshold adaptation
+  RANGE taugka, gkinc      : used for gk adaptation (gkadapt state var)
+  RANGE ik,ek              : k-related variables
+  RANGE tauk               : tau for k current
+  RANGE i,spkht            : current, spike height
+  RANGE refrac             : duration of absolute refractory period
+  RANGE inrefrac           : if in refractory period
+  RANGE apdur              : action potential duration
+  RANGE ena,gnamax         : na-related variables -- NOT used
+
+  GLOBAL verbose
+  GLOBAL checkref          : check for spikes @ end of refrac
+}
+
+ASSIGNED { 
+  v (mV)
+  iother (nA)
+}
+
+STATE { gk vthadapt gkadapt }
+
+PARAMETER {
+  gkbase=0.060(uS) : Max Potassium conductance
+  taugka=100 (ms) : Time constant of adaptation
+  kadapt=0.007(uS) : Amount of adaptation for potassium
+  spkht = 55(mV)
+  tauk=2.3 (ms) : Time constant for potassium current 
+  ek = -70(mV)
+  vth = -40(mV)
+  refrac = 2.7(ms)
+  inrefrac = 0
+  verbose = 0
+  apdur = 0.9 (ms)
+  gkinc = 0.006(uS)
+  tauvtha = 1(ms)
+  vthinc = 0
+  ik = 0(nA)
+  i = 0(nA)
+  gkmin = 0.00001(uS)
+  checkref = 1
+  gnamax = 0 : Na not used -- back compatibility
+  ena = 0
+}
+
+BREAKPOINT {
+  SOLVE states METHOD cnexp
+  if( gk < gkmin ) { gk = gkmin }
+  if( gkadapt < gkbase ) { gkadapt = gkbase }
+  if( vthadapt < vth ) { vthadapt = vth }
+  iassign()
+}
+
+INITIAL {
+  net_send(0,1)
+  gk = 0(uS)
+  gkadapt = gkbase
+  vthadapt = vth
+  ik = 0  
+  i = 0
+  iother = 0
+  inrefrac = 0
+}
+
+DERIVATIVE states {  
+  gk' = -gk/tauk
+  gkadapt' = (gkbase - gkadapt)/taugka
+  vthadapt' = (vth - vthadapt)/tauvtha
+}
+
+PROCEDURE iassign () {
+  ik = gk*(v-ek)
+  i = ik
+  iother = i
+}
+
+NET_RECEIVE (w) {
+  if (flag == 1) {
+    WATCH (v > vthadapt) 2
+  } else if (flag == 2) {  :v > threshold
+    if(inrefrac == 0) {      :if not in refractory period, spike
+      net_event(t)           :send spike event
+      net_send(apdur,3)      :send event for end of action potential
+      net_send(refrac,4)     :send event for end of refractory period
+      inrefrac=1             :in refractory period
+      if( verbose ) { printf("spike at t=%g\n",t) }
+    } else {
+      if( verbose ) { printf("in refrac @ t = %g, no spike\n",t) }
+    }
+  } else if(flag == 3) {   :end of action potential
+    gkadapt = gkadapt + gkinc
+    vthadapt = vthadapt + vthinc : threshold adaptation
+    gk = gkadapt           :turn gk to max after action potential over
+    if (verbose) { printf("end of action potential @ t = %g\n",t) }
+  } else if(flag == 4) {   :end of refractory period
+    inrefrac = 0           :set inrefrac flag off
+    if( verbose ) { printf("refrac over @ t = %g\n",t) }
+    :check for new spike @ end of refrac
+    if(checkref && v > vthadapt) { net_send(0,2) }
+  }
+}
+
+FUNCTION fflag () { fflag=1 }
+
+PROCEDURE version () {
+  printf("$Id: OFThpo.mod,v 1.3 2009/03/13 11:57:56 billl Exp $ ")
+}
+
+
+
+
+
+
diff --git a/netpyne/tutorials/mod/OFThresh.mod b/netpyne/tutorials/mod/OFThresh.mod
new file mode 100644
index 000000000..a1c082e41
--- /dev/null
+++ b/netpyne/tutorials/mod/OFThresh.mod
@@ -0,0 +1,139 @@
+: $Id: OFThresh.mod,v 1.22 2010/05/04 21:32:47 billl Exp $
+
+COMMENT
+based on Otto Friesen Neurodynamix model
+spiking portion of cell model
+ENDCOMMENT
+
+TITLE OF Threshold Spiking
+
+UNITS {
+    (mV) = (millivolt)
+    (nA) = (nanoamp)
+    (uS) = (microsiemens)
+}
+
+NEURON {
+  POINT_PROCESS OFTH
+  USEION other WRITE iother VALENCE 1.0
+
+  RANGE gkbase             : base level of k+ conductance after a spike
+  RANGE gkmin              : min level of k+ conductance can decay to after a spike
+  RANGE vth                : threshold for spike
+  RANGE tauvtha, vthinc    : used for threshold adaptation
+  RANGE taugka, gkinc      : used for gk adaptation (gkadapt state var)
+  RANGE ik,ek              : k-related variables
+  RANGE tauk               : tau for k current
+  RANGE i,spkht            : current, spike height
+  RANGE refrac             : duration of absolute refractory period
+  RANGE inrefrac           : if in refractory period
+  RANGE apdur              : action potential duration
+  RANGE gna,ena,ina,gnamax : na-related variables
+
+  RANGE verbose
+  GLOBAL checkref          : check for spikes @ end of refrac
+}
+
+ASSIGNED { 
+  v (mV)
+  iother (nA)
+  inrefrac
+}
+
+STATE { gk vthadapt gkadapt }
+
+PARAMETER {
+  gkbase=0.060(uS) : Max Potassium conductance
+  taugka=100 (ms) : Time constant of adaptation
+  kadapt=0.007(uS) : Amount of adaptation for potassium
+  spkht = 55(mV)
+  tauk=2.3 (ms) : Time constant for potassium current 
+  ek = -70(mV)
+  vth = -40(mV)
+  refrac = 2.7(ms)
+  verbose = 0
+  apdur = 0.9 (ms)
+  gkinc = 0.006(uS)
+  tauvtha = 1(ms)
+  vthinc = 0
+  gna = 0(uS)
+  ena = 55(mV)
+  ina = 0(nA)
+  ik = 0(nA)
+  i = 0(nA)
+  gnamax = .300(uS)
+  gkmin = 0.00001(uS)
+  checkref = 1
+}
+
+BREAKPOINT {
+  SOLVE states METHOD cnexp
+  if( gk < gkmin ) { gk = gkmin }
+  if( gkadapt < gkbase ) { gkadapt = gkbase }
+  if( vthadapt < vth ) { vthadapt = vth }
+  iassign()
+}
+
+INITIAL {
+  net_send(0,1)
+  gk = 0(uS)
+  gkadapt = gkbase
+  vthadapt = vth
+  gna = 0(uS)
+  ina = 0
+  ik = 0  
+  i = 0
+  iother = 0
+  inrefrac = 0
+}
+
+DERIVATIVE states {  
+  gk' = -gk/tauk
+  gkadapt' = (gkbase - gkadapt)/taugka
+  vthadapt' = (vth - vthadapt)/tauvtha
+}
+
+PROCEDURE iassign () {
+  ik = gk*(v-ek)
+  ina = gna*(v-ena)
+  i = ik + ina
+  iother = i
+}
+
+NET_RECEIVE (w) {
+  if (flag==1) {
+    WATCH (v > vthadapt) 2
+  } else if (flag==2 && !inrefrac) {  :v>threshold or direct input
+      net_event(t)           :send spike event
+      net_send(apdur,3)      :send event for end of action potential
+      net_send(refrac,4)     :send event for end of refractory period
+      inrefrac=1             :in refractory period
+      gkadapt = gkadapt + gkinc : explicit 'state_discontinuity' command not needed
+      vthadapt = vthadapt + vthinc : threshold adaptation
+      gna = gnamax : turn on na
+      if (verbose) { printf("spike at t=%g\n",t) }
+  } else if(flag==3) {   :end of action potential
+    gk = gkadapt           :turn gk to max after action potential over
+    gna = 0 :turn off na
+    if (verbose) { printf("end of action potential @ t = %g\n",t) }
+  } else if(flag==4) {   :end of refractory period
+    inrefrac = 0           :set inrefrac flag off
+    if (verbose) { printf("refrac over @ t = %g\n",t) }
+    :check for new spike @ end of refrac
+    if(checkref && v > vthadapt) { net_send(0,2) }
+  } else if (flag==0 && w>0) {
+    net_event(t)           :extra spike event from outside -- just pass on to postsyn cells
+  } else if (flag==2 && inrefrac && verbose ) { printf("in refrac @ t = %g, no spike\n",t) }
+}
+
+FUNCTION fflag () { fflag=1 }
+
+PROCEDURE version () {
+  printf("$Id: OFThresh.mod,v 1.22 2010/05/04 21:32:47 billl Exp $ ")
+}
+
+
+
+
+
+
diff --git a/netpyne/tutorials/mod/ar.mod b/netpyne/tutorials/mod/ar.mod
new file mode 100755
index 000000000..8ba93bf93
--- /dev/null
+++ b/netpyne/tutorials/mod/ar.mod
@@ -0,0 +1,53 @@
+TITLE Anomalous rectifier current for RD Traub, J Neurophysiol 89:909-921, 2003
+
+COMMENT
+
+	Implemented by Maciej Lazarewicz 2003 (mlazarew@seas.upenn.edu)
+
+ENDCOMMENT
+
+INDEPENDENT { t FROM 0 TO 1 WITH 1 (ms) }
+
+UNITS { 
+	(mV) = (millivolt) 
+	(mA) = (milliamp) 
+} 
+NEURON { 
+	SUFFIX ar
+	NONSPECIFIC_CURRENT i
+	RANGE gbar, i
+}
+PARAMETER { 
+	gbar = 0.0 	(mho/cm2)
+	v		(mV) 
+	erev = -35	(mV)  
+} 
+ASSIGNED { 
+	i 		(mA/cm2) 
+	minf 		(1)
+	mtau 		(ms) 
+} 
+STATE {
+	m
+}
+BREAKPOINT { 
+	SOLVE states METHOD cnexp
+	i = gbar * m * ( v - erev ) 
+} 
+INITIAL { 
+	settables(v) 
+	m = minf
+	m = 0.25
+} 
+DERIVATIVE states { 
+	settables(v) 
+	m' = ( minf - m ) / mtau 
+}
+
+UNITSOFF 
+PROCEDURE settables(v) { 
+	TABLE minf, mtau FROM -120 TO 40 WITH 641
+	minf  = 1 / ( 1 + exp( ( v + 75 ) / 5.5 ) )
+	mtau = 1 / ( exp( -14.6 - 0.086 * v ) + exp( -1.87 + 0.07 * v ) )
+}
+UNITSON
\ No newline at end of file
diff --git a/netpyne/tutorials/mod/aux_fun.inc b/netpyne/tutorials/mod/aux_fun.inc
new file mode 100644
index 000000000..ccb579afb
--- /dev/null
+++ b/netpyne/tutorials/mod/aux_fun.inc
@@ -0,0 +1,43 @@
+: $Id: aux_fun.inc,v 1.1 2009/11/04 01:24:52 samn Exp $ 
+COMMENT
+
+//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+//
+// NOTICE OF COPYRIGHT AND OWNERSHIP OF SOFTWARE
+//
+// Copyright 2007, The University Of Pennsylvania
+// 	School of Engineering & Applied Science.
+//   All rights reserved.
+//   For research use only; commercial use prohibited.
+//   Distribution without permission of Maciej T. Lazarewicz not permitted.
+//   mlazarew@seas.upenn.edu
+//
+//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+ENDCOMMENT
+
+
+
+:-------------------------------------------------------------------
+FUNCTION fun1(v(mV),V0(mV),A(/ms),B(mV))(/ms) {
+
+	 fun1 = A*exp((v-V0)/B)
+}
+
+FUNCTION fun2(v(mV),V0(mV),A(/ms),B(mV))(/ms) {
+
+	 fun2 = A/(exp((v-V0)/B)+1)
+}
+
+FUNCTION fun3(v(mV),V0(mV),A(/ms),B(mV))(/ms) {
+
+    if(fabs((v-V0)/B)<1e-6) {
+    :if(v==V0) {
+        fun3 = A*B/1(mV) * (1- 0.5 * (v-V0)/B)
+    } else {
+        fun3 = A/1(mV)*(v-V0)/(exp((v-V0)/B)-1)
+    }
+}
+
+FUNCTION min(x,y) { if (x<=y){ min = x }else{ min = y } }
+FUNCTION max(x,y) { if (x>=y){ max = x }else{ max = y } }
diff --git a/netpyne/tutorials/mod/beforestep_py.mod b/netpyne/tutorials/mod/beforestep_py.mod
new file mode 100644
index 000000000..3e2552a7f
--- /dev/null
+++ b/netpyne/tutorials/mod/beforestep_py.mod
@@ -0,0 +1,48 @@
+: Python callback from BEFORE STEP
+
+NEURON {
+  POINT_PROCESS beforestep_callback
+  POINTER ptr     
+}
+
+ASSIGNED {
+  ptr     
+}
+
+INITIAL {
+}
+
+VERBATIM
+extern int (*nrnpy_hoccommand_exec)(Object*);
+extern Object** hoc_objgetarg(int);
+extern int ifarg(int);
+extern void hoc_obj_ref(Object*);
+extern void hoc_obj_unref(Object*);
+ENDVERBATIM
+
+BEFORE STEP {
+  :printf("beforestep_callback t=%g\n", t)
+VERBATIM
+{
+  Object* cb = (Object*)(_p_ptr);
+  if (cb) {
+    (*nrnpy_hoccommand_exec)(cb);
+  }
+}
+ENDVERBATIM
+}
+
+PROCEDURE set_callback() {
+VERBATIM
+  Object** pcb = (Object**)(&(_p_ptr));
+  if (*pcb) {
+    hoc_obj_unref(*pcb);
+    *pcb = (Object*)0;
+  }
+  if (ifarg(1)) {
+    *pcb = *(hoc_objgetarg(1));
+    hoc_obj_ref(*pcb);
+  }
+ENDVERBATIM
+}
+
diff --git a/netpyne/tutorials/mod/ca.mod b/netpyne/tutorials/mod/ca.mod
new file mode 100644
index 000000000..663b77513
--- /dev/null
+++ b/netpyne/tutorials/mod/ca.mod
@@ -0,0 +1,135 @@
+COMMENT
+    26 Ago 2002 Modification of original channel to allow variable time step
+            and to correct an initialization error.
+
+    Done by Michael Hines(michael.hines@yale.edu) and Ruggero Scorcioni (rscorcio@gmu.edu)
+            at EU Advance Course in Computational Neuroscience. Obidos, Portugal
+
+    ca.mod
+    Uses fixed eca instead of GHK eqn
+
+    HVA Ca current
+    Based on Reuveni, Friedman, Amitai and Gutnick (1993) J. Neurosci. 13: 4609-4621.
+
+    Author: Zach Mainen, Salk Institute, 1994, zach@salk.edu
+ENDCOMMENT
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+NEURON {
+    SUFFIX ca_hnn
+    USEION ca READ eca WRITE ica
+    RANGE m, h, gca, gbar
+    RANGE minf, hinf, mtau, htau
+    GLOBAL q10, temp, tadj, vmin, vmax, vshift, tshift
+}
+
+PARAMETER {
+    gbar = 0.1   (pS/um2) : 0.12 mho/cm2
+    vshift = 0   (mV)     : voltage shift (affects all)
+
+    cao  = 2.5   (mM)     : external ca concentration
+    cai          (mM)
+
+    temp = 23    (degC)   : original temp
+    q10  = 2.3            : temperature sensitivity
+    tshift = 30.7
+
+    v            (mV)
+    dt           (ms)
+    celsius      (degC)
+    vmin = -120  (mV)
+    vmax = 100   (mV)
+}
+
+UNITS {
+    (mA) = (milliamp)
+    (mV) = (millivolt)
+    (pS) = (picosiemens)
+    (um) = (micron)
+    FARADAY = (faraday) (coulomb)
+    R = (k-mole) (joule/degC)
+    PI  = (pi) (1)
+}
+
+ASSIGNED {
+    ica         (mA/cm2)
+    gca         (pS/um2)
+    eca         (mV)
+    minf        hinf
+    mtau (ms)   htau (ms)
+    tadj
+}
+
+STATE { m h }
+
+INITIAL {
+    trates(v+vshift)
+    m = minf
+    h = hinf
+}
+
+BREAKPOINT {
+    SOLVE states METHOD cnexp
+    gca = tadj * gbar * m * m * h
+    ica = (1e-4) * gca * (v - eca)
+}
+
+LOCAL mexp, hexp
+
+: PROCEDURE states() {
+:         trates(v+vshift)
+:         m = m + mexp*(minf-m)
+:         h = h + hexp*(hinf-h)
+:    VERBATIM
+:    return 0;
+:    ENDVERBATIM
+: }
+
+DERIVATIVE states {
+    trates(v + vshift)
+    m' =  (minf - m) / mtau
+    h' =  (hinf - h) / htau
+}
+
+PROCEDURE trates(v) {
+    TABLE minf, hinf, mtau, htau
+    DEPEND  celsius, temp
+
+    FROM vmin TO vmax WITH 199
+
+    : not consistently executed from here if usetable == 1
+    rates(v)
+
+    : tinc = -dt * tadj
+
+    : mexp = 1 - exp(tinc/mtau)
+    : hexp = 1 - exp(tinc/htau)
+}
+
+PROCEDURE rates(vm) {
+    LOCAL a, b
+
+    tadj = q10^((celsius - temp - tshift)/10)
+
+    a = 0.055 * (-27 - vm) / (exp((-27 - vm) / 3.8) - 1)
+    b = 0.94 * exp((-75 - vm) / 17)
+
+    mtau = 1 / tadj / (a+b)
+    minf = a / (a + b)
+
+    : "h" inactivation
+    a = 0.000457 * exp((-13 - vm) / 50)
+    b = 0.0065 / (exp((-vm - 15) / 28) + 1)
+
+    htau = 1 / tadj / (a + b)
+    hinf = a / (a + b)
+}
+
+FUNCTION efun(z) {
+    if (fabs(z) < 1e-4) {
+        efun = 1 - z/2
+    } else {
+        efun = z / (exp(z) - 1)
+    }
+}
diff --git a/netpyne/tutorials/mod/cad.mod b/netpyne/tutorials/mod/cad.mod
new file mode 100755
index 000000000..ddc15f1ec
--- /dev/null
+++ b/netpyne/tutorials/mod/cad.mod
@@ -0,0 +1,44 @@
+TITLE Calcium dynamics for RD Traub, J Neurophysiol 89:909-921, 2003
+
+COMMENT
+
+	Implemented by Maciej Lazarewicz 2003 (mlazarew@seas.upenn.edu)
+
+ENDCOMMENT
+
+NEURON {
+	SUFFIX cad
+	USEION ca READ ica WRITE cai
+	RANGE  phi, beta
+	GLOBAL ceiling
+}
+
+UNITS {
+	(mA)	= (milliamp)
+}
+
+PARAMETER {
+	phi		(1)
+	beta		(/ms)
+	ceiling		(1)
+}
+
+STATE {	cai (1) }
+
+INITIAL { 
+	cai = 0.0 
+}
+
+ASSIGNED { 
+	ica		(mA/cm2) 
+}
+	
+BREAKPOINT {
+	SOLVE state METHOD cnexp
+	if( cai < 0 ){ cai = 0 }
+	if( cai > ceiling ){ cai = ceiling }
+}
+
+DERIVATIVE state { 
+	cai' = - phi * ica - beta * cai 
+}
diff --git a/netpyne/tutorials/mod/cadad.mod b/netpyne/tutorials/mod/cadad.mod
new file mode 100644
index 000000000..46466753c
--- /dev/null
+++ b/netpyne/tutorials/mod/cadad.mod
@@ -0,0 +1,74 @@
+: $Id: cadad.mod,v 1.4 2002/11/08 15:42:37 billl Exp $
+TITLE Fast mechanism for submembranal Ca++ concentration (cai)
+:
+: Takes into account:
+:
+:	- increase of cai due to calcium currents
+:	- extrusion of calcium with a simple first order equation
+:
+: This mechanism is compatible with the calcium pump "cad" and has the 
+: same name and parameters; however the parameters specific to the pump
+: are dummy here.
+:
+: Parameters:
+:
+:	- depth: depth of the shell just beneath the membran (in um)
+:	- cainf: equilibrium concentration of calcium (2e-4 mM)
+:	- taur: time constant of calcium extrusion (must be fast)
+:	- kt,kd: dummy parameters
+:
+: Written by Alain Destexhe, Salk Institute, 1995
+:
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+NEURON {
+  SUFFIX cadad
+  USEION ca READ ica, cai WRITE cai
+  RANGE depth,kt,kd,cainf,taur
+}
+
+UNITS {
+  (molar) = (1/liter)			: moles do not appear in units
+  (mM)	= (millimolar)
+  (um)	= (micron)
+  (mA)	= (milliamp)
+  (msM)	= (ms mM)
+}
+
+CONSTANT {
+  FARADAY = 96489		(coul)		: moles do not appear in units
+  :	FARADAY = 96.489	(k-coul)	: moles do not appear in units
+}
+
+PARAMETER {
+  depth	= 1	(um)		: depth of shell
+  taur	= 5	(ms)		: rate of calcium removal
+  cainf	= 2.4e-4	(mM)
+  kt	= 0	(mM/ms)		: dummy
+  kd	= 0	(mM)		: dummy
+}
+
+STATE {
+  cai		(mM) 
+}
+
+INITIAL {
+  cai = cainf
+}
+
+ASSIGNED {
+  ica		(mA/cm2)
+  drive_channel	(mM/ms)
+}
+	
+BREAKPOINT {
+  SOLVE state METHOD cnexp
+}
+
+DERIVATIVE state { 
+  drive_channel =  - (10000) * ica / (2 * FARADAY * depth)
+  if (drive_channel <= 0.) { drive_channel = 0. }	: cannot pump inward
+  cai' = drive_channel + (cainf-cai)/taur
+}
+
diff --git a/netpyne/tutorials/mod/cadyn.mod b/netpyne/tutorials/mod/cadyn.mod
new file mode 100755
index 000000000..58aa6177d
--- /dev/null
+++ b/netpyne/tutorials/mod/cadyn.mod
@@ -0,0 +1,52 @@
+: simple first-order model of calcium dynamics
+
+NEURON {
+        SUFFIX cadyn
+        USEION ca READ cai,ica WRITE cai 
+        RANGE ca 
+	GLOBAL depth,cainf,taur 
+     
+}
+
+UNITS {
+        (molar) = (1/liter)		
+        (mM) = (milli/liter)
+	(um)	= (micron) 
+        (mA) = (milliamp)
+	(msM)	= (ms mM)  
+        FARADAY    = (faraday) (coul)
+}
+
+PARAMETER {
+       depth	= .1	(um)		
+        taur =  200 (ms)	: rate of calcium removal for stress conditions
+	cainf	= 50e-6(mM)	:changed oct2
+	cai		(mM)
+}
+
+ASSIGNED {
+	ica		(mA/cm2)
+	drive_channel	(mM/ms)
+}
+
+STATE {
+	ca		(mM) 
+}
+
+ 
+BREAKPOINT {
+	SOLVE state METHOD euler
+}
+
+DERIVATIVE state { 
+
+	drive_channel =  - (10000) * ica / (2 * FARADAY * depth)
+	if (drive_channel <= 0.) { drive_channel = 0.  }   : cannot pump inward 
+        ca' = drive_channel/18 + (cainf -ca)/taur*11
+	cai = ca
+}
+
+
+INITIAL {
+	ca = cainf
+}
diff --git a/netpyne/tutorials/mod/cagk.mod b/netpyne/tutorials/mod/cagk.mod
new file mode 100644
index 000000000..662d43764
--- /dev/null
+++ b/netpyne/tutorials/mod/cagk.mod
@@ -0,0 +1,87 @@
+TITLE CaGk
+: Calcium activated K channel.
+: Modified from Moczydlowski and Latorre (1983) J. Gen. Physiol. 82
+
+UNITS {
+	(molar) = (1/liter)
+}
+
+UNITS {
+	(mV) =	(millivolt)
+	(mA) =	(milliamp)
+	(mM) =	(millimolar)
+}
+
+
+NEURON {
+	SUFFIX cagk
+	USEION ca READ cai
+	USEION k READ ek WRITE ik
+	RANGE gbar,gkca,ik
+        RANGE oinf, tau
+}
+
+UNITS {
+	FARADAY = (faraday)  (kilocoulombs)
+	R = 8.313424 (joule/degC)
+}
+
+PARAMETER {
+	celsius		(degC)
+	v		(mV)
+	gbar=.01	(mho/cm2)	: Maximum Permeability
+	cai 		(mM)
+	ek		(mV)
+
+	d1 = .84
+	d2 = 1.
+	k1 = .48e-3	(mM)
+	k2 = .13e-6	(mM)
+	abar = .28	(/ms)
+	bbar = .48	(/ms)
+        st=1            (1)
+}
+
+ASSIGNED {
+	ik		(mA/cm2)
+	oinf
+	tau		(ms)
+        gkca          (mho/cm2)
+}
+
+INITIAL {
+        rate(v,cai)
+        o=oinf
+}
+
+STATE {	o }		: fraction of open channels
+
+BREAKPOINT {
+	SOLVE state METHOD cnexp
+	gkca = gbar*o^st
+	ik = gkca*(v - ek)
+}
+
+DERIVATIVE state {	: exact when v held constant; integrates over dt step
+	rate(v, cai)
+	o' = (oinf - o)/tau
+}
+
+FUNCTION alp(v (mV), c (mM)) (1/ms) { :callable from hoc
+	alp = c*abar/(c + exp1(k1,d1,v))
+}
+
+FUNCTION bet(v (mV), c (mM)) (1/ms) { :callable from hoc
+	bet = bbar/(1 + c/exp1(k2,d2,v))
+}
+
+FUNCTION exp1(k (mM), d, v (mV)) (mM) { :callable from hoc
+	exp1 = k*exp(-2*d*FARADAY*v/R/(273.15 + celsius))
+}
+
+PROCEDURE rate(v (mV), c (mM)) { :callable from hoc
+	LOCAL a
+	a = alp(v,c)
+	tau = 1/(a + bet(v, c))
+	oinf = a*tau
+}
diff --git a/netpyne/tutorials/mod/cal_mh.mod b/netpyne/tutorials/mod/cal_mh.mod
new file mode 100644
index 000000000..79ad9c924
--- /dev/null
+++ b/netpyne/tutorials/mod/cal_mh.mod
@@ -0,0 +1,139 @@
+
+COMMENT
+26 Ago 2002 Modification of original channel to allow variable time step and to correct an initialization error.
+    Done by Michael Hines(michael.hines@yale.e) and Ruggero Scorcioni(rscorcio@gmu.edu) at EU Advance Course in Computational Neuroscience. Obidos, Portugal
+
+ca.mod
+Uses fixed eca instead of GHK eqn
+
+HVA Ca current
+Based on Reuveni, Friedman, Amitai and Gutnick (1993) J. Neurosci. 13:
+4609-4621.
+
+Author: Zach Mainen, Salk Institute, 1994, zach@salk.edu
+
+ENDCOMMENT
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+NEURON {
+	SUFFIX ca
+	USEION ca READ eca WRITE ica
+	RANGE m, h, gca, gbar
+	RANGE minf, hinf, mtau, htau
+	RANGE q10, temp, tadj, vmin, vmax, vshift
+}
+
+PARAMETER {
+	gbar = 0.1   	(pS/um2)	: 0.12 mho/cm2
+	vshift = 0	(mV)		: voltage shift (affects all)
+
+	cao  = 2.5	(mM)	        : external ca concentration
+	cai		(mM)
+						
+	temp = 23	(degC)		: original temp 
+	q10  = 2.3			: temperature sensitivity
+
+	v 		(mV)
+	dt		(ms)
+	celsius		(degC)
+	vmin = -120	(mV)
+	vmax = 100	(mV)
+}
+
+
+UNITS {
+	(mA) = (milliamp)
+	(mV) = (millivolt)
+	(pS) = (picosiemens)
+	(um) = (micron)
+	FARADAY = (faraday) (coulomb)
+	R = (k-mole) (joule/degC)
+	PI	= (pi) (1)
+} 
+
+ASSIGNED {
+	ica 		(mA/cm2)
+	gca		(pS/um2)
+	eca		(mV)
+	minf 		hinf
+	mtau (ms)	htau (ms)
+	tadj
+}
+ 
+
+STATE { m h }
+
+INITIAL { 
+	trates(v+vshift)
+	m = minf
+	h = hinf
+}
+
+BREAKPOINT {
+        SOLVE states METHOD cnexp
+        gca = tadj*gbar*m*m*h
+	ica = (1e-4) * gca * (v - eca)
+} 
+
+LOCAL mexp, hexp
+
+:PROCEDURE states() {
+:        trates(v+vshift)      
+:        m = m + mexp*(minf-m)
+:        h = h + hexp*(hinf-h)
+:	VERBATIM
+:	return 0;
+:	ENDVERBATIM
+:}
+
+DERIVATIVE states {
+        trates(v+vshift)      
+        m' =  (minf-m)/mtau
+        h' =  (hinf-h)/htau
+}
+
+PROCEDURE trates(v) {  
+                      
+        
+        : TABLE minf, hinf, mtau, htau 
+	: DEPEND  celsius, temp
+	:
+	: FROM vmin TO vmax WITH 199
+
+	rates(v): not consistently executed from here if usetable == 1
+
+:        tinc = -dt * tadj
+
+:        mexp = 1 - exp(tinc/mtau)
+:        hexp = 1 - exp(tinc/htau)
+}
+
+
+PROCEDURE rates(vm) {  
+        LOCAL  a, b
+
+        tadj = q10^((celsius - temp)/10)
+
+	a = 0.055*(-27 - vm)/(exp((-27-vm)/3.8) - 1)
+	b = 0.94*exp((-75-vm)/17)
+	
+	mtau = 1/tadj/(a+b)
+	minf = a/(a+b)
+
+		:"h" inactivation 
+
+	a = 0.000457*exp((-13-vm)/50)
+	b = 0.0065/(exp((-vm-15)/28) + 1)
+
+	htau = 1/tadj/(a+b)
+	hinf = a/(a+b)
+}
+
+FUNCTION efun(z) {
+	if (fabs(z) < 1e-4) {
+		efun = 1 - z/2
+	}else{
+		efun = z/(exp(z) - 1)
+	}
+}
diff --git a/netpyne/tutorials/mod/cal_mig.mod b/netpyne/tutorials/mod/cal_mig.mod
new file mode 100644
index 000000000..9069e8643
--- /dev/null
+++ b/netpyne/tutorials/mod/cal_mig.mod
@@ -0,0 +1,131 @@
+TITLE L-calcium channel
+: L-type calcium channel with [Ca]i inactivation
+: from Jaffe, D. B., Ross, W. N., Lisman, J. E., Laser-Ross, N., Miyakawa, H., and Johnston, D. A. A model for dendritic Ca2
+: accumulation in hippocampal pyramidal neurons based on fluorescence imaging measurements. J. Neurophysiol. 71:1O65-1077 1994.
+: conduction density estimate of 50-200 pS/mu2; 0.0025 S/cm2 (5-20 channels of 10 each)
+: M. Migliore, E. Cook, D.B. Jaffe, D.A. Turner and D. Johnston, Computer simulations of morphologically reconstructed CA3
+: hippocampal neurons, J. Neurophysiol. 73, 1157-1168 (1995). 
+: adapted from http://senselab.med.yale.edu/modeldb/ShowModel.asp?model=3263&file=\ca3_db\cal2.mod
+: this version from https://senselab.med.yale.edu/ModelDB/ShowModel.asp?model=148094&file=\kv72-R213QW-mutations\cal2.mod
+: Miceli F, Soldovieri MV, Ambrosino P, Barrese V, Migliore M, Cilio MR, Taglialatela M (2013) Genotype-phenotype
+: correlations in neonatal epilepsies caused by mutations in the voltage sensor of Kv7.2 potassium channel subunits. PNAS 110:4386-4391
+
+UNITS {
+  (mA) = (milliamp)
+  (mV) = (millivolt)
+
+  FARADAY = 96520 (coul)
+  R = 8.3134 (joule/degC)
+  KTOMV = .0853 (mV/degC)
+}
+
+PARAMETER {
+  v (mV)
+  celsius 	(degC)
+  gcalbar=.003 (mho/cm2)
+  ki=.001 (mM)
+  cai = 50.e-6 (mM)
+  cao = 2 (mM)
+  q10 = 5
+  mmin=0.2
+  tfa = 1
+  a0m =0.1
+  zetam = 2
+  vhalfm = 4
+  gmm=0.1	
+  USEGHK=1
+  erev = 100
+}
+
+
+NEURON {
+  SUFFIX cal
+  USEION ca READ cai,cao WRITE ica
+  RANGE gcalbar,cai, ica, gcal, ggk
+  RANGE minf,tau
+  GLOBAL USEGHK
+}
+
+STATE {
+  m
+}
+
+ASSIGNED {
+  ica (mA/cm2)
+  gcal (mho/cm2)
+  minf
+  tau   (ms)
+  ggk
+}
+
+INITIAL {
+  rate(v)
+  m = minf
+}
+
+BREAKPOINT {
+  SOLVE state METHOD cnexp
+  gcal = gcalbar*m*m*h2(cai)
+  if (USEGHK == 1) {
+    ggk=ghk(v,cai,cao)
+  } else {
+    ggk=v-erev
+  }
+  ica = gcal*ggk
+}
+
+FUNCTION h2(cai(mM)) {
+  h2 = ki/(ki+cai)
+}
+
+
+FUNCTION ghk(v(mV), ci(mM), co(mM)) (mV) {
+  LOCAL nu,f
+  f = KTF(celsius)/2
+  nu = v/f
+  ghk=-f*(1. - (ci/co)*exp(nu))*efun(nu)
+}
+
+FUNCTION KTF(celsius (DegC)) (mV) {
+  KTF = ((25./293.15)*(celsius + 273.15))
+}
+
+
+FUNCTION efun(z) {
+  if (fabs(z) < 1e-4) {
+    efun = 1 - z/2
+  }else{
+    efun = z/(exp(z) - 1)
+  }
+}
+
+FUNCTION alp(v(mV)) (1/ms) {
+  alp = 15.69*(-1.0*v+81.5)/(exp((-1.0*v+81.5)/10.0)-1.0)
+}
+
+FUNCTION bet(v(mV)) (1/ms) {
+  bet = 0.29*exp(-v/10.86)
+}
+
+FUNCTION alpmt(v(mV)) {
+  alpmt = exp(0.0378*zetam*(v-vhalfm)) 
+}
+
+FUNCTION betmt(v(mV)) {
+  betmt = exp(0.0378*zetam*gmm*(v-vhalfm)) 
+}
+
+DERIVATIVE state {  
+  rate(v)
+  m' = (minf - m)/tau
+}
+
+PROCEDURE rate(v (mV)) { :callable from hoc
+  LOCAL a, b, qt
+  qt=q10^((celsius-25)/10)
+  a = alp(v)
+  b = 1/((a + bet(v)))
+  minf = a*b
+  tau = betmt(v)/(qt*a0m*(1+alpmt(v)))
+  if (tau<mmin/qt) {tau=mmin/qt}
+}
diff --git a/netpyne/tutorials/mod/can_mig.mod b/netpyne/tutorials/mod/can_mig.mod
new file mode 100644
index 000000000..aa16f008d
--- /dev/null
+++ b/netpyne/tutorials/mod/can_mig.mod
@@ -0,0 +1,144 @@
+TITLE n-calcium channel
+: n-type calcium channel
+: MODELDB 126814 CA3 by Safiulina et al - http://senselab.med.yale.edu/modeldb/ShowModel.asp?model=126814
+: by Michele Migliore
+
+
+UNITS {
+	(mA) = (milliamp)
+	(mV) = (millivolt)
+
+	FARADAY = 96520 (coul)
+	R = 8.3134 (joule/degC)
+	KTOMV = .0853 (mV/degC)
+}
+
+PARAMETER {
+	v (mV)
+	celsius 		(degC)
+	gcanbar=.0003 (mho/cm2)
+	ki=.001 (mM)
+	cai=50.e-6 (mM)
+	cao = 2  (mM)
+	q10=5
+	mmin = 0.2
+	hmin = 3
+	a0m =0.03
+	zetam = 2
+	vhalfm = -14
+	gmm=0.1	
+        USEGHK=1
+        erev = 100
+}
+
+NEURON {
+	SUFFIX can
+	USEION ca READ cai,cao WRITE ica
+        RANGE gcanbar, ica, gcan       
+        RANGE hinf,minf,taum,tauh
+        GLOBAL USEGHK
+}
+
+STATE {
+	m h 
+}
+
+ASSIGNED {
+	ica (mA/cm2)
+        gcan  (mho/cm2) 
+        minf
+        hinf
+        taum
+        tauh
+}
+
+INITIAL {
+        rates(v)
+        m = minf
+        h = hinf
+}
+
+BREAKPOINT {
+	SOLVE states METHOD cnexp
+	gcan = gcanbar*m*m*h*h2(cai)
+        if (USEGHK == 1) {
+          ica = gcan*ghk(v,cai,cao)
+        } else {
+          ica = gcan*(v-erev)
+        }
+}
+
+UNITSOFF
+FUNCTION h2(cai(mM)) {
+	h2 = ki/(ki+cai)
+}
+
+
+FUNCTION ghk(v(mV), ci(mM), co(mM)) (mV) {
+        LOCAL nu,f
+
+        f = KTF(celsius)/2
+        nu = v/f
+        ghk=-f*(1. - (ci/co)*exp(nu))*efun(nu)
+}
+
+FUNCTION KTF(celsius (degC)) (mV) {
+        KTF = ((25./293.15)*(celsius + 273.15))
+}
+
+
+FUNCTION efun(z) {
+	if (fabs(z) < 1e-4) {
+		efun = 1 - z/2
+	}else{
+		efun = z/(exp(z) - 1)
+	}
+}
+
+FUNCTION alph(v(mV)) {
+	alph = 1.6e-4*exp(-v/48.4)
+}
+
+FUNCTION beth(v(mV)) {
+	beth = 1/(exp((-v+39.0)/10.)+1.)
+}
+
+FUNCTION alpm(v(mV)) {
+	alpm = 0.1967*(-1.0*v+19.88)/(exp((-1.0*v+19.88)/10.0)-1.0)
+}
+
+FUNCTION betm(v(mV)) {
+	betm = 0.046*exp(-v/20.73)
+}
+
+FUNCTION alpmt(v(mV)) {
+  alpmt = exp(0.0378*zetam*(v-vhalfm)) 
+}
+
+FUNCTION betmt(v(mV)) {
+  betmt = exp(0.0378*zetam*gmm*(v-vhalfm)) 
+}
+
+UNITSON
+
+DERIVATIVE states {     : exact when v held constant; integrates over dt step
+        rates(v)
+        m' = (minf - m)/taum
+        h' = (hinf - h)/tauh
+}
+
+PROCEDURE rates(v (mV)) { :callable from hoc
+        LOCAL a, b, qt
+        qt=q10^((celsius-25)/10)
+        a = alpm(v)
+        b = 1/(a + betm(v))
+        minf = a*b
+	taum = betmt(v)/(qt*a0m*(1+alpmt(v)))
+	if (taum<mmin/qt) {taum=mmin/qt}
+        a = alph(v)
+        b = 1/(a + beth(v))
+        hinf = a*b
+:	tauh=b/qt
+	tauh= 80
+	if (tauh<hmin) {tauh=hmin}
+}
diff --git a/netpyne/tutorials/mod/canin.mod b/netpyne/tutorials/mod/canin.mod
new file mode 100755
index 000000000..aa9a6bdf9
--- /dev/null
+++ b/netpyne/tutorials/mod/canin.mod
@@ -0,0 +1,127 @@
+TITLE N-type calcium channel 
+: used in somatic and dendritic regions 
+: After Borg 
+:  Updated by Maria Markaki  03/12/03
+
+NEURON {
+	SUFFIX canin
+	USEION ca READ cai, eca WRITE ica 
+        RANGE gcalbar, ica, po
+	:GLOBAL hinf, minf, s_inf
+}
+
+UNITS {
+	(mA) = (milliamp)
+	(mV) = (millivolt)
+	(molar) = (1/liter)
+	(mM) =	(millimolar)
+	FARADAY = (faraday) (coulomb)
+	R = (k-mole) (joule/degC)
+}
+
+PARAMETER {           :parameters that can be entered when function is called in cell-setup 
+	gcalbar = 0   (mho/cm2)  : initialized conductance
+  	ki     = 0.025  (mM)            :test middle point of inactivation fct
+	zetam = -3.4
+	zetah = 2
+	vhalfm =-21 (mV)
+	vhalfh =-40 (mV)
+	tm0=1.5(ms)
+	th0=75(ms)
+	taumin  = 2    (ms)            : minimal value of the time cst
+}
+
+
+
+ASSIGNED {     : parameters needed to solve DE
+	v            (mV)
+	celsius      (degC)
+	ica          (mA/cm2)
+	po
+	cai          (mM)       :5e-5 initial internal Ca++ concentration
+	eca             (mV)
+        minf
+        hinf
+	s_inf
+}
+
+
+FUNCTION h2(cai(mM)) {
+	h2 = ki/(ki+cai)
+}
+
+
+
+STATE {	
+	m 
+	h 
+	s
+}  
+
+INITIAL {
+	rates(v,cai)
+        m = minf
+        h = hinf
+	s = s_inf
+}
+
+BREAKPOINT {
+	SOLVE states METHOD cnexp
+	po = m*m*h
+ 	ica = gcalbar *po*h2(cai) * (v - eca)
+
+}
+
+
+FUNCTION ghk(v(mV), ci(mM), co(mM)) (.001 coul/cm3) {
+	LOCAL z, eci, eco
+	z = (1e-3)*2*FARADAY*v/(R*(celsius+273.15))
+	eco = co*efun(z)
+	eci = ci*efun(-z)
+	ghk = (.001)*2*FARADAY*(eci - eco)
+}
+
+FUNCTION efun(z) {
+	if (fabs(z) < 1e-4) {
+		efun = 1 - z/2
+	}else{
+		efun = z/(exp(z) - 1)
+	}
+}
+
+DERIVATIVE states {
+	rates(v,cai)
+	m' = (minf -m)/tm0
+	h'=  (hinf - h)/th0
+	s' = (s_inf-s)/taumin
+}
+
+
+
+PROCEDURE rates(v (mV), cai(mM)) { 
+        LOCAL a, b, alpha2
+        
+	a = alpm(v)
+	minf = 1/(1+a)
+        
+        b = alph(v)
+	hinf = 1/(1+b)
+	alpha2 = (ki/cai)^2
+	s_inf = alpha2 / (alpha2 + 1)
+}
+
+
+
+
+FUNCTION alpm(v(mV)) {
+UNITSOFF
+  alpm = exp(1.e-3*zetam*(v-vhalfm)*9.648e4/(8.315*(273.16+celsius))) 
+UNITSON
+}
+
+FUNCTION alph(v(mV)) {
+UNITSOFF
+  alph = exp(1.e-3*zetah*(v-vhalfh)*9.648e4/(8.315*(273.16+celsius))) 
+UNITSON
+}
+
diff --git a/netpyne/tutorials/mod/cat.mod b/netpyne/tutorials/mod/cat.mod
new file mode 100644
index 000000000..36d2b343b
--- /dev/null
+++ b/netpyne/tutorials/mod/cat.mod
@@ -0,0 +1,67 @@
+TITLE Calcium low threshold T type current for RD Traub, J Neurophysiol 89:909-921, 2003
+
+COMMENT
+    Implemented by Maciej Lazarewicz 2003 (mlazarew@seas.upenn.edu)
+ENDCOMMENT
+
+INDEPENDENT { t FROM 0 TO 1 WITH 1 (ms) }
+
+UNITS {
+    (mV) = (millivolt)
+    (mA) = (milliamp)
+}
+
+NEURON {
+    SUFFIX cat_hnn
+    NONSPECIFIC_CURRENT i   : not causing [Ca2+] influx
+    RANGE gbar, i
+}
+
+PARAMETER {
+    gbar = 0.0  (mho/cm2)
+    v eca       (mV)
+}
+
+ASSIGNED {
+    i           (mA/cm2)
+    minf hinf   (1)
+    mtau htau   (ms)
+}
+
+STATE {
+    m h
+}
+
+BREAKPOINT {
+    SOLVE states METHOD cnexp
+    i = gbar * m * m * h * ( v - 125 )
+}
+
+INITIAL {
+    settables(v)
+    m  = minf
+    h  = hinf
+    m  = 0
+}
+
+DERIVATIVE states {
+    settables(v)
+    m' = ( minf - m ) / mtau
+    h' = ( hinf - h ) / htau
+}
+
+UNITSOFF
+PROCEDURE settables(v) {
+    TABLE minf, mtau, hinf, htau FROM -120 TO 40 WITH 641
+
+    minf  = 1 / (1 + exp(( -v - 56 ) / 6.2))
+    mtau  = 0.204 + 0.333 / (exp(( v + 15.8) / 18.2) + exp((-v - 131) / 16.7))
+    hinf  = 1 / (1 + exp((v + 80) / 4))
+
+    if (v < -81) {
+        htau  = 0.333 * exp((v + 466 ) / 66.6)
+    } else {
+        htau  = 9.32 + 0.333 * exp((-v - 21) / 10.5)
+    }
+}
+UNITSON
diff --git a/netpyne/tutorials/mod/cat_mig.mod b/netpyne/tutorials/mod/cat_mig.mod
new file mode 100644
index 000000000..57e8d0922
--- /dev/null
+++ b/netpyne/tutorials/mod/cat_mig.mod
@@ -0,0 +1,136 @@
+TITLE T-calcium channel
+: T-type calcium channel
+: MODELDB 126814 CA3 by Safiulina et al - http://senselab.med.yale.edu/modeldb/ShowModel.asp?model=126814
+: by Michele Migliore
+
+
+UNITS {
+	(mA) = (milliamp)
+	(mV) = (millivolt)
+	(molar) = (1/liter)
+	(mM) = (millimolar)
+
+	FARADAY = 96520 (coul)
+	R = 8.3134 (joule/degC)
+	KTOMV = .0853 (mV/degC)
+}
+
+PARAMETER {
+	v (mV)
+	celsius = 25	(degC)
+	gcatbar=.003 (mho/cm2)
+	cai = 50.e-6 (mM)
+	cao = 2 (mM)
+	q10 = 5
+	mmin=0.2
+	hmin=10
+	a0h =0.015
+	zetah = 3.5
+	vhalfh = -75
+	gmh=0.6	
+	a0m =0.04
+	zetam = 2
+	vhalfm = -28
+	gmm=0.1	
+        USEGHK=1
+        erev = 100
+}
+
+NEURON {
+	SUFFIX cat
+	USEION ca READ cai,cao WRITE ica
+        RANGE gcatbar, ica, gcat
+        RANGE hinf,minf,mtau,htau
+        GLOBAL USEGHK
+}
+
+STATE {
+	m h 
+}
+
+ASSIGNED {
+	ica (mA/cm2)
+        gcat (mho/cm2)
+	hinf
+	htau
+	minf
+	mtau
+}
+
+INITIAL {
+	rates(v)
+	m = minf
+	h = hinf
+}
+
+BREAKPOINT {
+	SOLVE states METHOD cnexp
+	gcat = gcatbar*m*m*h
+        if (USEGHK == 1) {
+  	  ica = gcat*ghk(v,cai,cao)
+        } else {
+          ica = gcat*(v-erev)
+        }
+}
+
+DERIVATIVE states {	: exact when v held constant
+	rates(v)
+	m' = (minf - m)/mtau
+	h' = (hinf - h)/htau
+}
+
+
+FUNCTION ghk(v(mV), ci(mM), co(mM)) (mV) {
+        LOCAL nu,f
+
+        f = KTF(celsius)/2
+        nu = v/f
+        ghk=-f*(1. - (ci/co)*exp(nu))*efun(nu)
+}
+
+FUNCTION KTF(celsius (DegC)) (mV) {
+        KTF = ((25./293.15)*(celsius + 273.15))
+}
+
+
+FUNCTION efun(z) {
+	if (fabs(z) < 1e-4) {
+		efun = 1 - z/2
+	}else{
+		efun = z/(exp(z) - 1)
+	}
+}
+
+FUNCTION alph(v(mV)) {
+  alph = exp(0.0378*zetah*(v-vhalfh)) 
+}
+
+FUNCTION beth(v(mV)) {
+  beth = exp(0.0378*zetah*gmh*(v-vhalfh)) 
+}
+
+FUNCTION alpmt(v(mV)) {
+  alpmt = exp(0.0378*zetam*(v-vhalfm)) 
+}
+
+FUNCTION betmt(v(mV)) {
+  betmt = exp(0.0378*zetam*gmm*(v-vhalfm)) 
+}
+
+PROCEDURE rates(v (mV)) { :callable from hoc
+	LOCAL a,b, qt
+        qt=q10^((celsius-25)/10)
+
+	a = 0.2*(-1.0*v+19.26)/(exp((-1.0*v+19.26)/10.0)-1.0)
+	b = 0.009*exp(-v/22.03)
+	minf = a/(a+b)
+	mtau = betmt(v)/(qt*a0m*(1+alpmt(v)))
+	if (mtau<mmin) {mtau=mmin}
+
+	a = 1.e-6*exp(-v/16.26)
+	b = 1/(exp((-v+29.79)/10.)+1.)
+	hinf = a/(a+b)
+	htau = beth(v)/(qt*a0h*(1+alph(v)))
+	if (htau<hmin) {htau=hmin}
+}
+
diff --git a/netpyne/tutorials/mod/cat_traub.mod b/netpyne/tutorials/mod/cat_traub.mod
new file mode 100644
index 000000000..b50844e7e
--- /dev/null
+++ b/netpyne/tutorials/mod/cat_traub.mod
@@ -0,0 +1,67 @@
+TITLE Calcium low threshold T type current for RD Traub, J Neurophysiol 89:909-921, 2003
+
+COMMENT
+
+	Implemented by Maciej Lazarewicz 2003 (mlazarew@seas.upenn.edu)
+
+ENDCOMMENT
+
+INDEPENDENT { t FROM 0 TO 1 WITH 1 (ms) }
+
+UNITS { 
+	(mV) = (millivolt) 
+	(mA) = (milliamp) 
+}
+ 
+NEURON { 
+	SUFFIX catt
+	NONSPECIFIC_CURRENT i   : not causing [Ca2+] influx
+	RANGE gbar, i
+}
+
+PARAMETER { 
+	gbar = 0.0 	(mho/cm2)
+	v eca 		(mV)  
+}
+ 
+ASSIGNED { 
+	i 		(mA/cm2) 
+	minf hinf 	(1)
+	mtau htau 	(ms) 
+}
+ 
+STATE {
+	m h
+}
+
+BREAKPOINT { 
+	SOLVE states METHOD cnexp
+	i = gbar * m * m * h * ( v - 125 ) 
+}
+ 
+INITIAL { 
+	minf  = 1 / ( 1 + exp( ( -v - 56 ) / 6.2 ) )
+	mtau  = 0.204 + 0.333 / ( exp( ( v + 15.8 ) / 18.2 ) + exp( ( - v - 131 ) / 16.7 ) )
+	hinf  = 1 / ( 1 + exp( ( v + 80 ) / 4 ) )
+	if( v < -81 ) {
+		htau  = 0.333 * exp( ( v + 466 ) / 66.6 )
+	}else{
+		htau  = 9.32 + 0.333 * exp( ( -v - 21 ) / 10.5 )
+	}
+	m  = minf
+	h  = hinf
+} 
+
+DERIVATIVE states { 
+	minf  = 1 / ( 1 + exp( ( -v - 56 ) / 6.2 ) )
+	mtau  = 0.204 + 0.333 / ( exp( ( v + 15.8 ) / 18.2 ) + exp( ( - v - 131 ) / 16.7 ) )
+	hinf  = 1 / ( 1 + exp( ( v + 80 ) / 4 ) )
+	if( v < -81 ) {
+		htau  = 0.333 * exp( ( v + 466 ) / 66.6 )
+	}else{
+		htau  = 9.32 + 0.333 * exp( ( -v - 21 ) / 10.5 )
+	}
+	m' = ( minf - m ) / mtau 
+	h' = ( hinf - h ) / htau
+}
+
diff --git a/netpyne/tutorials/mod/catcb.mod b/netpyne/tutorials/mod/catcb.mod
new file mode 100755
index 000000000..303d13400
--- /dev/null
+++ b/netpyne/tutorials/mod/catcb.mod
@@ -0,0 +1,109 @@
+TITLE t-type calcium channel with high threshold for activation
+: used in somatic and dendritic regions 
+: Updated to use CVode --Carl Gold 08/10/03
+
+
+NEURON {
+	SUFFIX catcb
+	USEION ca READ cai, eca    
+        RANGE gcatbar, iCa
+        RANGE gcatbar, ica
+	GLOBAL hinf, minf
+}
+
+UNITS {
+	(mA) = (milliamp)
+	(mV) = (millivolt)
+	(molar) = (1/liter)
+	(mM) =	(millimolar)
+	FARADAY = (faraday) (coulomb)
+	R = (k-mole) (joule/degC)
+}
+
+PARAMETER {           
+	gcatbar = 0   (mho/cm2)  
+	zetam = -3
+	zetah = 5.2
+	vhalfm =-36 (mV)
+	vhalfh =-68 (mV)
+	tm0=1.5(ms)
+	th0=10(ms)
+}
+
+
+
+ASSIGNED {     : parameters needed to solve DE
+	v            (mV)
+	celsius      (degC)
+	ica          (mA/cm2)
+	cai          (mM)       
+	eca          (mV)       
+        minf
+        hinf
+}
+
+
+STATE {	
+	m 
+	h 
+}  
+
+INITIAL {
+	rates(v)
+        m = minf
+        h = hinf
+}
+
+BREAKPOINT {
+	SOLVE states METHOD cnexp
+	ica = gcatbar*m*m*h*(v-eca)	
+}
+
+FUNCTION ghk(v(mV), ci(mM), co(mM)) (.001 coul/cm3) {
+	LOCAL z, eci, eco
+	z = (1e-3)*2*FARADAY*v/(R*(celsius+273.15))
+	eco = co*efun(z)
+	eci = ci*efun(-z)
+	ghk = (.001)*2*FARADAY*(eci - eco)
+}
+
+FUNCTION efun(z) {
+	if (fabs(z) < 1e-4) {
+		efun = 1 - z/2
+	}else{
+		efun = z/(exp(z) - 1)
+	}
+}
+
+
+DERIVATIVE states {
+	rates(v)
+	m' = (minf -m)/tm0
+	h'=  (hinf - h)/th0
+}
+
+
+PROCEDURE rates(v (mV)) { 
+        LOCAL a, b
+        
+	a = alpm(v)
+	minf = 1/(1+a)
+        
+        b = alph(v)
+	hinf = 1/(1+b)
+}
+
+
+
+FUNCTION alpm(v(mV)) {
+UNITSOFF
+  alpm = exp(1.e-3*zetam*(v-vhalfm)*9.648e4/(8.315*(273.16+celsius))) 
+UNITSON
+}
+
+FUNCTION alph(v(mV)) {
+UNITSOFF
+  alph = exp(1.e-3*zetah*(v-vhalfh)*9.648e4/(8.315*(273.16+celsius))) 
+UNITSON
+}
+
diff --git a/netpyne/tutorials/mod/dipole.mod b/netpyne/tutorials/mod/dipole.mod
new file mode 100644
index 000000000..06c0c5e4a
--- /dev/null
+++ b/netpyne/tutorials/mod/dipole.mod
@@ -0,0 +1,52 @@
+: dipole.mod - mod file for range variable dipole
+:
+: v 1.9.1m0
+: rev 2015-12-15 (SL: minor)
+: last rev: (SL: Added back Qtotal, which WAS used in par version)
+
+NEURON {
+    SUFFIX dipole
+    RANGE ri, ia, Q, ztan
+    POINTER pv
+
+    : for density. sums into Dipole at section position 1
+    POINTER Qsum
+    POINTER Qtotal
+}
+
+UNITS {
+    (nA)   = (nanoamp)
+    (mV)   = (millivolt)
+    (Mohm) = (megaohm)
+    (um)   = (micrometer)
+    (Am)   = (amp meter)
+    (fAm)  = (femto amp meter)
+}
+
+ASSIGNED {
+    ia (nA)
+    ri (Mohm)
+    pv (mV)
+    v (mV)
+    ztan (um)
+    Q (fAm)
+
+    : human dipole order of 10 nAm
+    Qsum (fAm)
+    Qtotal (fAm)
+}
+
+: solve for v's first then use them
+AFTER SOLVE {
+    ia = (pv - v) / ri
+    Q = ia * ztan
+    Qsum = Qsum + Q
+    Qtotal = Qtotal + Q
+}
+
+AFTER INITIAL {
+    ia = (pv - v) / ri
+    Q = ia * ztan
+    Qsum = Qsum + Q
+    Qtotal = Qtotal + Q
+}
diff --git a/netpyne/tutorials/mod/dipole_pp.mod b/netpyne/tutorials/mod/dipole_pp.mod
new file mode 100644
index 000000000..5a9179e18
--- /dev/null
+++ b/netpyne/tutorials/mod/dipole_pp.mod
@@ -0,0 +1,61 @@
+: dipole_pp.mod - creates point process mechanism Dipole
+:
+: v 1.9.1m0
+: rev 2015-12-15 (SL: minor)
+: last rev: (SL: added Qtotal back, used for par calc)
+
+NEURON {
+    POINT_PROCESS Dipole
+    RANGE ri, ia, Q, ztan
+    POINTER pv
+
+    : for POINT_PROCESS. Gets additions from dipole
+    RANGE Qsum
+    POINTER Qtotal
+}
+
+UNITS {
+    (nA)   = (nanoamp)
+    (mV)   = (millivolt)
+    (Mohm) = (megaohm)
+    (um)   = (micrometer)
+    (Am)   = (amp meter)
+    (fAm)  = (femto amp meter)
+}
+
+ASSIGNED {
+    ia (nA)
+    ri (Mohm)
+    pv (mV)
+    v (mV)
+    ztan (um)
+    Q (fAm)
+    Qsum (fAm)
+    Qtotal (fAm)
+}
+
+: solve for v's first then use them
+AFTER SOLVE {
+    ia = (pv - v) / ri
+    Q = ia * ztan
+    Qsum = Qsum + Q
+    Qtotal = Qtotal + Q
+}
+
+AFTER INITIAL {
+    ia = (pv - v) / ri
+    Q = ia * ztan
+    Qsum = Qsum + Q
+    Qtotal = Qtotal + Q
+}
+
+: following needed for POINT_PROCESS only but will work if also in SUFFIX
+BEFORE INITIAL {
+    Qsum = 0
+    Qtotal = 0
+}
+
+BEFORE BREAKPOINT {
+    Qsum = 0
+    Qtotal = 0
+}
diff --git a/netpyne/tutorials/mod/expsynstdp.mod b/netpyne/tutorials/mod/expsynstdp.mod
new file mode 100644
index 000000000..6dcad99e4
--- /dev/null
+++ b/netpyne/tutorials/mod/expsynstdp.mod
@@ -0,0 +1,73 @@
+: $Id: expsynstdp.mod,v 1.3 2012/02/17 15:45:45 samn Exp $ 
+:
+: basic STDP exponential synapse
+: from http://www.neuron.yale.edu/neuron/static/news/stdp.mod
+:
+
+NEURON {
+	POINT_PROCESS ExpSynSTDP
+	RANGE tau, e, i, d, p, dtau, ptau
+	NONSPECIFIC_CURRENT i
+        GLOBAL verbose
+}
+
+UNITS {
+	(nA) = (nanoamp)
+	(mV) = (millivolt)
+	(uS) = (microsiemens)
+}
+
+PARAMETER {
+	tau = 0.1 (ms) <1e-9,1e9>
+	e = 0	(mV)
+	d = 0 <0,1>: depression factor (multiplicative to prevent < 0)
+	p = 0 : potentiation factor (additive, non-saturating)
+	dtau = 34 (ms) : depression effectiveness time constant
+	ptau = 17 (ms) : Bi & Poo (1998, 2001)
+        verbose = 0 
+}
+
+ASSIGNED {
+	v (mV)
+	i (nA)
+	tpost (ms)
+}
+
+STATE {
+	g (uS)
+}
+
+INITIAL {
+	g=0
+	tpost = -1e9
+	net_send(0, 1)
+}
+
+BREAKPOINT {
+	SOLVE state METHOD cnexp
+	i = g*(v - e)
+}
+
+DERIVATIVE state {
+	g' = -g/tau
+}
+
+NET_RECEIVE(w (uS), A, tpre (ms)) {
+	INITIAL { A = 0  tpre = -1e9 }
+	if (flag == 0) { : presynaptic spike  (after last post so depress)
+          if(verbose) {printf("entry flag=%g t=%g w=%g A=%g tpre=%g tpost=%g\n", flag, t, w, A, tpre, tpost)}
+		g = g + w*(1 + A)
+		tpre = t
+		A = A * (1 - d*exp((tpost - t)/dtau))
+	}else if (flag == 2) { : postsynaptic spike
+          if(verbose) {printf("entry flag=%g t=%g tpost=%g\n", flag, t, tpost)}
+		tpost = t
+		FOR_NETCONS(w1, A1, tp) { : also can hide NET_RECEIVE args
+                  if(verbose) {printf("entry FOR_NETCONS w1=%g A1=%g tp=%g\n", w1, A1, tp)}
+			A1 = A1 + p*exp((tp - t)/ptau)
+		}
+	} else { : flag == 1 from INITIAL block
+          if(verbose) {printf("entry flag=%g t=%g\n", flag, t)}
+		WATCH (v > -20) 2
+	}
+}
diff --git a/netpyne/tutorials/mod/gabab.mod b/netpyne/tutorials/mod/gabab.mod
new file mode 100644
index 000000000..768744b47
--- /dev/null
+++ b/netpyne/tutorials/mod/gabab.mod
@@ -0,0 +1,210 @@
+: $Id: gabab.mod,v 1.9 2004/06/17 16:04:05 billl Exp $
+
+COMMENT
+-----------------------------------------------------------------------------
+
+	Kinetic model of GABA-B receptors
+	=================================
+
+  MODEL OF SECOND-ORDER G-PROTEIN TRANSDUCTION AND FAST K+ OPENING
+  WITH COOPERATIVITY OF G-PROTEIN BINDING TO K+ CHANNEL
+
+  PULSE OF TRANSMITTER
+
+  SIMPLE KINETICS WITH NO DESENSITIZATION
+
+	Features:
+
+  	  - peak at 100 ms; time course fit to Tom Otis' PSC
+	  - SUMMATION (psc is much stronger with bursts)
+
+
+	Approximations:
+
+	  - single binding site on receptor	
+	  - model of alpha G-protein activation (direct) of K+ channel
+	  - G-protein dynamics is second-order; simplified as follows:
+		- saturating receptor
+		- no desensitization
+		- Michaelis-Menten of receptor for G-protein production
+		- "resting" G-protein is in excess
+		- Quasi-stat of intermediate enzymatic forms
+	  - binding on K+ channel is fast
+
+
+	Kinetic Equations:
+
+	  dR/dt = K1 * T * (1-R-D) - K2 * R
+
+	  dG/dt = K3 * R - K4 * G
+
+	  R : activated receptor
+	  T : transmitter
+	  G : activated G-protein
+	  K1,K2,K3,K4 = kinetic rate cst
+
+  n activated G-protein bind to a K+ channel:
+
+	n G + C <-> O		(Alpha,Beta)
+
+  If the binding is fast, the fraction of open channels is given by:
+
+	O = G^n / ( G^n + KD )
+
+  where KD = Beta / Alpha is the dissociation constant
+
+-----------------------------------------------------------------------------
+
+  Parameters estimated from patch clamp recordings of GABAB PSP's in
+  rat hippocampal slices (Otis et al, J. Physiol. 463: 391-407, 1993).
+
+-----------------------------------------------------------------------------
+
+  PULSE MECHANISM
+
+  Kinetic synapse with release mechanism as a pulse.  
+
+  Warning: for this mechanism to be equivalent to the model with diffusion 
+  of transmitter, small pulses must be used...
+
+  For a detailed model of GABAB:
+
+  Destexhe, A. and Sejnowski, T.J.  G-protein activation kinetics and
+  spill-over of GABA may account for differences between inhibitory responses
+  in the hippocampus and thalamus.  Proc. Natl. Acad. Sci. USA  92:
+  9515-9519, 1995.
+
+  For a review of models of synaptic currents:
+
+  Destexhe, A., Mainen, Z.F. and Sejnowski, T.J.  Kinetic models of 
+  synaptic transmission.  In: Methods in Neuronal Modeling (2nd edition; 
+  edited by Koch, C. and Segev, I.), MIT press, Cambridge, 1996.
+
+  This simplified model was introduced in:
+
+  Destexhe, A., Bal, T., McCormick, D.A. and Sejnowski, T.J.
+  Ionic mechanisms underlying synchronized oscillations and propagating
+  waves in a model of ferret thalamic slices. Journal of Neurophysiology
+  76: 2049-2070, 1996.  
+
+  See also http://www.cnl.salk.edu/~alain
+
+
+
+  Alain Destexhe, Salk Institute and Laval University, 1995
+
+-----------------------------------------------------------------------------
+ENDCOMMENT
+
+
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+NEURON {
+	POINT_PROCESS GABAB
+	RANGE R, G, g
+	NONSPECIFIC_CURRENT i
+	GLOBAL Cmax, Cdur
+	GLOBAL K1, K2, K3, K4, KD, Erev, warn, cutoff
+}
+UNITS {
+	(nA) = (nanoamp)
+	(mV) = (millivolt)
+	(umho) = (micromho)
+	(mM) = (milli/liter)
+}
+
+PARAMETER {
+
+	Cmax	= 0.5	(mM)		: max transmitter concentration
+	Cdur	= 0.3	(ms)		: transmitter duration (rising phase)
+:
+:	From Kfit with long pulse (5ms 0.5mM)
+:
+	K1	= 0.52	(/ms mM)	: forward binding rate to receptor
+	K2	= 0.0013 (/ms)		: backward (unbinding) rate of receptor
+	K3	= 0.098 (/ms)		: rate of G-protein production
+	K4	= 0.033 (/ms)		: rate of G-protein decay
+	KD	= 100			: dissociation constant of K+ channel
+	n	= 4			: nb of binding sites of G-protein on K+
+	Erev	= -95	(mV)		: reversal potential (E_K)
+	warn	= 0			: too large G warning has/has not been issued
+        cutoff = 1e12
+}
+
+
+ASSIGNED {
+	v		(mV)		: postsynaptic voltage
+	i 		(nA)		: current = g*(v - Erev)
+	g 		(umho)		: conductance
+	Gn
+	R				: fraction of activated receptor
+	edc
+	synon
+	Rinf
+	Rtau (ms)
+	Beta (/ms)
+}
+
+STATE {
+	Ron Roff
+	G				: fraction of activated G-protein
+}
+
+
+INITIAL {
+	R = 0
+	G = 0
+	Ron = 0
+	Roff = 0
+	synon = 0
+	Rinf = K1*Cmax/(K1*Cmax + K2)
+	Rtau = 1/(K1*Cmax + K2)
+	Beta = K2
+
+}
+
+BREAKPOINT {
+	SOLVE bindkin METHOD derivimplicit
+	if (G < cutoff) {
+		Gn = G*G*G*G : ^n = 4
+		g = Gn / (Gn+KD)
+	} else {
+		if(!warn){
+			printf("gabab.mod WARN: G = %g too large\n", G)		
+			warn = 1
+		}
+		g = 1
+	}
+	i = g*(v - Erev)
+}
+
+
+DERIVATIVE bindkin {
+	Ron' = synon*K1*Cmax - (K1*Cmax + K2)*Ron
+	Roff' = -K2*Roff
+	R = Ron + Roff
+	G' = K3 * R - K4 * G
+}
+
+: following supports both saturation from single input and
+: summation from multiple inputs
+: Note: automatic initialization of all reference args to 0 except first
+
+NET_RECEIVE(weight,  r0, t0 (ms)) {
+	if (flag == 1) { : at end of Cdur pulse so turn off
+		r0 = weight*(Rinf + (r0 - Rinf)*exp(-(t - t0)/Rtau))
+		t0 = t
+		synon = synon - weight
+		state_discontinuity(Ron, Ron - r0)
+		state_discontinuity(Roff, Roff + r0)
+        }else{ : at beginning of Cdur pulse so turn on
+		r0 = weight*r0*exp(-Beta*(t - t0))
+		t0 = t
+		synon = synon + weight
+		state_discontinuity(Ron, Ron + r0)
+		state_discontinuity(Roff, Roff - r0)
+		:come again in Cdur
+		net_send(Cdur, 1)
+        }
+}
diff --git a/netpyne/tutorials/mod/ghk.inc b/netpyne/tutorials/mod/ghk.inc
new file mode 100644
index 000000000..95babfc11
--- /dev/null
+++ b/netpyne/tutorials/mod/ghk.inc
@@ -0,0 +1,40 @@
+COMMENT
+    GHK function that returns effective driving force
+    Slope at low voltages is 1
+    z needs to be set as a PARAMETER
+ENDCOMMENT
+
+FUNCTION ghkg(v(mV), ci(mM), co(mM), z) (mV) {
+    LOCAL xi, f, exi, fxi
+    f = R*(celsius+273.15)/(z*(1e-3)*FARADAY)
+    xi = v/f
+    exi = exp(xi)
+    if (fabs(xi) < 1e-4) {
+        fxi = 1 - xi/2
+    }else{
+        fxi = xi/(exi - 1)
+    }
+    ghkg = f*((ci/co)*exi - 1)*fxi
+}
+
+FUNCTION ghk(v(mV), ci(mM), co(mM), z) (.001 coul/cm3) {
+    LOCAL xi, f, exi, fxi
+    f = R*(celsius+273.15)/(z*(1e-3)*FARADAY)
+    xi = v/f
+    exi = exp(xi)
+    if (fabs(xi) < 1e-4) {
+        fxi = 1 - xi/2
+    }else{
+        fxi = xi/(exi - 1)
+    }
+    ghk = (.001)*z*FARADAY*(ci*exi - co)*fxi
+}
+
+
+
+
+
+
+
+
+
diff --git a/netpyne/tutorials/mod/h_BS.mod b/netpyne/tutorials/mod/h_BS.mod
new file mode 100644
index 000000000..eeca578ac
--- /dev/null
+++ b/netpyne/tutorials/mod/h_BS.mod
@@ -0,0 +1,88 @@
+TITLE I-h channel from Magee 1998 for distal dendrites
+: modified to take into account Sonia's exp. Apr.2008 M.Migliore
+: thread-safe 2010-05-18 Ben Suter
+: 2010-11-07 Ben Suter, removing "hd" from parameter names, changing suffix from "hd" to "h"
+: Parameters fit to pre-ZD current-clamp step responses from experiment BS0284 (traces and reconstruction from single corticospinal neuron)
+: 2011-09-18 Ben Suter, set default parameter values to those found from MRF optimization for BS0284 model
+:
+: :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
+: Copyright 2011, Benjamin Suter (for changes only)
+: Used in model of corticospinal neuron BS0284 and published as:
+:  "Intrinsic electrophysiology of mouse corticospinal neurons: a characteristic set of features embodied in a realistic computational model"
+:  by Benjamin Suter, Michele Migliore, and Gordon Shepherd
+:  Submitted September 2011
+: :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
+
+
+UNITS {
+    (mA) = (milliamp)
+    (mV) = (millivolt)
+}
+
+PARAMETER {
+    v                       (mV)
+    celsius  = 34.0    (degC)
+    erev     = -37.0               (mV)
+    gbar     = 0.0001       (mho/cm2)
+    vhalfl   = -78.474      (mV)    : was -81
+    kl       = -6                   : was -8
+    vhalft   = -66.139      (mV)    : was -62
+    a0t      = 0.009        (/ms)   : was 0.0077696
+    zetat    = 20           (1)     : was 5
+    gmt      = 0.01         (1)     : was 0.057127
+    q10      = 4.5
+    qtl      = 1
+    taumin	= 2.0	(ms)		: minimal value of time constant
+}
+
+NEURON {
+    SUFFIX h
+    NONSPECIFIC_CURRENT i
+    RANGE gbar, vhalfl
+    RANGE linf, taul, g
+    GLOBAL taumin
+}
+
+STATE {
+    l
+}
+
+ASSIGNED {
+    i       (mA/cm2)
+    linf
+    taul
+    g
+}
+
+INITIAL {
+    rate(v)
+    l       = linf
+}
+
+BREAKPOINT {
+    SOLVE states METHOD cnexp
+    g       = gbar*l
+    i       = g*(v-erev)
+}
+
+FUNCTION alpt(v(mV)) {
+    alpt    = exp(0.0378*zetat*(v-vhalft))
+}
+
+FUNCTION bett(v(mV)) {
+    bett    = exp(0.0378*zetat*gmt*(v-vhalft))
+}
+
+DERIVATIVE states {     : exact when v held constant; integrates over dt step
+    rate(v)
+    l'      = (linf - l)/taul
+}
+
+PROCEDURE rate(v (mV)) { :callable from hoc
+    LOCAL a,qt
+    qt      = q10^((celsius-33)/10)
+    a       = alpt(v)
+    linf    = 1/(1 + exp(-(v-vhalfl)/kl))
+    taul    = bett(v)/(qtl*qt*a0t*(1+a)) + 1e-8 
+    if(taul < taumin) { taul = taumin } 	: min value of time constant
+}
diff --git a/netpyne/tutorials/mod/h_harnett.mod b/netpyne/tutorials/mod/h_harnett.mod
new file mode 100644
index 000000000..84b0c64f7
--- /dev/null
+++ b/netpyne/tutorials/mod/h_harnett.mod
@@ -0,0 +1,69 @@
+TITLE I-h channel from Harnett 2015 - J Neurosci
+
+UNITS {
+    (mA) = (milliamp)
+    (mV) = (millivolt)
+}
+
+PARAMETER {
+    v                       (mV)
+    celsius                 (degC)
+    erev     = -30          (mV)
+    gbar     = 0.0001       (mho/cm2)
+    vhalf    = -100.6       (mV)
+    k        = 6.4
+    bA       = 9.63 : for activation tau - (note - only 1 tau)
+    bD       = 1.30 : for deactivation tau
+    mA       = 0.0458 : for activation tau
+    mD       = -0.0447 : for deactivation tau
+    q10      = 2.2
+    taumin	= 2.0	(ms)		: minimal value of time constant
+}
+
+NEURON {
+    SUFFIX h15
+    NONSPECIFIC_CURRENT i
+    RANGE gbar, minf, tau, g, m
+    GLOBAL taumin, k, bA, bD, mA, mD, vhalf
+}
+
+STATE {
+    m
+}
+
+ASSIGNED {
+    i       (mA/cm2)
+    minf
+    tau
+    g
+}
+
+INITIAL {
+    rate(v)
+    m = minf
+}
+
+BREAKPOINT {
+    SOLVE states METHOD cnexp
+    g       = gbar*m
+    i       = g*(v-erev)
+}
+
+DERIVATIVE states {     : exact when v held constant; integrates over dt step
+    rate(v)
+    m' = (minf - m) / tau
+}
+
+PROCEDURE rate(v (mV)) { :callable from hoc - leads to segfault in python
+    LOCAL qt
+    qt = q10^((celsius-26.0)/10.0)
+
+    if(v <= -92.0046199111992) {
+      tau = exp(bA + mA * v) / qt 
+    } else {
+      tau = exp(bD + mD * v) / qt
+    }
+    if(tau < taumin) { tau = taumin }
+
+    minf = 1.0/(1.0 + exp((v-vhalf)/k))
+}
diff --git a/netpyne/tutorials/mod/h_kole.mod b/netpyne/tutorials/mod/h_kole.mod
new file mode 100644
index 000000000..3971e5125
--- /dev/null
+++ b/netpyne/tutorials/mod/h_kole.mod
@@ -0,0 +1,76 @@
+TITLE Ih-current
+: modified from http://senselab.med.yale.edu/ModelDB/showmodel.cshtml?model=64195&file=%5cStochastic%5cStochastic_Na%5cih.mod
+: /u/samn/papers/jnsci_26_1677.pdf
+: 
+: @article{kole2006single,
+:  title={Single Ih channels in pyramidal neuron dendrites: properties, distribution, and impact on action potential output},
+:  author={Kole, M.H.P. and Hallermann, S. and Stuart, G.J.},
+:  journal={The Journal of neuroscience},
+:  volume={26},
+:  number={6},
+:  pages={1677--1687},
+:  year={2006},
+:  publisher={Soc Neuroscience}
+: }
+
+COMMENT
+Author: Stefan Hallermann; modified by Sam Neymotin (parameterized)
+Provides deterministic Ih-currents as described in Kole et al. (2006).
+ENDCOMMENT
+
+UNITS {
+  (mA) = (milliamp)
+  (mV) = (millivolt)
+}
+
+PARAMETER {
+  v (mV)
+  erev=-45  		(mV) 	:ih-reversal potential			       
+  gbar=0.00015 	(S/cm2)	:default Ih conductance; exponential distribution is set in Ri18init.hoc 
+  q10 = 2.2
+  ascale = 0.00643
+  bscale = 0.193
+  ashift = 154.9
+  aslope = 11.9
+  bslope = 33.1
+}
+
+NEURON {
+ THREADSAFE
+  SUFFIX ih
+  NONSPECIFIC_CURRENT i
+  RANGE i,gbar,ascale,bscale,ashift,aslope,bslope
+}
+
+STATE {
+  m
+}
+
+ASSIGNED {
+  i (mA/cm2)
+}
+
+INITIAL { LOCAL a,b
+  a = alpha(v)
+  b = beta(v)
+  m = a / (a + b)
+}
+
+BREAKPOINT {
+  SOLVE state METHOD cnexp
+  i = gbar*m*(v-erev)
+}
+
+: tau = 1 / (alpha + beta)
+FUNCTION alpha(v(mV)) {
+  alpha = ascale*(v+ashift)/(exp((v+ashift)/aslope)-1)  
+  :parameters are estimated by direct fitting of HH model to activation time constants and voltage activation curve recorded at 34C
+}
+
+FUNCTION beta(v(mV)) {
+  beta = bscale*exp(v/bslope)
+}
+
+DERIVATIVE state {
+  m' = (1-m)*alpha(v) - m*beta(v)
+}
diff --git a/netpyne/tutorials/mod/h_migliore.mod b/netpyne/tutorials/mod/h_migliore.mod
new file mode 100644
index 000000000..f65df095e
--- /dev/null
+++ b/netpyne/tutorials/mod/h_migliore.mod
@@ -0,0 +1,102 @@
+TITLE I-h channel from Magee 1998 for distal dendrites
+: default values are for dendrites and low Na
+: plus leakage, M.Migliore Mar 2010
+
+UNITS {
+	(mA) = (milliamp)
+	(mV) = (millivolt)
+
+}
+
+PARAMETER {
+	v 		(mV)
+    ehd = -30 		(mV)        
+	celsius 	(degC)
+	gbar=.0001 	(mho/cm2)
+    vhalfl=-90   	(mV)
+    vhalft=-75   	(mV)
+    a0t=0.0046      	(/ms)
+    zetal=4    	(1)
+    zetat=2.2    	(1)
+    gmt=.4   	(1)
+	q10=4.5
+	qtl=1
+	clk=0
+	elk = -70 (mV)
+}
+
+
+NEURON {
+	THREADSAFE SUFFIX hd
+	NONSPECIFIC_CURRENT i
+	NONSPECIFIC_CURRENT lk
+        RANGE gbar, vhalfl, elk, clk, glk, ehd
+        GLOBAL linf,taul
+}
+
+
+STATE {
+        l
+}
+
+ASSIGNED {
+	i (mA/cm2)
+	lk (mA/cm2)
+        linf      
+        taul
+        ghd
+	glk
+}
+
+INITIAL {
+	rate(v)
+	l=linf
+}
+
+
+BREAKPOINT {
+	SOLVE states METHOD cnexp
+	ghd = gbar*l
+	i = ghd*(v-ehd)
+	lk = clk*gbar*(v-elk)
+}
+
+
+FUNCTION alpl(v(mV)) {
+  alpl = exp(0.0378*zetal*(v-vhalfl)) 
+}
+
+FUNCTION alpt(v(mV)) {
+  alpt = exp(0.0378*zetat*(v-vhalft)) 
+}
+
+FUNCTION bett(v(mV)) {
+  bett = exp(0.0378*zetat*gmt*(v-vhalft)) 
+}
+
+DERIVATIVE states {     : exact when v held constant; integrates over dt step
+        rate(v)
+        l' =  (linf - l)/taul
+}
+
+PROCEDURE rate(v (mV)) { :callable from hoc
+        LOCAL a,qt
+        qt=q10^((celsius-33)/10)
+        a = alpt(v)
+        linf = 1/(1+ alpl(v))
+        taul = bett(v)/(qtl*qt*a0t*(1+a))
+}
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/netpyne/tutorials/mod/hh2.mod b/netpyne/tutorials/mod/hh2.mod
new file mode 100644
index 000000000..7d0884c3f
--- /dev/null
+++ b/netpyne/tutorials/mod/hh2.mod
@@ -0,0 +1,122 @@
+TITLE hh2.mod   sodium, potassium, and leak channels
+ 
+COMMENT
+ This is an adjusted Hodgkin-Huxley treatment for sodium, 
+  potassium, and leakage channels.
+ Membrane voltage is in absolute mV and has been reversed in polarity
+  from the original HH convention and shifted to reflect a resting potential
+  of -65 mV.
+ Remember to set celsius in your HOC file.
+ENDCOMMENT
+ 
+UNITS {
+        (mA) = (milliamp)
+        (mV) = (millivolt)
+	(S) = (siemens)
+}
+ 
+? interface
+NEURON {
+        SUFFIX hh2
+        USEION na READ ena WRITE ina
+        USEION k READ ek WRITE ik
+        NONSPECIFIC_CURRENT il
+        RANGE gnabar, gkbar, gl, el, gna, gk
+        GLOBAL minf, hinf, ninf, mtau, htau, ntau, tshift, temp
+	THREADSAFE : assigned GLOBALs will be per thread
+}
+ 
+PARAMETER {
+        gnabar = .12 (S/cm2)	<0,1e9>
+        gkbar = .036 (S/cm2)	<0,1e9>
+        gl = .0003 (S/cm2)	<0,1e9>
+        el = -54.3 (mV)
+        temp = 6.3
+        tshift = 30.7
+}
+ 
+STATE {
+        m h n
+}
+ 
+ASSIGNED {
+        v (mV)
+        celsius (degC)
+        ena (mV)
+        ek (mV)
+
+	gna (S/cm2)
+	gk (S/cm2)
+        ina (mA/cm2)
+        ik (mA/cm2)
+        il (mA/cm2)
+        minf hinf ninf
+	mtau (ms) htau (ms) ntau (ms)
+}
+ 
+? currents
+BREAKPOINT {
+        SOLVE states METHOD cnexp
+        gna = gnabar*m*m*m*h
+	ina = gna*(v - ena)
+        gk = gkbar*n*n*n*n
+	ik = gk*(v - ek)      
+        il = gl*(v - el)
+}
+ 
+ 
+INITIAL {
+	rates(v)
+	m = minf
+	h = hinf
+	n = ninf
+}
+
+? states
+DERIVATIVE states {  
+        rates(v)
+        m' =  (minf-m)/mtau
+        h' = (hinf-h)/htau
+        n' = (ninf-n)/ntau
+}
+ 
+:LOCAL q10
+
+
+? rates
+PROCEDURE rates(v(mV)) {  :Computes rate and other constants at current v.
+                      :Call once from HOC to initialize inf at resting v.
+        LOCAL  alpha, beta, sum, q10
+        TABLE minf, mtau, hinf, htau, ninf, ntau DEPEND celsius FROM -100 TO 100 WITH 200
+
+UNITSOFF
+        q10 = 3^((celsius - temp - tshift)/10)
+                :"m" sodium activation system
+        alpha = .1 * vtrap(-(v+40),10)
+        beta =  4 * exp(-(v+65)/18)
+        sum = alpha + beta
+	mtau = 1/(q10*sum)
+        minf = alpha/sum
+                :"h" sodium inactivation system
+        alpha = .07 * exp(-(v+65)/20)
+        beta = 1 / (exp(-(v+35)/10) + 1)
+        sum = alpha + beta
+	htau = 1/(q10*sum)
+        hinf = alpha/sum
+                :"n" potassium activation system
+        alpha = .01*vtrap(-(v+55),10) 
+        beta = .125*exp(-(v+65)/80)
+	sum = alpha + beta
+        ntau = 1/(q10*sum)
+        ninf = alpha/sum
+}
+ 
+FUNCTION vtrap(x,y) {  :Traps for 0 in denominator of rate eqns.
+        if (fabs(x/y) < 1e-6) {
+                vtrap = y*(1 - x/y/2)
+        }else{
+                vtrap = x/(exp(x/y) - 1)
+        }
+}
+ 
+UNITSON
diff --git a/netpyne/tutorials/mod/hh3.mod b/netpyne/tutorials/mod/hh3.mod
new file mode 100755
index 000000000..4b02f60ee
--- /dev/null
+++ b/netpyne/tutorials/mod/hh3.mod
@@ -0,0 +1,77 @@
+TITLE HH channel
+: Mel-modified Hodgkin - Huxley conductances (after Ojvind et al.)
+
+VERBATIM
+static const char rcsid[]="$Id: hh3.mod,v 1.1 1996/05/19 19:26:28 karchie Exp $";
+ENDVERBATIM
+
+NEURON {
+	SUFFIX hh3
+	USEION na READ ena WRITE ina
+	USEION k READ ek WRITE ik
+	NONSPECIFIC_CURRENT il
+	RANGE gnabar, gkbar, gl, el
+	GLOBAL taus,taun,taum,tauh,tausb
+	GLOBAL tausv,tausd,mN,nN
+}
+
+UNITS {
+	(mA) = (milliamp)
+	(mV) = (millivolt)
+}
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+PARAMETER {
+	v (mV)
+	celsius = 37	(degC)
+	dt (ms)
+	gnabar=.20 (mho/cm2)
+	gkbar=.12 (mho/cm2)
+	gl=.0001 (mho/cm2)
+	ena = 40 (mV)
+	ek = -80 (mV)
+	el = -70.0 (mV)	: steady state at v = -65 mV
+	taum=0.05
+	tauh=0.5
+	taus=50
+	tausv=30
+	tausd=1
+	taun=1
+	mN=3
+	nN=3
+	tausb=0.5
+}
+STATE {
+	m h n s
+}
+ASSIGNED {
+	ina (mA/cm2)
+	ik (mA/cm2)
+	il (mA/cm2)
+
+}
+
+BREAKPOINT {
+	SOLVE states
+
+	ina = gnabar*h*s*(v - ena)*m^mN
+	ik = gkbar*(v - ek)*n^nN
+
+:ina = gnabar*m*m*h*(v - ena)
+:ik = gkbar*n*n*(v - ek)
+	il = gl*(v - el)
+}
+
+PROCEDURE states() {	: exact when v held constant
+	LOCAL sigmas
+	sigmas=1/(1+exp((v+tausv)/tausd))
+	m = m + (1 - exp(-dt/taum))*(1 / (1 + exp((v + 40)/(-3)))  - m)
+	h = h + (1 - exp(-dt/tauh))*(1 / (1 + exp((v + 45)/3))  - h)
+	s = s + (1 - exp(-dt/(taus*sigmas+tausb)))*(1 / (1 + exp((v + 44)/3))  - s)
+	n = n + (1 - exp(-dt/taun))*(1 / (1 + exp((v + 40)/(-3)))  - n)
+	VERBATIM
+	return 0;
+	ENDVERBATIM
+}
+
diff --git a/netpyne/tutorials/mod/hin.mod b/netpyne/tutorials/mod/hin.mod
new file mode 100755
index 000000000..0492ca91b
--- /dev/null
+++ b/netpyne/tutorials/mod/hin.mod
@@ -0,0 +1,76 @@
+TITLE  H-current that uses Na ions
+: Updated to use Cvode by Yiota Poirazi 12/1/2005
+
+NEURON {
+	SUFFIX hin
+        RANGE  gbar,vhalf, K, taun, ninf, g, ihi    
+	USEION hi READ ehi WRITE ihi VALENCE 1      
+
+}
+
+UNITS {
+	(um) = (micrometer)
+	(mA) = (milliamp)
+	(uA) = (microamp)
+	(mV) = (millivolt)
+	(pmho) = (picomho)
+	(mmho) = (millimho)
+}
+
+:INDEPENDENT {t FROM 0 TO 1 WITH 100 (ms)}
+
+PARAMETER {              
+        ena    = 55    (mV)
+        ehi     = -10   (mV)
+	K      = 10.0   (mV)
+	gbar   = 0     (mho/cm2)  : initialize conductance to zero
+	vhalf  = -90   (mV)       : half potential
+}	
+
+
+STATE {                
+	n
+}
+
+ASSIGNED {            
+        v 
+:	ina (mA/cm2)
+	ihi (mA/cm2)
+	ninf
+	taun (ms)
+	g
+}
+
+
+INITIAL {               
+	rates()	
+	n = ninf
+	g = gbar*n
+	ihi = g*(v-ehi)
+}
+
+
+BREAKPOINT {
+	SOLVE states METHOD cnexp
+	g = gbar*n  
+	ihi = g*(v-ehi)  
+}
+
+DERIVATIVE states {
+	rates()
+        n' = (ninf - n)/taun
+}
+
+PROCEDURE rates() {  
+ 
+ 	if (v > -10) {
+	   taun = 1
+	} else {
+           taun = 2*(1/(exp((v+145)/-17.5)+exp((v+16.8)/16.5)) + 10) :h activation tau +5
+
+	}  
+         ninf = 1 - (1 / (1 + exp((vhalf - v)/K)))                  :steady state value
+}
+
+
+
diff --git a/netpyne/tutorials/mod/ican_sidi.mod b/netpyne/tutorials/mod/ican_sidi.mod
new file mode 100644
index 000000000..f2d69a3ff
--- /dev/null
+++ b/netpyne/tutorials/mod/ican_sidi.mod
@@ -0,0 +1,110 @@
+TITLE Slow Ca-dependent cation current
+: from
+:  https://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=144089&file=/PFCcell/mechanism/ican.mod
+:
+:   Ca++ dependent nonspecific cation current ICAN
+:   Differential equations
+:
+:   Model based on a first order kinetic scheme
+:
+:       + n cai <->     (alpha,beta)
+:
+:   Following this model, the activation fct will be half-activated at 
+:   a concentration of Cai = (beta/alpha)^(1/n) = cac (parameter)
+:
+:   The mod file is here written for the case n=2 (2 binding sites)
+:   ---------------------------------------------
+:
+:   Kinetics based on: Partridge & Swandulla, TINS 11: 69-72, 1988.
+:
+:   This current has the following properties:
+:      - inward current (non specific for cations Na, K, Ca, ...)
+:      - activated by intracellular calcium
+:      - NOT voltage dependent
+:
+:   A minimal value for the time constant has been added
+:
+:   Ref: Destexhe et al., J. Neurophysiology 72: 803-818, 1994.
+:   See also:  http://www.cnl.salk.edu/~alain , http://cns.fmed.ulaval.ca
+:
+
+: Updated by Kiki Sidiropoulou (2010) so that dADP has slow inactivation kinetics and it
+: is activated after 5 spikes
+
+: Updated by Sam Neymotin (2016) to avoid using n ion and get rid of 'mystart' rule; also
+: make sure that INITIAL block assigns currents
+
+NEURON {
+  SUFFIX ican
+  NONSPECIFIC_CURRENT i
+  USEION ca READ cai
+  USEION na WRITE ina
+  RANGE gbar, m_inf, tau_m
+  GLOBAL beta, cac, taumin
+}
+
+UNITS {
+  (mA) = (milliamp)
+  (mV) = (millivolt)
+  (molar) = (1/liter)
+  (mM) = (millimolar)
+}
+
+PARAMETER {
+  v               (mV)
+  celsius = 36    (degC)
+  erev      = -20   (mV)            	: reversal potential
+  cai     	     (mM)           	: initial [Ca]i
+  gbar    = 0.0001 (mho/cm2)
+  beta    = 0.0001 (1/ms) 	 	: backward rate constant
+  cac     = 0.0004 (mM)
+  : middle point of activation fct, for ip3 as somacar, for current injection
+  taumin  = 0.1   (ms)            	: minimal value of time constant
+}
+
+STATE {
+  m
+}
+
+ASSIGNED {
+  i      (mA/cm2)
+  ina     (mA/cm2)
+  m_inf
+  tau_m   (ms)
+  tadj
+  g (mho/cm2)
+}
+
+PROCEDURE iassign () {
+  g = gbar * m * m
+  i = g * (v - erev) 
+  ina = 0.7 * i
+}
+
+BREAKPOINT { 
+  SOLVE states METHOD cnexp  
+  iassign()
+}
+
+DERIVATIVE states { 
+  evaluate_fct(v,cai)  
+  m' = (m_inf - m) / tau_m
+}
+
+UNITSOFF
+INITIAL {
+  :  activation kinetics are assumed to be at 22 deg. C
+  :  Q10 is assumed to be 3
+  tadj = 3.0 ^ ((celsius-22.0)/10)  
+  evaluate_fct(v,cai)
+  m = m_inf
+  iassign()
+}
+
+PROCEDURE evaluate_fct(v(mV),cai(mM)) {  LOCAL alpha2  
+  alpha2 = beta * (cai/cac)^2  
+  tau_m = 1 / (alpha2 + beta) / tadj
+  m_inf = alpha2 / (alpha2 + beta)  
+  if(tau_m < taumin) { tau_m = taumin } : min value of time constant
+}
+UNITSON
diff --git a/netpyne/tutorials/mod/iholmw.mod b/netpyne/tutorials/mod/iholmw.mod
new file mode 100644
index 000000000..3bbcb5d8b
--- /dev/null
+++ b/netpyne/tutorials/mod/iholmw.mod
@@ -0,0 +1,65 @@
+: $Id: iholmw.mod,v 1.5 2013/01/02 16:02:37 samn Exp $ 
+COMMENT
+
+//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+//
+// NOTICE OF COPYRIGHT AND OWNERSHIP OF SOFTWARE
+//
+// Copyright 2007, The University Of Pennsylvania
+// 	School of Engineering & Applied Science.
+//   All rights reserved.
+//   For research use only; commercial use prohibited.
+//   Distribution without permission of Maciej T. Lazarewicz not permitted.
+//   mlazarew@seas.upenn.edu
+//
+//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+ENDCOMMENT
+
+UNITS {
+  (mA) = (milliamp)
+  (mV) = (millivolt)
+}
+
+NEURON {
+  SUFFIX Iholmw
+  NONSPECIFIC_CURRENT i
+  RANGE gbar,gh,eh,gfactor
+}
+	
+PARAMETER {
+  gbar = 0.00015 (mho/cm2)
+  eh = -40  (mV)
+  gfactor = 1
+}
+    
+ASSIGNED { 
+  v (mV)
+  i (mA/cm2)
+  gh (mho/cm2)
+}
+
+STATE { q }
+
+PROCEDURE giassign () { 
+  : i = (1e-3) * gh * q * (v-eh) * gfactor 
+  gh = gbar * q * gfactor 
+  i =  gh * (v-eh)
+}
+
+INITIAL { 
+  q  = qinf(v) 
+  giassign()
+}
+
+BREAKPOINT {
+  SOLVE states METHOD cnexp
+  giassign()
+}
+
+DERIVATIVE states { q' = (qinf(v)-q)/qtau(v) }
+
+FUNCTION qinf(v(mV))     { qinf = fun2(v, -80, 1, 10)*1(ms) }
+FUNCTION qtau(v(mV))(ms) { qtau = 200(ms)/(exp((v+70(mV))/20(mV))+exp(-(v+70(mV))/20(mV))) + 5(ms) }
+
+INCLUDE "aux_fun.inc"
diff --git a/netpyne/tutorials/mod/izhi2003a.mod b/netpyne/tutorials/mod/izhi2003a.mod
new file mode 100644
index 000000000..72d566294
--- /dev/null
+++ b/netpyne/tutorials/mod/izhi2003a.mod
@@ -0,0 +1,89 @@
+: Izhikevich artificial neuron model from 
+: EM Izhikevich "Simple Model of Spiking Neurons"
+: IEEE Transactions On Neural Networks, Vol. 14, No. 6, November 2003 pp 1569-1572
+: V is the voltage analog, u controls 
+: see COMMENT below or izh.hoc for typical parameter values
+: uncomment lines with dvv,du to graph derivatives
+
+NEURON {
+  POINT_PROCESS Izhi2003a
+  RANGE a,b,c,d,f,g,Iin,fflag,thresh,erev,taug
+}
+
+INITIAL {
+  V=-65
+  u=0.0
+  gsyn=0
+  net_send(0,1)
+}
+
+PARAMETER {
+  a = 0.02
+  b = 0.2
+  c = -65
+  d = 2
+  f = 5
+  g = 140
+  Iin = 10
+  taug = 1
+  thresh=30
+  erev = 0
+  fflag = 1
+}
+
+STATE { u V gsyn } : use V for voltage so don't interfere with built-in v of cell
+
+ASSIGNED {
+}
+
+BREAKPOINT {
+  SOLVE states METHOD derivimplicit
+}
+
+DERIVATIVE states {
+  V' = 0.04*V*V + f*V + g - u + Iin - gsyn*(V-erev)
+  u' = a*(b*V-u) 
+  gsyn' = -gsyn/taug
+}
+
+NET_RECEIVE (w) {
+  if (flag == 1) {
+    WATCH (V>thresh) 2
+  } else if (flag == 2) {
+    net_event(t)
+    V = c
+    u = u+d
+  } else { : synaptic activation
+    gsyn = gsyn+w
+  }
+}
+
+:** vers gives version
+PROCEDURE version () {
+
+}
+
+COMMENT
+        a        b       c      d       Iin
+================================================================================
+      0.02      0.2     -65     6      14       % tonic spiking
+      0.02      0.25    -65     6       0.5     % phasic spiking
+      0.02      0.2     -50     2      15       % tonic bursting
+      0.02      0.25    -55     0.05    0.6     % phasic bursting
+      0.02      0.2     -55     4      10       % mixed mode
+      0.01      0.2     -65     8      30       % spike frequency adaptation
+      0.02     -0.1     -55     6       0       % Class 1
+      0.2       0.26    -65     0       0       % Class 2
+      0.02      0.2     -65     6       7       % spike latency
+      0.05      0.26    -60     0       0       % subthreshold oscillations
+      0.1       0.26    -60    -1       0       % resonator
+      0.02     -0.1     -55     6       0       % integrator
+      0.03      0.25    -60     4       0       % rebound spike
+      0.03      0.25    -52     0       0       % rebound burst
+      0.03      0.25    -60     4       0       % threshold variability
+      1         1.5     -60     0     -65       % bistability
+      1         0.2     -60   -21       0       % DAP
+      0.02      1       -55     4       0       % accomodation
+     -0.02     -1       -60     8      80       % inhibition-induced spiking
+     -0.026    -1       -45     0      80       % inhibition-induced bursting    
+ENDCOMMENT
diff --git a/netpyne/tutorials/mod/izhi2003b.mod b/netpyne/tutorials/mod/izhi2003b.mod
new file mode 100644
index 000000000..2b06c833a
--- /dev/null
+++ b/netpyne/tutorials/mod/izhi2003b.mod
@@ -0,0 +1,58 @@
+NEURON {
+    POINT_PROCESS Izhi2003b
+    RANGE a,b,c,d,f,g,Iin,fflag,thresh,cellid
+    NONSPECIFIC_CURRENT i
+}
+
+UNITS {
+    (mV) = (millivolt)
+    (nA) = (nanoamp)
+    (nF) = (nanofarad)
+}
+
+INITIAL {
+  v=-65
+  u=0.0
+  net_send(0,1)  
+}
+
+PARAMETER {
+    a       = 0.02 (/ms)
+    b       = 0.2  (/ms)
+    c       = -65  (mV)   : reset potential after a spike
+    d       = 2    (mV/ms)
+    f = 5
+    g = 140
+    Iin = 10
+    thresh = 30   (mV)   : spike threshold
+    fflag = 1
+    cellid = -1 : A parameter for storing the cell ID, if required (useful for diagnostic information)
+}
+
+ASSIGNED {
+    v (mV)
+    i (nA)
+}
+
+STATE {
+    u (mV/ms)
+}
+
+BREAKPOINT {
+    SOLVE states METHOD derivimplicit  : cnexp # either method works
+    i = -0.001*(0.04*v*v + f*v + g - u + Iin)
+}
+
+DERIVATIVE states {
+    u' = a*(b*v - u)
+}
+
+NET_RECEIVE (w) {
+    if (flag == 1) {
+        WATCH (v > thresh) 2
+    } else if (flag == 2) {
+        net_event(t)
+        v = c
+        u = u + d
+    }
+}
diff --git a/netpyne/tutorials/mod/izhi2007a.mod b/netpyne/tutorials/mod/izhi2007a.mod
new file mode 100644
index 000000000..f6fb0dccb
--- /dev/null
+++ b/netpyne/tutorials/mod/izhi2007a.mod
@@ -0,0 +1,248 @@
+COMMENT
+A "simple" implementation of the Izhikevich neuron with AMPA, NMDA,
+GABA_A, and GABA_B receptor dynamics. Equations and parameter values are taken from
+  Izhikevich EM (2007).
+  "Dynamical systems in neuroscience"
+  MIT Press
+
+Equation for synaptic inputs taken from
+  Izhikevich EM, Edelman GM (2008).
+  "Large-scale model of mammalian thalamocortical systems." 
+  PNAS 105(9) 3593-3598.
+
+Example usage (in Python):
+  from neuron import h
+  dummycell = h.Section() # Since Izhi is a point process, it needs to be in a section
+  izhl = [h.Izhi2007(0.5) for i in range(2)] # Create two new Izhikevich cells
+  connection = h.NetCon(izhl[0], izhl[1]) # Connect them
+  izhl[0].Iin = 70  # activate 1 cell
+
+Cell types available are based on Izhikevich, 2007 book:
+    1. RS - Layer 5 regular spiking pyramidal cell (fig 8.12 from 2007 book)
+    2. IB - Layer 5 intrinsically bursting cell (fig 8.19 from 2007 book)
+    3. CH - Cat primary visual cortex chattering cell (fig8.23 from 2007 book)
+    4. LTS - Rat barrel cortex Low-threshold  spiking interneuron (fig 8.25 from 2007 book)
+    5. FS - Rat visual cortex layer 5 fast-spiking interneuron (fig 8.27 from 2007 book)
+    6. TC - Cat dorsal LGN thalamocortical (TC) cell (fig 8.31 from 2007 book)
+    7. RTN - Rat reticular thalamic nucleus (RTN) cell  (fig 8.32 from 2007 book)
+ENDCOMMENT
+
+: Declare name of object and variables
+NEURON {
+  POINT_PROCESS Izhi2007a
+  RANGE C, k, vr, vt, vpeak, a, b, c, d, Iin, tauAMPA, tauNMDA, tauGABAA, tauGABAB, tauOpsin, celltype, alive, cellid, verbose
+  RANGE V, u, gAMPA, gNMDA, gGABAA, gGABAB, gOpsin, I
+  RANGE factor, eventflag, delta, t0
+}
+
+: Specify units that have physiological interpretations (NB: ms is already declared)
+UNITS {
+  (mV) = (millivolt)
+  (uM) = (micrometer)
+}
+
+: Parameters from Izhikevich 2007, MIT Press for regular spiking pyramidal cell
+PARAMETER {
+  C = 1 : Capacitance
+  k = 0.7
+  vr = -60 (mV) : Resting membrane potential
+  vt = -40 (mV) : Membrane threhsold
+  vpeak = 35 (mV) : Peak voltage
+  a = 0.03
+  b = -2
+  c = -50
+  d = 100
+  Iin = 0
+  Vpre = 0
+  tauAMPA = 5 (ms) : Receptor time constant, AMPA
+  tauNMDA = 150 (ms) : Receptor time constant, NMDA
+  tauGABAA = 6 (ms) : Receptor time constant, GABAA
+  tauGABAB = 150 (ms) : Receptor time constant, GABAB
+  tauOpsin = 50 (ms) : Receptor time constant, opsin, from Mattis et al. (2011)
+  celltype = 1 : A flag for indicating what kind of cell it is,  used for changing the dynamics slightly (see list of cell types in initial comment).
+  alive = 1 : A flag for deciding whether or not the cell is alive -- if it's dead, acts normally except it doesn't fire spikes
+  cellid = -1 : A parameter for storing the cell ID, if required (useful for diagnostic information)
+  verbose = 0 : Whether or not to print diagnostic information to file -- WARNING, do not modify this manually -- it's set by useverbose()
+}
+
+: Variables used for internal calculations
+ASSIGNED {
+  factor : Voltage factor used for calculating the current
+  eventflag : For diagnostic information
+  V (mV) : Membrane voltage
+  u (mV) : Slow current/recovery variable
+  gAMPA : AMPA conductance
+  gNMDA : NMDA conductance
+  gGABAA : GABAA conductance
+  gGABAB : GABAB conductance
+  gOpsin : Opsin conductance
+  I : Total current
+  delta : Time step
+  t0 : Previous time  
+}
+
+: Initial conditions
+INITIAL {
+  V = vr
+  u = 0.0
+  t0 = t
+  gAMPA = 0
+  gNMDA = 0
+  gGABAA = 0
+  gGABAB = 0
+  gOpsin = 0
+  I = 0
+  delta = 0
+  net_send(0,1) : Required for the WATCH statement to be active
+}
+
+
+: Function for printing diagnostic information to a file -- usage example: cell.useverbose(2,"logfile.txt")
+VERBATIM
+char filename[1000]; // Allocate some memory for the filename
+ENDVERBATIM
+PROCEDURE useverbose() { : Create user-accessible function
+  VERBATIM
+  #include<stdio.h> // Basic input-output
+  verbose = (float) *getarg(1); // Set verbosity -- 0 = none, 1 = events, 2 = events + timesteps
+  strcpy(filename, gargstr(2)); // Copy input filename into memory
+  ENDVERBATIM
+}
+
+: Define neuron dynamics
+BREAKPOINT {
+  delta = t-t0 : Find time difference
+
+  : Receptor dynamics -- the correct form is gAMPA = gAMPA*exp(-delta/tauAMPA), but this is 30% slower and, in the end, not really any more physiologically realistic
+  gAMPA = gAMPA - delta*gAMPA/tauAMPA : "Exponential" decays -- fast excitatory (AMPA)
+  gNMDA = gNMDA - delta*gNMDA/tauNMDA : Slow excitatory (NMDA)
+  gGABAA = gGABAA - delta*gGABAA/tauGABAA : Fast inhibitory (GABA_A)
+  gGABAB = gGABAB - delta*gGABAB/tauGABAB : Slow inhibitory (GABA_B)
+  gOpsin = gOpsin - delta*gOpsin/tauOpsin : Optogenetic (opsin)
+  
+  : Calculate current
+  factor = ((V+80)/60)*((V+80)/60)
+  I = gAMPA*(V-0) + gNMDA*factor/(1+factor)*(V-0) + gGABAA*(V+70) + gGABAB*(V+90) + gOpsin*(V-0) : Treat the opsin channel like an AMPA channel
+  
+  : Calculate neuronal dynamics; -I since I = -I_{syn}, which is really what I is as I've defined it above
+  Vpre = V
+  V = V + delta*(k*(V-vr)*(V-vt) - u - I + Iin)/C  : Calculate voltage
+
+  if (Vpre<=c && V>vpeak) {V=c+1} : if just spiked, wait at least 1 timestep before increasing V>vpeak again, so V reset value takes effect; WATCH statement requires V to cross the vpeak threshod)
+
+  : Cell-type specific dynamics
+  if (celltype<5) {
+    u = u + delta*a*(b*(V-vr)-u) : Calculate recovery variable
+  }
+  else {
+     : For FS neurons, include nonlinear U(v): U(v) = 0 when v<vb ; U(v) = 0.025(v-vb) when v>=vb (d=vb=-55)
+     if (celltype==5) {
+       if (V<d) { 
+        u = u + delta*a*(0-u)
+       }
+       else { 
+        u = u + delta*a*((0.025*(V-d)^3)-u)
+       }
+     }
+
+     : For TC neurons, reset b
+     if (celltype==6) {
+       if (V>-65) {b=0}
+       else {b=15}
+       u = u + delta*a*(b*(V-vr)-u) : Calculate recovery variable
+     }
+     
+     : For TRN neurons, reset b
+     if (celltype==7) {
+       if (V>-65) {b=2}
+       else {b=10}
+       u = u + delta*a*(b*(V-vr)-u) : Calculate recovery variable
+     }
+  }
+
+  t0=t : Reset last time so delta can be calculated in the next time step
+  
+  : Print diagnostic inormation to a file
+  if (verbose>1) { : Verbose turned on?
+    VERBATIM
+    FILE *outfile; // Declare file object
+    outfile=fopen(filename,"a"); // Open file for appending
+    fprintf(outfile,"%8.2f   cell=%6.0f   delta=%8.2f   gAMPA=%8.2f   gNMDA=%8.2f   gGABAA=%8.2f   gGABAB=%8.2f   gOpsin=%8.2f   factor=%8.2f   I=%8.2f   V=%8.2f   u=%8.2f (timestep)\n",t,cellid,delta,gAMPA,gNMDA,gGABAA,gGABAB,gOpsin,factor,I,V,u);
+    fclose(outfile); // Close file
+    ENDVERBATIM
+  }
+}
+
+: Input received
+NET_RECEIVE (wAMPA, wNMDA, wGABAA, wGABAB, wOpsin) {  
+  INITIAL { wAMPA=wAMPA wNMDA=wNMDA wGABAA=wGABAA wGABAB=wGABAB wOpsin=wOpsin} : Insanely stupid but required, otherwise reset to 0, 
+
+  : Check if spike occurred
+  if (flag == 1) { : Fake event from INITIAL block
+    if (celltype < 4 || celltype == 5 || celltype == 7) { : default
+      WATCH (V>vpeak) 2 : Check if threshold has been crossed, and if so, set flag=2
+    }
+    else if (celltype == 4) { : LTS cell
+      WATCH (V>(vpeak-0.1*u)) 2 : Check if threshold has been crossed, and if so, set flag=2     
+    }
+    else if (celltype == 6) { : TC cell
+      WATCH (V>(vpeak+0.1*u)) 2 : Check if threshold has been crossed, and if so, set flag=2     
+    }
+  } 
+  
+  : Event created by WATCH statement -- i.e. threshold crossed
+  else if (flag == 2) { 
+    if (alive) {net_event(t)} : Send spike event if the cell is alive
+
+    : For RS, IB and CH neurons, and RTN
+    if (celltype < 4 || celltype == 7) {
+      V = c : Reset voltage
+      u = u+d : Reset recovery variable
+    }
+    : For LTS neurons
+    else if (celltype == 4) {
+      V = c+0.04*u : Reset voltage
+      if ((u+d)<670) {u=u+d} : Reset recovery variable
+      else {u=670} 
+     }  
+    : For FS neurons (only update v)
+    else if (celltype == 5) {
+      V = c : Reset voltage
+     }  
+    : For TC neurons (only update v)
+    else if (celltype == 6) {
+      V = c-0.1*u : Reset voltage
+      u = u+d : Reset recovery variable
+     }  
+
+    gAMPA = 0 : Reset conductances -- not mentioned in Izhikevich's paper but necessary to stop things from exploding!
+    gNMDA = 0
+    gGABAA = 0
+    gGABAB = 0
+    gOpsin = 0
+  } 
+  
+  : Actual input, calculate receptor dynamics
+  else {
+    gAMPA = gAMPA + wAMPA
+    gNMDA = gNMDA + wNMDA
+    gGABAA = gGABAA + wGABAA
+    gGABAB = gGABAB + wGABAB
+    gOpsin = gOpsin + wOpsin
+  }
+  
+  : Print diagnostic information to a file
+  if (verbose>0) { : Verbose turned on?
+    eventflag = flag
+    VERBATIM
+    FILE *outfile; // Declare file object
+//if(cellid>=0 && cellid < 300) {
+    outfile=fopen(filename,"a"); // Open file for appending
+    fprintf(outfile,"t=%8.2f   cell=%6.0f   flag=%1.0f   gAMPA=%8.2f   gNMDA=%8.2f   gGABAA=%8.2f   gGABAB=%8.2f   gOpsin=%8.2f   V=%8.2f   u=%8.2f (event)\n",t, cellid,eventflag,gAMPA,gNMDA,gGABAA,gGABAB,gOpsin,V,u);
+    fclose(outfile); // Close file
+//}
+    ENDVERBATIM
+  }
+  
+  
+}
diff --git a/netpyne/tutorials/mod/izhi2007b.mod b/netpyne/tutorials/mod/izhi2007b.mod
new file mode 100644
index 000000000..cfec092bf
--- /dev/null
+++ b/netpyne/tutorials/mod/izhi2007b.mod
@@ -0,0 +1,186 @@
+COMMENT
+
+A "simple" implementation of the Izhikevich neuron.
+Equations and parameter values are taken from
+  Izhikevich EM (2007).
+  "Dynamical systems in neuroscience"
+  MIT Press
+
+Equation for synaptic inputs taken from
+  Izhikevich EM, Edelman GM (2008).
+  "Large-scale model of mammalian thalamocortical systems." 
+  PNAS 105(9) 3593-3598.
+
+Example usage (in Python):
+  from neuron import h
+  sec = h.Section(name=sec) # section will be used to calculate v
+  izh = h.Izhi2007b(0.5)
+  def initiz () : sec.v=-60
+  fih=h.FInitializeHandler(initz)
+  izh.Iin = 70  # current clamp
+
+Cell types available are based on Izhikevich, 2007 book:
+    1. RS - Layer 5 regular spiking pyramidal cell (fig 8.12 from 2007 book)
+    2. IB - Layer 5 intrinsically bursting cell (fig 8.19 from 2007 book)
+    3. CH - Cat primary visual cortex chattering cell (fig 8.23 from 2007 book)
+    4. LTS - Rat barrel cortex Low-threshold  spiking interneuron (fig 8.25 from 2007 book)
+    5. FS - Rat visual cortex layer 5 fast-spiking interneuron (fig 8.27 from 2007 book)
+    6. TC - Cat dorsal LGN thalamocortical (TC) cell (fig 8.31 from 2007 book)
+    7. RTN - Rat reticular thalamic nucleus (RTN) cell  (fig 8.32 from 2007 book)
+
+ENDCOMMENT
+
+: Declare name of object and variables
+NEURON {
+  POINT_PROCESS Izhi2007b
+  RANGE C, k, vr, vt, vpeak, u, a, b, c, d, Iin, celltype, alive, cellid, verbose, derivtype, delta, t0
+  NONSPECIFIC_CURRENT i
+}
+
+: Specify units that have physiological interpretations (NB: ms is already declared)
+UNITS {
+  (mV) = (millivolt)
+  (uM) = (micrometer)
+}
+
+: Parameters from Izhikevich 2007, MIT Press for regular spiking pyramidal cell
+PARAMETER {
+  C = 1 : Capacitance
+  k = 0.7
+  vr = -60 (mV) : Resting membrane potential
+  vt = -40 (mV) : Membrane threhsold
+  vpeak = 35 (mV) : Peak voltage
+  a = 0.03
+  b = -2
+  c = -50
+  d = 100
+  Iin = 0
+  celltype = 1 : A flag for indicating what kind of cell it is,  used for changing the dynamics slightly (see list of cell types in initial comment).
+  alive = 1 : A flag for deciding whether or not the cell is alive -- if it's dead, acts normally except it doesn't fire spikes
+  cellid = -1 : A parameter for storing the cell ID, if required (useful for diagnostic information)
+}
+
+: Variables used for internal calculations
+ASSIGNED {
+  v (mV)
+  i (nA)
+  u (mV) : Slow current/recovery variable
+  delta
+  t0
+  derivtype
+}
+
+: Initial conditions
+INITIAL {
+  u = 0.0
+  derivtype=2
+  net_send(0,1) : Required for the WATCH statement to be active; v=vr initialization done there
+}
+
+: Define neuron dynamics
+BREAKPOINT {
+  delta = t-t0 : Find time difference
+  if (celltype<5) {
+    u = u + delta*a*(b*(v-vr)-u) : Calculate recovery variable
+  }
+  else {
+     : For FS neurons, include nonlinear U(v): U(v) = 0 when v<vb ; U(v) = 0.025(v-vb) when v>=vb (d=vb=-55)
+     if (celltype==5) {
+       if (v<d) { 
+        u = u + delta*a*(0-u)
+       }
+       else { 
+        u = u + delta*a*((0.025*(v-d)*(v-d)*(v-d))-u)
+       }
+     }
+
+     : For TC neurons, reset b
+     if (celltype==6) {
+       if (v>-65) {b=0}
+       else {b=15}
+       u = u + delta*a*(b*(v-vr)-u) : Calculate recovery variable
+     }
+     
+     : For TRN neurons, reset b
+     if (celltype==7) {
+       if (v>-65) {b=2}
+       else {b=10}
+       u = u + delta*a*(b*(v-vr)-u) : Calculate recovery variable
+     }
+  }
+
+  t0=t : Reset last time so delta can be calculated in the next time step
+  i = -(k*(v-vr)*(v-vt) - u + Iin)/C/1000
+}
+
+FUNCTION derivfunc () {
+  if (celltype==5 && derivtype==2) { : For FS neurons, include nonlinear U(v): U(v) = 0 when v<vb ; U(v) = 0.025(v-vb) when v>=vb (d=vb=-55)
+    derivfunc = a*(0-u)
+  } else if (celltype==5 && derivtype==1) { : For FS neurons, include nonlinear U(v): U(v) = 0 when v<vb ; U(v) = 0.025(v-vb) when v>=vb (d=vb=-55)
+    derivfunc = a*((0.025*(v-d)*(v-d)*(v-d))-u)
+  } else if (celltype==5) { 
+    VERBATIM
+    hoc_execerror("izhi2007b.mod ERRA: derivtype not set",0);
+    ENDVERBATIM
+  } else {
+    derivfunc = a*(b*(v-vr)-u) : Calculate recovery variable
+  }
+}
+
+: Input received
+NET_RECEIVE (w) {
+  : Check if spike occurred
+  if (flag == 1) { : Fake event from INITIAL block
+    if (celltype == 4) { : LTS cell
+      WATCH (v>(vpeak-0.1*u)) 2 : Check if threshold has been crossed, and if so, set flag=2     
+    } else if (celltype == 6) { : TC cell
+      WATCH (v>(vpeak+0.1*u)) 2 
+    } else { : default for all other types
+      WATCH (v>vpeak) 2 
+    }
+    : additional WATCHfulness
+    if (celltype==6 || celltype==7) {
+      WATCH (v> -65) 3 : change b param
+      WATCH (v< -65) 4 : change b param
+    }
+    if (celltype==5) {
+      WATCH (v> d) 3  : going up
+      WATCH (v< d) 4  : coming down
+    }
+    v = vr  : initialization can be done here
+  : FLAG 2 Event created by WATCH statement -- threshold crossed for spiking
+  } else if (flag == 2) { 
+    if (alive) {net_event(t)} : Send spike event if the cell is alive
+    : For LTS neurons
+    if (celltype == 4) {
+      v = c+0.04*u : Reset voltage
+      if ((u+d)<670) {u=u+d} : Reset recovery variable
+      else {u=670} 
+     }  
+    : For FS neurons (only update v)
+    else if (celltype == 5) {
+      v = c : Reset voltage
+     }  
+    : For TC neurons (only update v)
+    else if (celltype == 6) {
+      v = c-0.1*u : Reset voltage
+      u = u+d : Reset recovery variable
+     }  else {: For RS, IB and CH neurons, and RTN
+      v = c : Reset voltage
+      u = u+d : Reset recovery variable
+     }
+  : FLAG 3 Event created by WATCH statement -- v exceeding set point for param reset
+  } else if (flag == 3) { 
+    : For TC neurons 
+    if (celltype == 5)        { derivtype = 1 : if (v>d) u'=a*((0.025*(v-d)*(v-d)*(v-d))-u)
+    } else if (celltype == 6) { b=0
+    } else if (celltype == 7) { b=2 
+    }
+  : FLAG 4 Event created by WATCH statement -- v dropping below a setpoint for param reset
+  } else if (flag == 4) { 
+    if (celltype == 5)        { derivtype = 2  : if (v<d) u==a*(0-u)
+    } else if (celltype == 6) { b=15
+    } else if (celltype == 7) { b=10
+    }
+  }
+}
diff --git a/netpyne/tutorials/mod/k2.mod b/netpyne/tutorials/mod/k2.mod
new file mode 100755
index 000000000..f6288ef36
--- /dev/null
+++ b/netpyne/tutorials/mod/k2.mod
@@ -0,0 +1,59 @@
+TITLE Potasium Type K2 current for RD Traub, J Neurophysiol 89:909-921, 2003
+
+COMMENT
+
+	Implemented by Maciej Lazarewicz 2003 (mlazarew@seas.upenn.edu)
+
+ENDCOMMENT
+
+INDEPENDENT { t FROM 0 TO 1 WITH 1 (ms) }
+
+UNITS { 
+	(mV) = (millivolt) 
+	(mA) = (milliamp) 
+} 
+NEURON { 
+	SUFFIX k2
+	USEION k READ ek WRITE ik
+	RANGE gbar, ik
+}
+PARAMETER { 
+	gbar = 0.0 	(mho/cm2)
+	v ek 		(mV)  
+} 
+ASSIGNED { 
+	ik 		(mA/cm2) 
+	minf hinf 	(1)
+	mtau htau 	(ms) 
+} 
+STATE {
+	m h
+}
+BREAKPOINT { 
+	SOLVE states METHOD cnexp
+	ik = gbar * m * h * ( v - ek ) 
+} 
+INITIAL { 
+	settables(v) 
+	m  = minf
+	m  = 0
+	h  = hinf
+} 
+DERIVATIVE states { 
+	settables(v)  
+	m' = ( minf - m ) / mtau 
+	h' = ( hinf - h ) / htau
+}
+
+UNITSOFF 
+
+PROCEDURE settables(v) { 
+	TABLE minf, hinf, mtau, htau  FROM -120 TO 40 WITH 641
+
+	minf  = 1 / ( 1 + exp( ( - v - 10 ) / 17 ) )
+	mtau  = 4.95 + 0.5 / ( exp( ( v - 81 ) / 25.6 ) + exp( ( - v - 132 ) / 18 ) )
+	hinf  = 1 / ( 1 + exp( ( v + 58 ) / 10.6 ) )
+	htau  = 60 + 0.5 / ( exp( ( v - 1.33 ) / 200 ) + exp( ( - v - 130 ) / 7.1 ) )
+}
+
+UNITSON
\ No newline at end of file
diff --git a/netpyne/tutorials/mod/kBK.mod b/netpyne/tutorials/mod/kBK.mod
new file mode 100644
index 000000000..353571e71
--- /dev/null
+++ b/netpyne/tutorials/mod/kBK.mod
@@ -0,0 +1,108 @@
+: from https://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=168148&file=/stadler2014_layerV/kBK.mod
+TITLE large-conductance calcium-activated potassium channel (BK)
+	:Mechanism according to Gong et al 2001 and Womack&Khodakakhah 2002,
+	:adapted for Layer V cells on the basis of Benhassine&Berger 2005.
+	:NB: concentrations in mM
+	
+NEURON {
+	SUFFIX kBK
+	USEION k READ ek WRITE ik
+	USEION ca READ cai
+	RANGE gpeak, gkact, caPh, caPk, caPmax, caPmin
+	RANGE caVhh, CaVhk, caVhmax, caVhmin, k, tau
+        GLOBAL pinfmin : cutoff - if pinf < pinfmin, set to 0.; by default cutoff not used (pinfmin==0)
+}
+
+
+UNITS {
+	(mA) = (milliamp)
+	(mV) = (millivolt)
+	(molar) = (1/liter)
+	(mM) 	= (millimolar)
+}
+
+
+PARAMETER {
+		:maximum conductance (Benhassine 05)
+	gpeak   = 268e-4	(mho/cm2) <0, 1e9>
+	
+	                                    : Calcium dependence of opening probability (Gong 2001)
+	caPh    = 2e-3     (mM)             : conc. with half maximum open probaility
+	caPk    = 1                         : Steepness of calcium dependence curve
+	caPmax  = 1                         : max and
+	caPmin  = 0                         : min open probability
+		
+	                                    : Calcium dependence of Vh shift (Womack 2002)
+	caVhh   = 2e-3    (mM)              : Conc. for half of the Vh shift
+	caVhk   = -0.94208                  : Steepness of the Vh-calcium dependence curve
+	caVhmax = 155.67 (mV)               : max and
+	caVhmin = -46.08 (mV)               : min Vh
+	
+	                                    : Voltage dependence of open probability (Gong 2001)
+	                                    : must not be zero
+	k       = 17	(mV)
+	
+	                                    : Timeconstant of channel kinetics
+	                                    : no data for a description of a calcium&voltage dependence
+	                                    : some points (room temp) in Behassine 05 & Womack 02
+	tau     = 1 (ms) <1e-12, 1e9>
+	scale   = 100                       : scaling to incorporate higher ca conc near ca channels
+        
+        pinfmin = 0.0                       : cutoff for pinf - less than that set pinf to 0.0
+
+} 	
+
+
+ASSIGNED {
+	v 		(mV)
+	ek		(mV)
+	ik 		(mA/cm2)
+    	cai  		(mM)
+	caiScaled	(mM)
+	pinf		(1)
+}
+
+
+STATE {
+        p
+}
+
+BREAKPOINT {
+	SOLVE states METHOD cnexp
+	ik = gpeak*p* (v - ek)
+}
+
+DERIVATIVE states {     
+        rate(v, cai)
+        p' =  (pinf - p)/tau
+}
+
+INITIAL {     
+        rate(v, cai)
+        p = pinf
+}
+
+PROCEDURE rate(v(mV), ca(mM))  {
+        caiScaled = ca*scale	
+        pinf = P0ca(caiScaled) / ( 1 + exp( (Vhca(caiScaled)-v)/k ) )
+        if(pinf < pinfmin) { pinf = 0.0 }
+}
+
+FUNCTION P0ca(ca(mM)) (1) {
+		
+	if (ca < 1E-18) { 		:check for division by zero		
+	P0ca = caPmin
+	} else {
+	P0ca = caPmin + ( (caPmax - caPmin) / ( 1 + (caPh/ca)^caPk ))
+	}
+}
+
+FUNCTION Vhca(ca(mM)) (mV) {
+		
+	if (ca < 1E-18) {		:check for division by zero
+	Vhca = caVhmax
+	} else {
+	Vhca = caVhmin + ( (caVhmax - caVhmin ) / ( 1 + ((caVhh/ca)^caVhk)) )
+	}
+}	
+
diff --git a/netpyne/tutorials/mod/ka.mod b/netpyne/tutorials/mod/ka.mod
new file mode 100755
index 000000000..3c08dd23c
--- /dev/null
+++ b/netpyne/tutorials/mod/ka.mod
@@ -0,0 +1,63 @@
+TITLE Potasium Type A current for RD Traub, J Neurophysiol 89:909-921, 2003
+
+COMMENT
+
+	Implemented by Maciej Lazarewicz 2003 (mlazarew@seas.upenn.edu)
+
+ENDCOMMENT
+
+INDEPENDENT { t FROM 0 TO 1 WITH 1 (ms) }
+
+UNITS { 
+	(mV) = (millivolt) 
+	(mA) = (milliamp) 
+} 
+NEURON { 
+	SUFFIX ka
+	USEION k READ ek WRITE ik
+	RANGE gbar, ik
+}
+PARAMETER { 
+	gbar = 0.0 	(mho/cm2)
+	v ek 		(mV)  
+} 
+ASSIGNED { 
+	ik 		(mA/cm2) 
+	minf hinf 	(1)
+	mtau htau 	(ms) 
+} 
+STATE {
+	m h
+}
+BREAKPOINT { 
+	SOLVE states METHOD cnexp
+	ik = gbar * m * m * m * m * h * ( v - ek ) 
+} 
+INITIAL { 
+	settables(v) 
+	m  = minf
+	m  = 0
+	h  = hinf
+} 
+DERIVATIVE states { 
+	settables(v) 
+	m' = ( minf - m ) / mtau 
+	h' = ( hinf - h ) / htau
+}
+
+UNITSOFF 
+
+PROCEDURE settables(v) { 
+	TABLE minf, hinf, mtau, htau  FROM -120 TO 40 WITH 641
+
+	minf  = 1 / ( 1 + exp( ( - v - 60 ) / 8.5 ) )
+	mtau = 0.185 + 0.5 / ( exp( ( v + 35.8 ) / 19.7 ) + exp( ( - v - 79.7 ) / 12.7 ) )
+	hinf  = 1 / ( 1 + exp( ( v + 78 ) / 6 ) )
+	if( v < -63 ) {
+		htau = 0.5 / ( exp( ( v + 46 ) / 5 ) + exp( ( - v - 238 ) / 37.5 ) )
+	}else{
+		htau = 9.5
+	}
+}
+
+UNITSON
\ No newline at end of file
diff --git a/netpyne/tutorials/mod/kacurrent.mod b/netpyne/tutorials/mod/kacurrent.mod
new file mode 100644
index 000000000..9e4fe6922
--- /dev/null
+++ b/netpyne/tutorials/mod/kacurrent.mod
@@ -0,0 +1,85 @@
+: $Id: CA1ika.mod,v 1.2 2010/12/01 05:06:07 samn Exp $ 
+TITLE Ika CA1
+
+UNITS {
+  (mA) = (milliamp)
+  (mV) = (millivolt)
+}
+ 
+NEURON {
+  SUFFIX kacurrent
+  NONSPECIFIC_CURRENT ika, ikad
+  RANGE g, gd, e, ninf, ntau, ndinf, ndtau, linf, ltau
+}
+ 
+PARAMETER {
+  celsius	(degC)
+  g= 0.048	(mho/cm2)
+  gd= 0		(mho/cm2)
+  e= -90	(mV)
+}
+ 
+STATE {
+  n
+  nd : distal
+  l
+}
+ 
+ASSIGNED {
+  v	(mV)
+  ika	(mA/cm2) 
+  ikad	(mA/cm2)
+  ninf
+  ntau  (ms)
+  ndinf
+  ndtau (ms)
+  linf
+  ltau	(ms)
+}
+
+PROCEDURE iassign () {
+  ika=g*n*l*(v-e)
+  ikad=gd*nd*l*(v-e)
+}
+ 
+BREAKPOINT {
+  SOLVE states METHOD cnexp
+  iassign()
+}
+ 
+DERIVATIVE states { 
+  rates(v)
+  n'= (ninf- n)/ ntau
+  l'= (linf- l)/ ltau
+  nd'= (ndinf-nd)/ndtau
+}
+
+INITIAL { 
+  rates(v)
+  n = ninf
+  l = linf
+  iassign()
+}
+
+PROCEDURE rates(v (mV)) {
+  LOCAL  a, b
+  UNITSOFF
+  a = exp(-0.038*(1.5+1/(1+exp(v+40)/5))*(v-11))
+  b =	exp(-0.038*(0.825+1/(1+exp(v+40)/5))*(v-11))
+  ntau=4*b/(1+a)
+  if (ntau<0.1) {ntau=0.1}
+  ninf=1/(1+a)
+	
+  a=exp(-0.038*(1.8+1/(1+exp(v+40)/5))*(v+1))
+  b=exp(-0.038*(0.7+1/(1+exp(v+40)/5))*(v+1))
+  ndtau=2*b/(1+a)
+  if (ndtau<0.1) {ndtau=0.1}
+  ndinf=1/(1+a)
+
+  a = exp(0.11*(v+56))
+  ltau=0.26*(v+50)
+  if (ltau<2) {ltau=2}
+  linf=1/(1+a)
+  UNITSON
+}
+
diff --git a/netpyne/tutorials/mod/kahp.mod b/netpyne/tutorials/mod/kahp.mod
new file mode 100755
index 000000000..234730f54
--- /dev/null
+++ b/netpyne/tutorials/mod/kahp.mod
@@ -0,0 +1,65 @@
+TITLE Potasium AHP type current for RD Traub, J Neurophysiol 89:909-921, 2003
+
+COMMENT
+
+	Implemented by Maciej Lazarewicz 2003 (mlazarew@seas.upenn.edu)
+
+ENDCOMMENT
+
+UNITS { 
+	(mV) = (millivolt) 
+	(mA) = (milliamp) 
+} 
+
+NEURON { 
+	SUFFIX kahp
+	USEION k READ ek WRITE ik
+	USEION ca READ cai
+	RANGE gbar, ik
+}
+
+PARAMETER { 
+	gbar = 0.0 	(mho/cm2)
+	v		(mV) 
+	ek 		(mV)  
+	cai		(1)
+}
+ 
+ASSIGNED { 
+	ik 		(mA/cm2) 
+	alpha beta	(/ms)
+}
+ 
+STATE {
+	m
+}
+
+BREAKPOINT { 
+	SOLVE states METHOD cnexp
+	ik = gbar * m * ( v - ek ) 
+}
+ 
+INITIAL { 
+	rates( cai )
+	m = alpha / ( alpha + beta )
+	m = 0
+}
+ 
+DERIVATIVE states { 
+	rates( cai )
+	m' = alpha * ( 1 - m ) - beta * m 
+}
+
+UNITSOFF 
+
+PROCEDURE rates(chi) { 
+
+	if( cai < 100 ) {
+		alpha = cai / 10000
+	}else{
+		alpha = 0.01
+	}
+	beta = 0.01
+}
+
+UNITSON
\ No newline at end of file
diff --git a/netpyne/tutorials/mod/kap_BS.mod b/netpyne/tutorials/mod/kap_BS.mod
new file mode 100644
index 000000000..501817d5b
--- /dev/null
+++ b/netpyne/tutorials/mod/kap_BS.mod
@@ -0,0 +1,124 @@
+TITLE K-A channel from Klee Ficker and Heinemann
+: modified to account for Dax A Current --- M.Migliore Jun 1997
+: modified to be used with cvode  M.Migliore 2001
+: thread-safe 2010-05-31 Ben Suter
+: 2010-11-07 Ben Suter, removing "ka" from parameter names, reformatting, setting sh = 0 (was 24 mV)
+:
+: :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
+: Copyright 2011, Benjamin Suter (for changes only)
+: Used in model of corticospinal neuron BS0284 and published as:
+:  "Intrinsic electrophysiology of mouse corticospinal neurons: a characteristic set of features embodied in a realistic computational model"
+:  by Benjamin Suter, Michele Migliore, and Gordon Shepherd
+:  Submitted September 2011
+: :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
+
+
+UNITS {
+    (mA) = (milliamp)
+    (mV) = (millivolt)
+}
+
+PARAMETER {
+    v                   (mV)
+    celsius             (degC)
+    ek
+
+    sh      = 0
+    gbar    = 0.008     (mho/cm2)
+    vhalfn  = 11        (mV)
+    vhalfl  = -56       (mV)
+    a0l     = 0.05      (/ms)
+    a0n     = 0.05      (/ms)
+    zetan   = -1.5      (1)
+    zetal   = 3         (1)
+    gmn     = 0.55      (1)
+    gml     = 1         (1)
+    lmin    = 5         (mS)
+    nmin    = 0.4       (mS)
+    pw      = -1        (1)
+    tq      = -40
+    qq      = 5
+    q10     = 5
+    qtl     = 1
+}
+
+
+NEURON {
+    SUFFIX kap
+    USEION k READ ek WRITE ik
+    RANGE gbar, g, sh, tq, vhalfn, vhalfl, ik : 
+:        GLOBAL ninf,linf,taul,taun,lmin
+}
+
+STATE {
+    n
+    l
+}
+
+ASSIGNED {
+    ik      (mA/cm2)
+    ninf
+    linf
+    taul
+    taun
+    g
+}
+
+INITIAL {
+    rates(v)
+    n=ninf
+    l=linf
+}
+
+
+BREAKPOINT {
+    SOLVE states METHOD cnexp
+    g = gbar*n*l
+    ik = g*(v-ek)
+}
+
+
+FUNCTION alpn(v(mV)) {
+    LOCAL zeta
+    zeta=zetan+pw/(1+exp((v-tq-sh)/qq))
+    alpn = exp(1.e-3*zeta*(v-vhalfn-sh)*9.648e4/(8.315*(273.16+celsius)))
+}
+
+FUNCTION betn(v(mV)) {
+    LOCAL zeta
+    zeta=zetan+pw/(1+exp((v-tq-sh)/qq))
+    betn = exp(1.e-3*zeta*gmn*(v-vhalfn-sh)*9.648e4/(8.315*(273.16+celsius)))
+}
+
+FUNCTION alpl(v(mV)) {
+    alpl = exp(1.e-3*zetal*(v-vhalfl-sh)*9.648e4/(8.315*(273.16+celsius)))
+}
+
+FUNCTION betl(v(mV)) {
+    betl = exp(1.e-3*zetal*gml*(v-vhalfl-sh)*9.648e4/(8.315*(273.16+celsius)))
+}
+
+DERIVATIVE states {     : exact when v held constant; integrates over dt step
+    rates(v)
+    n' = (ninf - n) / taun
+    l' = (linf - l) / taul
+}
+
+PROCEDURE rates(v (mV)) { :callable from hoc
+    LOCAL a,qt
+    qt = q10^((celsius-24)/10)
+
+    a = alpn(v)
+    ninf = 1/(1 + a)
+    taun = betn(v)/(qt*a0n*(1+a))
+    if (taun<nmin) {
+        taun=nmin
+    }
+
+    a = alpl(v)
+    linf = 1/(1+ a)
+    taul = 0.26*(v+50-sh)/qtl
+    if (taul<lmin/qtl) {
+        taul=lmin/qtl
+    }
+}
diff --git a/netpyne/tutorials/mod/kapcb.mod b/netpyne/tutorials/mod/kapcb.mod
new file mode 100755
index 000000000..53935544a
--- /dev/null
+++ b/netpyne/tutorials/mod/kapcb.mod
@@ -0,0 +1,120 @@
+TITLE K-A channel from Klee Ficker and Heinemann
+: modified by Brannon and Yiota Poirazi (poirazi@LNC.usc.edu)
+: to account for Hoffman et al 1997 proximal region kinetics
+: used only in soma and sections located < 100 microns from the soma
+
+NEURON {
+	SUFFIX kapcb
+	USEION k READ ko, ki WRITE ik		:Changed from READ ek, 23/04/2010,Nassi
+        RANGE gkabar, ik
+        :GLOBAL ninf,linf,taul,taun,lmin
+}
+
+UNITS {
+	(mA) = (milliamp)
+	(mV) = (millivolt)
+}
+
+
+PARAMETER {                       :parameters that can be entered when function is called in cell-setup
+       	gkabar = 0      (mho/cm2) :initialized conductance
+        vhalfn = 11     (mV)      :activation half-potential
+        vhalfl = -56    (mV) 	  :inactivation half-potential
+	:vhalfl = -56    (mV) 	  :inactivation half-potential
+        a0n = 0.05      (/ms)     :parameters used
+        zetan = -1.5    (1)       :in calculation of (-1.5)
+        zetal = 3       (1)       :steady state values(3)
+        gmn = 0.55      (1)       :and time constants(0.55) change to get an effect on spike repolarization
+        gml = 1         (1)
+	:gml = 1         (1)
+	lmin = 2        (ms)
+	nmin = 0.1      (ms)
+	pw = -1         (1)
+	tq = -40	(mV)
+	qq = 5		(mV)
+	q10 = 5                   :temperature sensitivity
+
+}
+
+ASSIGNED {       :parameters needed to solve DE
+	v               (mV)
+        ek              (mV)      :K reversal potential  (reset in cell-setup.hoc)
+	celsius         (degC)
+	ik              (mA/cm2)
+        ninf
+        linf      
+        taul            (ms)
+        taun            (ms)
+	ko		(mM)
+	ki		(mM)
+}
+
+STATE {          :the unknown parameters to be solved in the DEs 
+	n l
+}
+
+LOCAL qt
+
+INITIAL {		:initialize the following parameter using rates()
+        qt = q10^((celsius-24)/10(degC))         : temprature adjustment factor
+	rates(v)
+	n = ninf
+	l = linf
+}
+
+BREAKPOINT {
+	SOLVE states METHOD cnexp
+	ek=25*log(ko/ki)			:Changed, added, 23/04/2010, Nassi
+	ik = gkabar*n*l*(v-ek)
+}
+
+DERIVATIVE states {
+	rates(v)
+        n' = (ninf - n)/taun
+        l' = (linf - l)/taul
+}
+
+
+
+PROCEDURE rates(v (mV)) {                  :callable from hoc
+        LOCAL a
+	
+        a = alpn(v)
+        ninf = 1/(1 + a)                   : activation variable steady state value
+        taun = betn(v)/(qt*a0n*(1+a))      : activation variable time constant
+	if (taun<nmin) {taun=nmin}         : time constant not allowed to be less than nmin
+        
+	a = alpl(v)
+        linf = 1/(1+ a)                    : inactivation variable steady state value
+	taul = 12 (ms)
+	:taul = 0.26(ms/mV)*(v+50)               : inactivation variable time constant
+	:if (taul<lmin) {taul=lmin}         : time constant not allowed to be less than lmin
+
+}
+
+FUNCTION alpn(v(mV)) { LOCAL zeta 
+  zeta = zetan+pw/(1+exp((v-tq)/qq))
+UNITSOFF
+  alpn = exp(1.e-3*zeta*(v-vhalfn)*9.648e4/(8.315*(273.16+celsius))) 
+UNITSON
+}
+
+FUNCTION betn(v(mV)) { LOCAL zeta
+  zeta = zetan+pw/(1+exp((v-tq)/qq))
+UNITSOFF
+  betn = exp(1.e-3*zeta*gmn*(v-vhalfn)*9.648e4/(8.315*(273.16+celsius))) 
+UNITSON
+}
+
+FUNCTION alpl(v(mV)) {
+UNITSOFF
+  alpl = exp(1.e-3*zetal*(v-vhalfl)*9.648e4/(8.315*(273.16+celsius))) 
+UNITSON
+}
+
+FUNCTION betl(v(mV)) {
+UNITSOFF
+  betl = exp(1.e-3*zetal*gml*(v-vhalfl)*9.648e4/(8.315*(273.16+celsius))) 
+UNITSON
+}
+
diff --git a/netpyne/tutorials/mod/kapin.mod b/netpyne/tutorials/mod/kapin.mod
new file mode 100755
index 000000000..0dae89109
--- /dev/null
+++ b/netpyne/tutorials/mod/kapin.mod
@@ -0,0 +1,124 @@
+TITLE K-A channel from Klee Ficker and Heinemann
+: modified by Brannon and Yiota Poirazi (poirazi@LNC.usc.edu)
+: to account for Hoffman et al 1997 proximal region kinetics
+: used only in soma and sections located < 100 microns from the soma
+
+
+NEURON {
+	SUFFIX kapin
+	USEION k READ ek WRITE ik
+        RANGE gkabar, ik
+        :GLOBAL ninf,linf,taul,taun,lmin
+}
+
+UNITS {
+	(mA) = (milliamp)
+	(mV) = (millivolt)
+
+}
+
+
+PARAMETER {                       :parameters that can be entered when function is called in cell-setup
+
+       	gkabar = 0      (mho/cm2) :initialized conductance
+        vhalfn = 11     (mV)      :activation half-potential
+        vhalfl = -56    (mV) 	  :inactivation half-potential
+	:vhalfl = -56    (mV) 	  :inactivation half-potential
+        a0n = 0.05      (/ms)     :parameters used
+        zetan = -1.5    (1)       :in calculation of (-1.5)
+        zetal = 3       (1)       :steady state values(3)
+        gmn = 0.55      (1)       :and time constants(0.55) change to get an effect on spike repolarization
+        gml = 1         (1)
+	:gml = 1         (1)
+	lmin = 2        (ms)
+	nmin = 0.1      (ms)
+	pw = -1         (1)
+	tq = -40	(mV)
+	qq = 5		(mV)
+	q10 = 5                   :temperature sensitivity
+}
+
+
+
+ 
+ASSIGNED {       :parameters needed to solve DE
+	v               (mV)
+        ek              (mV)      :K reversal potential  (reset in cell-setup.hoc)
+	celsius         (degC)
+	ik              (mA/cm2)
+        ninf
+        linf      
+        taul            (ms)
+        taun            (ms)
+}
+
+
+STATE {          :the unknown parameters to be solved in the DEs 
+	n l
+}
+
+LOCAL qt
+
+INITIAL {		:initialize the following parameter using rates()
+        qt = q10^((celsius-24)/10(degC))         : temprature adjustment factor
+	rates(v)
+	n = ninf
+	l = linf
+}
+
+BREAKPOINT {
+	SOLVE states METHOD cnexp
+:	ik = gkabar*n*l*(v+70)
+	ik = gkabar*n*l*(v-ek)
+}
+
+DERIVATIVE states {
+	rates(v)
+        n' = (ninf - n)/taun
+        l' = (linf - l)/taul
+}
+
+
+
+PROCEDURE rates(v (mV)) {                  :callable from hoc
+        LOCAL a
+	
+        a = alpn(v)
+        ninf = 1/(1 + a)                   : activation variable steady state value
+        taun = betn(v)/(qt*a0n*(1+a))      : activation variable time constant
+	if (taun<nmin) {taun=nmin}         : time constant not allowed to be less than nmin
+        
+	a = alpl(v)
+        linf = 1/(1+ a)                    : inactivation variable steady state value
+	taul = 12 (ms)
+	:taul = 0.26(ms/mV)*(v+50)               : inactivation variable time constant
+	:if (taul<lmin) {taul=lmin}         : time constant not allowed to be less than lmin
+
+}
+
+FUNCTION alpn(v(mV)) { LOCAL zeta 
+  zeta = zetan+pw/(1+exp((v-tq)/qq))
+UNITSOFF
+  alpn = exp(1.e-3*zeta*(v-vhalfn)*9.648e4/(8.315*(273.16+celsius))) 
+UNITSON
+}
+
+FUNCTION betn(v(mV)) { LOCAL zeta
+  zeta = zetan+pw/(1+exp((v-tq)/qq))
+UNITSOFF
+  betn = exp(1.e-3*zeta*gmn*(v-vhalfn)*9.648e4/(8.315*(273.16+celsius))) 
+UNITSON
+}
+
+FUNCTION alpl(v(mV)) {
+UNITSOFF
+  alpl = exp(1.e-3*zetal*(v-vhalfl)*9.648e4/(8.315*(273.16+celsius))) 
+UNITSON
+}
+
+FUNCTION betl(v(mV)) {
+UNITSOFF
+  betl = exp(1.e-3*zetal*gml*(v-vhalfl)*9.648e4/(8.315*(273.16+celsius))) 
+UNITSON
+}
+
diff --git a/netpyne/tutorials/mod/kc.mod b/netpyne/tutorials/mod/kc.mod
new file mode 100755
index 000000000..d96635157
--- /dev/null
+++ b/netpyne/tutorials/mod/kc.mod
@@ -0,0 +1,74 @@
+TITLE Potasium C type current for RD Traub, J Neurophysiol 89:909-921, 2003
+
+COMMENT
+
+	Implemented by Maciej Lazarewicz 2003 (mlazarew@seas.upenn.edu)
+
+ENDCOMMENT
+
+INDEPENDENT { t FROM 0 TO 1 WITH 1 (ms) }
+
+UNITS { 
+	(mV) = (millivolt) 
+	(mA) = (milliamp) 
+}
+ 
+NEURON { 
+	SUFFIX kc
+	USEION k READ ek WRITE ik
+	USEION ca READ cai
+	RANGE  gbar, ik
+}
+
+PARAMETER { 
+	gbar = 0.0 	(mho/cm2)
+	v ek 		(mV)  
+	cai		(1)
+} 
+
+ASSIGNED { 
+	ik 		(mA/cm2) 
+	alpha beta	(/ms)
+}
+ 
+STATE {
+	m
+}
+
+BREAKPOINT { 
+	SOLVE states METHOD cnexp
+	if( 0.004 * cai < 1 ) {
+		ik = gbar * m * 0.004 * cai * ( v - ek ) 
+	}else{
+		ik = gbar * m * ( v - ek ) 
+	}
+}
+ 
+INITIAL { 
+	settables(v) 
+	m = alpha / ( alpha + beta )
+	m = 0
+}
+ 
+DERIVATIVE states { 
+	settables(v) 
+	m' = alpha * ( 1 - m ) - beta * m 
+}
+
+UNITSOFF 
+
+PROCEDURE settables(v) { 
+	TABLE alpha, beta FROM -120 TO 40 WITH 641
+
+	if( v < -10.0 ) {
+		alpha = 2 / 37.95 * ( exp( ( v + 50 ) / 11 - ( v + 53.5 ) / 27 ) )
+
+		: Note that there is typo in the paper - missing minus sign in the front of 'v'
+		beta  = 2 * exp( ( - v - 53.5 ) / 27 ) - alpha
+	}else{
+		alpha = 2 * exp( ( - v - 53.5 ) / 27 )
+		beta  = 0
+	}
+}
+
+UNITSON
\ No newline at end of file
diff --git a/netpyne/tutorials/mod/kca.mod b/netpyne/tutorials/mod/kca.mod
new file mode 100644
index 000000000..21d6a5075
--- /dev/null
+++ b/netpyne/tutorials/mod/kca.mod
@@ -0,0 +1,100 @@
+: $Id: kca.mod,v 1.5 2004/06/08 21:07:12 billl Exp $
+
+COMMENT
+26 Ago 2002 Modification of original channel to allow variable time step and to correct an initialization error.
+Done by Michael Hines(michael.hines@yale.e) and Ruggero Scorcioni(rscorcio@gmu.edu) at EU Advance Course in Computational Neuroscience. Obidos, Portugal
+
+kca.mod
+
+Calcium-dependent potassium channel
+Based on
+Pennefather (1990) -- sympathetic ganglion cells
+taken from
+Reuveni et al (1993) -- neocortical cells
+
+Author: Zach Mainen, Salk Institute, 1995, zach@salk.edu
+
+ENDCOMMENT
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+NEURON {
+  SUFFIX kca
+  USEION k READ ek WRITE ik
+  USEION ca READ cai
+  RANGE i, n, gk, gmax
+  RANGE ninf, ntau
+  GLOBAL Ra, Rb, caix
+  GLOBAL q10, temp, tadj, vmin, vmax
+}
+
+UNITS {
+  (mA) = (milliamp)
+  (mV) = (millivolt)
+  (pS) = (picosiemens)
+  (um) = (micron)
+} 
+
+PARAMETER {
+  gmax = 10   	(pS/um2)	: 0.03 mho/cm2
+  v 		(mV)
+  cai  		(mM)
+  caix = 1	
+  
+  Ra   = 0.01	(/ms)		: max act rate  
+  Rb   = 0.02	(/ms)		: max deact rate 
+
+  dt		(ms)
+  celsius		(degC)
+  temp = 23	(degC)		: original temp 	
+  q10  = 2.3			: temperature sensitivity
+
+  vmin = -120	(mV)
+  vmax = 100	(mV)
+} 
+
+
+ASSIGNED {
+  a		(/ms)
+  b		(/ms)
+  i 		(mA/cm2)
+  ik 		(mA/cm2)
+  gk		(pS/um2)
+  ek		(mV)
+  ninf
+  ntau 		(ms)	
+  tadj
+}
+
+
+STATE { n }
+
+INITIAL { 
+  tadj = q10^((celsius - temp)/10)
+  rates(cai)
+  n = ninf
+}
+
+BREAKPOINT {
+  SOLVE states METHOD cnexp
+  gk = tadj*gmax*n
+  i = (1e-4) * gk * (v - ek)
+  ik = i
+} 
+
+LOCAL nexp
+
+DERIVATIVE states {   :Computes state variable n 
+  rates(cai)      :             at the current v and dt.
+  n' =  (ninf-n)/ntau
+
+}
+
+PROCEDURE rates(cai(mM)) {  
+  a = Ra * cai : ^caix 1
+  b = Rb
+  ntau = 1/tadj/(a+b)
+  ninf = a/(a+b)
+  :        tinc = -dt * tadj
+  :        nexp = 1 - exp(tinc/ntau)
+}
diff --git a/netpyne/tutorials/mod/kctin.mod b/netpyne/tutorials/mod/kctin.mod
new file mode 100755
index 000000000..3b71c94cc
--- /dev/null
+++ b/netpyne/tutorials/mod/kctin.mod
@@ -0,0 +1,97 @@
+
+TITLE Kct current
+
+COMMENT Equations from 
+		  Shao L.R., Halvorsrud R., Borg-Graham L., Storm J.F. The role of BK-type Ca2_-dependent K+ channels in spike broadening during repetitive firing in rat hippocampal pyramidal cells J.Physiology (1999),521:135-146 
+		  
+		  The Krasnow Institute
+		  George Mason University
+
+Copyright	  Maciej Lazarewicz, 2001
+		  (mlazarew@gmu.edu)
+		  All rights reserved.
+ENDCOMMENT
+
+NEURON {
+	:SUFFIX  mykca
+	SUFFIX kctin
+	USEION k READ ek WRITE ik
+	USEION ca READ cai   
+	RANGE  gkcbar,ik
+}
+
+UNITS {
+	(molar) = (1/liter)
+	(mM)	= (millimolar)
+	(S)  	= (siemens)
+	(mA) 	= (milliamp)
+	(mV) 	= (millivolt)
+}
+
+PARAMETER {
+        gkcbar	= 0	 (S/cm2)
+	:gkbar	= 1.0e-3 (S/cm2)
+}
+
+ASSIGNED {
+        v       (mV)
+        cai	(mM)     :26/10/04   htan se sxolio
+	celsius		 (degC)
+	ek		(mV)
+	ik	(mA/cm2)
+	k1	(/ms)
+	k2	(/ms)
+	k3	(/ms)
+	k4	(/ms)
+	q10	(1)
+}
+
+STATE { cst ost ist }
+
+BREAKPOINT { 
+	SOLVE kin METHOD sparse
+	ik = gkcbar * ost *( v - ek ) 
+}
+
+INITIAL {
+	SOLVE kin STEADYSTATE sparse
+}
+
+KINETIC kin {
+:	rates(v, cani)
+	rates(v, cai)
+	~cst<->ost  (k3,k4)
+	~ost<->ist  (k1,0.0)
+	~ist<->cst  (k2,0.0)
+	CONSERVE cst+ost+ist=1
+}
+
+:change feb8th for pfc
+:PROCEDURE rates( v(mV), cani(mM)) {
+PROCEDURE rates( v(mV), cai(mM)) {
+:	 k1=alp( 0.1, v,  -10.0,   1.0 ) : original
+	 k1=alp( 0.01, v,  -10.0,   1.0 ) :increases the current
+	 k2=alp( 0.1, v, -120.0, -10.0 ) :original (0.1, -120, -10)
+:	 k3=alpha( 0.001, 1.0, v, -20.0, 7.0 ) *1.0e8* ( cai*1.0(/mM) )^3  :original
+	 k3=alpha( 0.001, 1.0, v, -20.0, 7.0 ) *1.0e8* (cai*1.0(/mM) )^3
+	 :k3 changes the attenuation
+	 k4=alp( 0.2, v, -44.0,  -5.0 ) :original
+:	 k4=alp( 0.2, v, -44.0,  -5.0 )
+}
+
+FUNCTION alpha( tmin(ms), tmax(ms), v(mV), vhalf(mV), k(mV) )(/ms){
+        alpha = 1.0 / ( tmin + 1.0 / ( 1.0 / (tmax-tmin) + exp((v-vhalf)/k)*1.0(/ms) ) )
+}
+
+FUNCTION alp( tmin(ms), v(mV), vhalf(mV), k(mV) )(/ms){
+        alp = 1.0 / ( tmin + exp( -(v-vhalf) / k )*1.0(ms) )
+}
+
+
+
+
+
+
+
+
+
diff --git a/netpyne/tutorials/mod/kdmc_BS.mod b/netpyne/tutorials/mod/kdmc_BS.mod
new file mode 100644
index 000000000..8fa27bd98
--- /dev/null
+++ b/netpyne/tutorials/mod/kdmc_BS.mod
@@ -0,0 +1,93 @@
+TITLE K-D channel with activation for motor cortex
+: K-D current with activation, for motor cortex pyramidal neurons, per Miller et al. (2008)
+: Based on K-A current K-A current for Mitral Cells from Wang et al (1996), by M.Migliore Jan. 2002
+: 2011-02-25 Ben Suter, first version, using MM's kamt.mod as a starting template
+: 2011-09-18 Ben Suter, set default parameter values to those found from MRF optimization for BS0284 model
+:
+: :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
+: Copyright 2011, Benjamin Suter
+: Used in model of corticospinal neuron BS0284 and published as:
+:  "Intrinsic electrophysiology of mouse corticospinal neurons: a characteristic set of features embodied in a realistic computational model"
+:  by Benjamin Suter, Michele Migliore, and Gordon Shepherd
+:  Submitted September 2011
+: :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
+
+
+NEURON {
+    THREADSAFE
+    SUFFIX kdmc
+    USEION k READ ek WRITE ik
+    RANGE  gbar, minf, mtau, hinf, htau, ik
+    GLOBAL taumin
+}
+
+PARAMETER {
+    gbar    = 0.002     (mho/cm2)
+
+    celsius
+    ek                  (mV)   : must be explicitly def. in hoc
+    v                   (mV)
+
+    : activation
+    vhalfmt = -25       : original -20   : rough estimate from Miller et al (2008) Fig. 3D I-V curve
+    km      = 14        : manual fit to match this I-V curve
+
+    : inactivation
+    : NOTE: These values are still quite arbitrary (but get about the correct htau at -40 and -30 mV
+    vhalfh  = -5        : original -55
+    zetah   = 0.02      : original 0.05
+    gmh     = 0.2       : original 0.7
+    a0h     = 0.00058   : original 0.00055
+    taumin	= 0.1	(ms)		: minimal value of time constant
+
+    vhalfht = -100      : original -88   : measured by Storm (1988)
+    kh      = 8         : manual fit to match inactivation curve in Storm (1988) and Johnston+Wu textbook
+
+    q10     = 3
+}
+
+
+UNITS {
+    (mA) = (milliamp)
+    (mV) = (millivolt)
+    (pS) = (picosiemens)
+    (um) = (micron)
+}
+
+ASSIGNED {
+    ik      (mA/cm2)
+    minf        mtau (ms)
+    hinf        htau (ms)
+}
+
+
+STATE { m h }
+
+BREAKPOINT {
+    SOLVE states METHOD cnexp
+    ik  = gbar*m*h*(v - ek)
+}
+
+INITIAL {
+    trates(v)
+    m   = minf
+    h   = hinf
+}
+
+DERIVATIVE states {
+    trates(v)
+    m'  = (minf-m)/mtau
+    h'  = (hinf-h)/htau
+}
+
+PROCEDURE trates(v) {
+    LOCAL qt
+    qt   = q10^((celsius-34)/10)
+
+    minf = 1/(1 + exp(-(v-vhalfmt)/km))
+    mtau = 1
+
+    hinf = 1/(1 + exp((v-vhalfht)/kh))
+    htau = exp(zetah*gmh*(v-vhalfh)) / (qt*a0h*(1 + exp(zetah*(v-vhalfh))))
+    if(htau < taumin) { htau = taumin } 	: min value of time constant
+}
diff --git a/netpyne/tutorials/mod/kdr_BS.mod b/netpyne/tutorials/mod/kdr_BS.mod
new file mode 100644
index 000000000..971fbbf92
--- /dev/null
+++ b/netpyne/tutorials/mod/kdr_BS.mod
@@ -0,0 +1,89 @@
+TITLE K-DR channel
+: from Klee Ficker and Heinemann
+: modified to account for Dax et al.
+: M.Migliore 1997
+: thread-safe 2010-05-31 Ben Suter
+: 2010-11-07 Ben Suter, removing "kdr" from parameter names, reformatting, setting sh = 0 (was 24 mV)
+:
+: :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
+: Copyright 2011, Benjamin Suter (for changes only)
+: Used in model of corticospinal neuron BS0284 and published as:
+:  "Intrinsic electrophysiology of mouse corticospinal neurons: a characteristic set of features embodied in a realistic computational model"
+:  by Benjamin Suter, Michele Migliore, and Gordon Shepherd
+:  Submitted September 2011
+: :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
+
+
+UNITS {
+    (mA) = (milliamp)
+    (mV) = (millivolt)
+}
+
+PARAMETER {
+    v                   (mV)
+    celsius             (degC)
+    ek                  (mV)     : must be explicitely def. in hoc
+
+    gbar    = 0.003     (mho/cm2)
+    vhalfn  = 13        (mV)
+    a0n     = 0.0075    (/ms)
+    zetan   = -3        (1)
+    gmn     = 0.7       (1)
+    nmax    = 20        (1)
+    q10     = 1
+    sh      = 0
+}
+
+NEURON {
+    THREADSAFE
+    SUFFIX kdr
+    USEION k READ ek WRITE ik
+    RANGE g, gbar, sh, ninf, taun, vhalfn, ik
+}
+
+STATE {
+    n
+}
+
+ASSIGNED {
+    ik      (mA/cm2)
+    ninf
+    g
+    taun
+}
+
+BREAKPOINT {
+    SOLVE states METHOD cnexp
+    g = gbar*n
+    ik = g*(v-ek)
+}
+
+INITIAL {
+    rates(v)
+    n=ninf
+}
+
+FUNCTION alpn(v(mV)) {
+    alpn = exp(1.e-3*zetan*(v-vhalfn-sh)*9.648e4/(8.315*(273.16+celsius)))
+}
+
+FUNCTION betn(v(mV)) {
+    betn = exp(1.e-3*zetan*gmn*(v-vhalfn-sh)*9.648e4/(8.315*(273.16+celsius)))
+}
+
+DERIVATIVE states {     : exact when v held constant; integrates over dt step
+    rates(v)
+    n' = (ninf - n)/taun
+}
+
+PROCEDURE rates(v (mV)) { :callable from hoc
+    LOCAL a,qt
+    qt = q10^((celsius-24)/10)
+
+    a = alpn(v)
+    ninf = 1/(1+a)
+    taun = betn(v)/(qt*a0n*(1+a))
+    if (taun<nmax) {
+        taun = nmax/qt
+    }
+}
diff --git a/netpyne/tutorials/mod/kdrcurrent.mod b/netpyne/tutorials/mod/kdrcurrent.mod
new file mode 100644
index 000000000..34d0c26c2
--- /dev/null
+++ b/netpyne/tutorials/mod/kdrcurrent.mod
@@ -0,0 +1,61 @@
+: $Id: CA1ikdr.mod,v 1.2 2010/12/01 05:10:52 samn Exp $ 
+TITLE IKDR CA1
+
+UNITS {
+  (mA) = (milliamp)
+  (mV) = (millivolt)
+}
+ 
+NEURON {
+  SUFFIX kdrcurrent
+  NONSPECIFIC_CURRENT ik
+  RANGE g, e, ninf, ntau, vshift
+}
+ 
+PARAMETER {
+  celsius	(degC)
+  g = 0.010	(mho/cm2)
+  e = -90	(mV)
+  vshift = -13
+}
+ 
+STATE {
+  n 
+}
+ 
+ASSIGNED {
+  v	  (mV)
+  ik	  (mA/cm2) 
+  ninf
+  ntau    (ms)
+}
+
+PROCEDURE iassign () { ik=g*n*(v-e) }
+ 
+BREAKPOINT {
+  SOLVE states METHOD cnexp
+  iassign()
+}
+ 
+DERIVATIVE states { 
+  rates(v)
+  n'= (ninf- n)/ ntau
+}
+
+INITIAL { 
+  rates(v)
+  n = ninf
+  iassign()
+}
+
+PROCEDURE rates(v (mV)) {
+  LOCAL  a, b
+  UNITSOFF
+  a = exp(-0.11*(v-13))
+  b = exp(-0.08*(v-13)) 	
+  ntau=50*b/(1+a)
+  if (ntau<2) {ntau=2}
+  ninf=1/(1+a)
+  UNITSON
+}
+
diff --git a/netpyne/tutorials/mod/kdrin.mod b/netpyne/tutorials/mod/kdrin.mod
new file mode 100755
index 000000000..b3ba940fa
--- /dev/null
+++ b/netpyne/tutorials/mod/kdrin.mod
@@ -0,0 +1,103 @@
+: Delayed rectifier K+ channel
+
+NEURON {
+	SUFFIX kdrin
+	USEION k READ ki, ko WRITE ik
+	RANGE gkdrbar, ik, gk
+	
+}
+
+UNITS {
+	(mA) = (milliamp)
+	(mV) = (millivolt)
+	
+}
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+PARAMETER {
+	v (mV)
+	dt (ms)
+	gkdrbar= 0.0338 (mho/cm2) <0,1e9>
+	
+	
+}
+
+STATE {
+	n
+}
+
+ASSIGNED {
+	ik (mA/cm2)
+	inf
+	tau (ms)
+	gk (mho/cm2)
+	ek (mV)
+	ki (mM)
+	ko (mM)
+
+}
+
+
+INITIAL {
+	rate(v)
+	n = inf
+}
+
+BREAKPOINT {
+	SOLVE states METHOD cnexp
+	gk= gkdrbar*n*n*n*n
+	ek = 25 * log(ko/ki)
+	ik = gk*(v-ek)
+	
+}
+
+DERIVATIVE states {
+	rate(v)
+	n' = (inf-n)/tau
+}
+
+UNITSOFF
+
+FUNCTION alf(v){ LOCAL va 
+	
+	   va=v-13
+	if (fabs(va)<1e-04){
+	   va=va+0.0001
+		alf= (-0.018*va)/(-1+exp(-(va/25)))
+	} else {
+	  	alf = (-0.018*(v-13))/(-1+exp(-((v-13)/25)))
+	}
+}
+
+
+FUNCTION bet(v) { LOCAL vb 
+	
+	  vb=v-23
+	if (fabs(vb)<1e-04){
+	  vb=vb+0.0001
+		bet= (0.0054*vb)/(-1+exp(vb/12))
+	} else {
+	  	bet = (0.0054*(v-23))/(-1+exp((v-23)/12))
+	}
+}	
+
+
+
+
+
+
+PROCEDURE rate(v (mV)) {LOCAL q10, sum, aa, ab
+	
+	aa=alf(v) ab=bet(v) 
+	
+	sum = aa+ab
+	inf = aa/sum
+	tau = 1/(sum)
+	
+	
+}
+
+UNITSON	
+
+
+
diff --git a/netpyne/tutorials/mod/km.mod b/netpyne/tutorials/mod/km.mod
new file mode 100755
index 000000000..c6843ca8e
--- /dev/null
+++ b/netpyne/tutorials/mod/km.mod
@@ -0,0 +1,97 @@
+: $Id: km.mod,v 1.5 2004/06/08 21:07:12 billl Exp $
+
+COMMENT
+26 Ago 2002 Modification of original channel to allow variable time step and to correct an initialization error.
+Done by Michael Hines(michael.hines@yale.e) and Ruggero Scorcioni(rscorcio@gmu.edu) at EU Advance Course in Computational Neuroscience. Obidos, Portugal
+
+km.mod
+
+Potassium channel, Hodgkin-Huxley style kinetics
+Based on I-M (muscarinic K channel)
+Slow, noninactivating
+
+Author: Zach Mainen, Salk Institute, 1995, zach@salk.edu
+
+ENDCOMMENT
+
+NEURON {
+  SUFFIX km
+  USEION k READ ek WRITE ik
+  RANGE n, gk, gmax, i
+  RANGE ninf, ntau, tadj
+  GLOBAL Ra, Rb
+  GLOBAL q10, temp, vmin, vmax
+}
+
+UNITS {
+  (mA) = (milliamp)
+  (mV) = (millivolt)
+  (pS) = (picosiemens)
+  (um) = (micron)
+} 
+
+PARAMETER {
+  gmax = 10   	(pS/um2)	: 0.03 mho/cm2
+  v 		(mV)
+  
+  tha  = -30	(mV)		: v 1/2 for inf
+  qa   = 9	(mV)		: inf slope		
+  
+  Ra   = 0.001	(/ms)		: max act rate  (slow)
+  Rb   = 0.001	(/ms)		: max deact rate  (slow)
+
+  dt		(ms)
+  celsius		(degC)
+  temp = 23	(degC)		: original temp 	
+  q10  = 2.3			: temperature sensitivity
+
+  vmin = -120	(mV)
+  vmax = 100	(mV)
+} 
+
+
+ASSIGNED {
+  a		(/ms)
+  b		(/ms)
+  i 		(mA/cm2)
+  ik 		(mA/cm2)
+  gk		(pS/um2)
+  ek		(mV)
+  ninf
+  ntau (ms)	
+  tadj
+}
+
+STATE { n }
+
+INITIAL { 
+  tadj = q10^((celsius - temp)/10)
+  rates(v)
+  n = ninf
+}
+
+BREAKPOINT {
+  SOLVE states METHOD cnexp
+  gk = tadj*gmax*n
+  i = (1e-4) * gk * (v - ek)
+  ik = i
+} 
+
+LOCAL nexp
+
+DERIVATIVE states {   :Computes state variable n 
+  rates(v)      :             at the current v and dt.
+  n' = (ninf-n)/ntau
+
+}
+
+PROCEDURE rates(v) {  :Computes rate and other constants at current v.
+  :Call once from HOC to initialize inf at resting v.
+
+  a = Ra * (v - tha) / (1 - exp(-(v - tha)/qa))
+  b = -Rb * (v - tha) / (1 - exp((v - tha)/qa))
+
+  ntau = 1/tadj/(a+b)
+  ninf = a/(a+b)
+}
+
diff --git a/netpyne/tutorials/mod/km2.mod b/netpyne/tutorials/mod/km2.mod
new file mode 100755
index 000000000..ff3a9b2dd
--- /dev/null
+++ b/netpyne/tutorials/mod/km2.mod
@@ -0,0 +1,60 @@
+TITLE Potasium M type current for RD Traub, J Neurophysiol 89:909-921, 2003
+
+COMMENT
+
+	Implemented by Maciej Lazarewicz 2003 (mlazarew@seas.upenn.edu)
+
+ENDCOMMENT
+
+INDEPENDENT { t FROM 0 TO 1 WITH 1 (ms) }
+
+UNITS { 
+	(mV) = (millivolt) 
+	(mA) = (milliamp) 
+}
+ 
+NEURON { 
+	SUFFIX km2
+	USEION k READ ek WRITE ik
+	RANGE gbar, ik
+}
+
+PARAMETER { 
+	gbar = 0.0 	(mho/cm2)
+	v ek 		(mV)  
+}
+ 
+ASSIGNED { 
+	ik 		(mA/cm2) 
+	alpha beta	(/ms)
+}
+ 
+STATE {
+	m
+}
+
+BREAKPOINT { 
+	SOLVE states METHOD cnexp
+	ik = gbar * m * ( v - ek ) 
+}
+ 
+INITIAL { 
+	settables(v) 
+	m = alpha / ( alpha + beta )
+	m = 0
+}
+ 
+DERIVATIVE states { 
+	settables(v) 
+	m' = alpha * ( 1 - m ) - beta * m 
+}
+
+UNITSOFF 
+
+PROCEDURE settables(v) { 
+	TABLE alpha, beta FROM -120 TO 40 WITH 641
+	alpha = 0.02 / ( 1 + exp( ( -v - 20 ) / 5 ) )
+	beta  = 0.01 * exp( ( -v - 43 ) / 18 )
+}
+
+UNITSON
\ No newline at end of file
diff --git a/netpyne/tutorials/mod/kv.mod b/netpyne/tutorials/mod/kv.mod
new file mode 100644
index 000000000..202819d95
--- /dev/null
+++ b/netpyne/tutorials/mod/kv.mod
@@ -0,0 +1,102 @@
+: $Id: kv.mod,v 1.9 2004/07/28 21:25:39 billl Exp $
+
+COMMENT
+26 Ago 2002 Modification of original channel to allow variable time step and to correct an initialization error.
+Done by Michael Hines(michael.hines@yale.e) and Ruggero Scorcioni(rscorcio@gmu.edu) at EU Advance Course in Computational Neuroscience. Obidos, Portugal
+
+kv.mod
+
+Potassium channel, Hodgkin-Huxley style kinetics
+Kinetic rates based roughly on Sah et al. and Hamill et al. (1991)
+
+Author: Zach Mainen, Salk Institute, 1995, zach@salk.edu
+
+ENDCOMMENT
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+NEURON {
+  SUFFIX kv
+  USEION k READ ek WRITE ik
+  RANGE  n, i, gk, gmax
+  GLOBAL ninf, ntau
+  GLOBAL Ra, Rb
+  GLOBAL q10, temp, tadj
+}
+
+UNITS {
+  (mA) = (milliamp)
+  (mV) = (millivolt)
+  (pS) = (picosiemens)
+  (um) = (micron)
+} 
+
+PARAMETER {
+  gmax = 5   	(pS/um2)	: 0.03 mho/cm2
+  v 		(mV)
+  
+  tha  = 25	(mV)		: v 1/2 for inf
+  qa   = 9	(mV)		: inf slope		
+  
+  Ra   = 0.02	(/ms)		: max act rate
+  Rb   = 0.002	(/ms)		: max deact rate	
+
+  dt		(ms)
+  celsius		(degC)
+  temp = 23	(degC)		: original temp 	
+  q10  = 2.3			: temperature sensitivity
+
+} 
+
+
+ASSIGNED {
+  a		(/ms)
+  b		(/ms)
+  i 		(mA/cm2)
+  ik 		(mA/cm2)
+  gk		(pS/um2)
+  ek		(mV)
+  ninf
+  ntau (ms)	
+  tadj
+}
+
+
+STATE { n }
+
+INITIAL { 
+  tadj = q10^((celsius - temp)/10)
+  rates(v)
+  n = ninf
+}
+
+BREAKPOINT {
+  SOLVE states METHOD cnexp
+  gk = tadj*gmax*n
+  i = (1e-4) * gk * (v - ek)
+  ik = i
+} 
+
+
+
+DERIVATIVE  states {   :Computes state variable n 
+  rates(v)      :             at the current v and dt.
+  n' =  (ninf-n)/ntau
+}
+
+PROCEDURE rates(v) {  :Computes rate and other constants at current v.
+  :Call once from HOC to initialize inf at resting v.
+
+  a = trap0(v,tha,Ra,qa)
+  b = trap0(v,tha,-Rb,-qa)
+  ntau = 1/tadj/(a+b)
+  ninf = a/(a+b)
+}
+
+FUNCTION trap0(v,th,a,q) {
+  if (fabs(v-th) > 1e-6) {
+    trap0 = a * (v - th) / (1 - exp(-(v - th)/q))
+  } else {
+    trap0 = a * q
+  }
+}	
diff --git a/netpyne/tutorials/mod/lfp.mod b/netpyne/tutorials/mod/lfp.mod
new file mode 100644
index 000000000..975a4dad1
--- /dev/null
+++ b/netpyne/tutorials/mod/lfp.mod
@@ -0,0 +1,49 @@
+: lfp.mod
+
+COMMENT
+LFPsim - Simulation scripts to compute Local Field Potentials (LFP) from cable compartmental models of neurons and networks implemented in NEURON simulation environment.
+
+LFPsim works reliably on biophysically detailed multi-compartmental neurons with ion channels in some or all compartments.
+
+Last updated 12-March-2016
+Developed by : Harilal Parasuram & Shyam Diwakar
+Computational Neuroscience & Neurophysiology Lab, School of Biotechnology, Amrita University, India.
+Email: harilalp@am.amrita.edu; shyam@amrita.edu
+www.amrita.edu/compneuro 
+ENDCOMMENT
+
+NEURON {
+	SUFFIX lfp
+	POINTER transmembrane_current
+	RANGE lfp_line,lfp_point,lfp_rc,initial_part_point, initial_part_line, initial_part_rc
+	
+}
+
+
+ASSIGNED {
+
+	initial_part_line 
+	initial_part_rc
+	transmembrane_current 
+	lfp_line
+	lfp_point
+	lfp_rc
+	initial_part_point
+
+
+}
+
+BREAKPOINT { 
+
+	:Point Source Approximation 	
+	lfp_point =   transmembrane_current * initial_part_point * 1e-1   : So the calculated signal will be in nV
+
+	:Line Source Approximation
+	lfp_line =   transmembrane_current * initial_part_line  * 1e-1  : So the calculated signal will be in nV
+
+	:RC
+	lfp_rc =   transmembrane_current * initial_part_rc * 1e-3 : So the calculated signal will be in nV
+
+}
+
+
diff --git a/netpyne/tutorials/mod/mea.mod b/netpyne/tutorials/mod/mea.mod
new file mode 100644
index 000000000..c237b4b1f
--- /dev/null
+++ b/netpyne/tutorials/mod/mea.mod
@@ -0,0 +1,89 @@
+: mea.mod
+
+COMMENT
+LFPsim - Simulation scripts to compute Local Field Potentials (LFP) from cable compartmental models of neurons and networks implemented in NEURON simulation environment.
+
+LFPsim works reliably on biophysically detailed multi-compartmental neurons with ion channels in some or all compartments.
+
+Last updated 12-March-2016
+Developed by : Harilal Parasuram & Shyam Diwakar
+Computational Neuroscience & Neurophysiology Lab, School of Biotechnology, Amrita University, India.
+Email: harilalp@am.amrita.edu; shyam@amrita.edu
+www.amrita.edu/compneuro 
+ENDCOMMENT
+
+NEURON {
+	SUFFIX mea
+	POINTER transmembrane_current_m
+	RANGE mea_line0,mea_line1,mea_line2,mea_line3,mea_line4,mea_line5,mea_line6,mea_line7,mea_line8,mea_line9,mea_line10,mea_line11,mea_line12,mea_line13,mea_line14,mea_line15
+	RANGE initial_part_line0,initial_part_line1,initial_part_line2,initial_part_line3,initial_part_line4,initial_part_line5,initial_part_line6,initial_part_line7,initial_part_line8,initial_part_line9,initial_part_line10,initial_part_line11,initial_part_line12,initial_part_line13,initial_part_line14,initial_part_line15	
+
+}
+
+PARAMETER {
+	: default values put here
+
+	}
+
+ASSIGNED {
+ 
+	transmembrane_current_m 
+	initial_part_line0
+	initial_part_line1
+	initial_part_line2
+	initial_part_line3
+	initial_part_line4
+	initial_part_line5
+	initial_part_line6
+	initial_part_line7
+	initial_part_line8
+	initial_part_line9
+	initial_part_line10
+	initial_part_line11
+	initial_part_line12
+	initial_part_line13
+	initial_part_line14
+	initial_part_line15
+
+	mea_line0
+	mea_line1
+	mea_line2
+	mea_line3
+	mea_line4
+	mea_line5
+	mea_line6
+	mea_line7
+	mea_line8
+	mea_line9
+	mea_line10
+	mea_line11
+	mea_line12
+	mea_line13
+	mea_line14
+	mea_line15
+
+
+}
+
+BREAKPOINT { 
+
+	:Line Source Approximation
+	mea_line0 = transmembrane_current_m * initial_part_line0 * 1e-1 	: 1e-1 (mA to uA) : calculated potential will be in uV
+	mea_line1 = transmembrane_current_m * initial_part_line1 * 1e-1 
+	mea_line2 = transmembrane_current_m * initial_part_line2 * 1e-1 
+	mea_line3 = transmembrane_current_m * initial_part_line3 * 1e-1 
+	mea_line4 = transmembrane_current_m * initial_part_line4 * 1e-1 
+	mea_line5 = transmembrane_current_m * initial_part_line5 * 1e-1 
+	mea_line6 = transmembrane_current_m * initial_part_line6 * 1e-1 
+	mea_line7 = transmembrane_current_m * initial_part_line7 * 1e-1 
+	mea_line8 = transmembrane_current_m * initial_part_line8 * 1e-1 
+	mea_line9 = transmembrane_current_m * initial_part_line9 * 1e-1 
+	mea_line10 = transmembrane_current_m * initial_part_line10 * 1e-1 
+	mea_line11 = transmembrane_current_m * initial_part_line11 * 1e-1 
+	mea_line12 = transmembrane_current_m * initial_part_line12 * 1e-1 
+	mea_line13 = transmembrane_current_m * initial_part_line13 * 1e-1 
+	mea_line14 = transmembrane_current_m * initial_part_line14 * 1e-1 
+	mea_line15 = transmembrane_current_m * initial_part_line15 * 1e-1 
+
+}
+
diff --git a/netpyne/tutorials/mod/misc.h b/netpyne/tutorials/mod/misc.h
new file mode 100644
index 000000000..8a4ed6038
--- /dev/null
+++ b/netpyne/tutorials/mod/misc.h
@@ -0,0 +1,134 @@
+// $Id: misc.h,v 1.38 2011/11/02 15:26:48 billl Exp $
+
+#include <stdlib.h>
+#include <math.h>
+#include <limits.h> /* contains LONG_MAX */
+#include <time.h>
+#include <sys/time.h> 
+#include <values.h>
+#include <pthread.h>
+
+#if !defined(t)
+  #define _pval pval
+#endif
+
+typedef struct LISTVEC {
+  int isz;
+  Object* pL;
+  double** pv;
+  unsigned int* plen;
+  unsigned int* pbuflen;
+} ListVec;
+
+typedef struct BVEC {
+ int size;
+ int bufsize;
+ short *x;
+ Object* o;
+} bvec;
+
+#define BYTEHEADER int _II__;  char *_IN__; char _OUT__[16]; int BYTESWAP_FLAG=0;
+#define BYTESWAP(_X__,_TYPE__) \
+    if (BYTESWAP_FLAG == 1) { \
+	_IN__ = (char *) &(_X__); \
+	for (_II__=0;_II__<sizeof(_TYPE__);_II__++) { \
+		_OUT__[_II__] = _IN__[sizeof(_TYPE__)-_II__-1]; } \
+	(_X__) = *((_TYPE__ *) &_OUT__); \
+    }
+
+#define UNCODE(_X_,_J_,_Y_) {(_Y_)=floor((_X_)/sc[(_J_)])/sc[4]; \
+                             (_Y_)=floor(sc[4]*((_Y_)-floor(_Y_))+0.5);}
+#define MIN(X,Y) ((X) < (Y) ? (X) : (Y))
+#define MAX(X,Y) ((X) > (Y) ? (X) : (Y))
+
+//square root of 2 * PI
+#define SQRT2PI 2.5066282746310002416
+//ln(2), base e log of 2
+#define LG2 0.69314718055994530941723212145818
+#define VRRY 200
+#define ISVEC(_OB__) (strncmp(hoc_object_name(_OB__),"Vector",6)==0)
+#define dmaxuint 4294967295. // for 32 bits
+
+// Andre Fentons cast designations
+typedef	unsigned char	ui1;	/* one byte unsigned integer */
+typedef	char		si1;	/* one byte signed integer */
+typedef unsigned short	ui2;	/* two byte unsigned integer */
+typedef short		si2;	/* two byte signed integer */
+typedef unsigned int	ui4;	/* four byte unsigned integer */ 
+typedef int		si4;	/* four byte signed integer */ 
+typedef float		sf4;	/* four byte signed floating point number */ 
+typedef double		sf8;	/* eight byte signed floating point number */ 
+
+extern double ERR,GET,SET,OK,NOP,ALL,NEG,POS,CHK,NOZ,GTH,GTE,LTH,LTE,EQU;
+extern double EQV,EQW,EQX,NEQ,SEQ,RXP,IBE,EBI,IBI,EBE;
+
+extern double *vector_newsize();
+extern unsigned int  dcrsz;
+extern double       *dcr;
+extern double       *dcrset(int);
+extern unsigned int  scrsz;
+extern unsigned int *scr;
+extern unsigned int *scrset(int);
+extern unsigned int  iscrsz;
+extern int *iscr;
+extern int *iscrset(int);
+extern double BVBASE;
+extern double* hoc_pgetarg();
+extern void hoc_notify_iv();
+extern double hoc_call_func(Symbol*, int narg);
+extern FILE* hoc_obj_file_arg(int narg);
+extern Object** hoc_objgetarg();
+char *gargstr();
+char** hoc_pgargstr();
+extern void vector_resize();
+extern int vector_instance_px();
+extern void* vector_arg();
+extern double* vector_vec();
+extern int vector_buffer_size(void*);
+extern double hoc_epsilon;
+extern int stoprun;
+extern void set_seed();
+extern void dshuffle(double* x,int nx);
+extern void mcell_ran4_init(u_int32_t);
+extern double mcell_ran4(u_int32_t *idx1, double *x, unsigned int n, double range);
+extern int nrn_mlh_gsort();
+extern int ivoc_list_count(Object*);
+extern Object* ivoc_list_item(Object*, int);
+extern int list_vector_px2();
+extern int hoc_is_double_arg(int narg);
+extern int hoc_is_str_arg(int narg);
+extern int hoc_is_object_arg(int narg);
+extern int hoc_is_pdouble_arg(int narg);
+extern Symbol *hoc_get_symbol(char *);
+extern Symbol *hoc_lookup(const char*);
+extern Point_process* ob2pntproc(Object*);
+
+extern char* hoc_object_name(Object*);
+extern int cmpdfn();
+extern int openvec(int, double **);
+int list_vector_px();
+double *list_vector_resize();
+static void hxe() { hoc_execerror("",0); }
+extern void FreeListVec(ListVec** pp);
+extern ListVec* AllocListVec(Object* p);
+extern ListVec* AllocILV(Object*, int, double *);
+void FillListVec(ListVec* p,double dval);
+void ListVecResize(ListVec* p,int newsz);
+extern short *nrn_artcell_qindex_;
+extern double nrn_event_queue_stats(double*);
+extern void clear_event_queue();
+
+static double sc[6];
+static FILE*  testout;
+
+//* in vecst.mod
+extern int** getint2D(int rows,int cols);
+extern void freeint2D(int*** ppp,int rows);
+extern double** getdouble2D(int rows,int cols);
+extern void freedouble2D(double*** ppp,int rows);
+extern double ismono1 (double *x, int n, int flag);
+
+//* in stats.mod
+double kcorfast(double* input1, double* input2, double* i1d , double* i2d,int n,double* ps);
+double Rktau (double* x, double* y, int n); // R version
+double kcorfast (double* input1, double* input2, double* i1d , double* i2d,int n,double* ps);
diff --git a/netpyne/tutorials/mod/nacurrent.mod b/netpyne/tutorials/mod/nacurrent.mod
new file mode 100644
index 000000000..43c458bb6
--- /dev/null
+++ b/netpyne/tutorials/mod/nacurrent.mod
@@ -0,0 +1,102 @@
+: $Id: CA1ina.mod,v 1.4 2010/11/30 19:50:00 samn Exp $ 
+TITLE INa CA1
+
+UNITS {
+  (mA) = (milliamp)
+  (mV) = (millivolt)
+}
+
+NEURON {
+  SUFFIX nacurrent
+  NONSPECIFIC_CURRENT ina
+  RANGE g, e, vi, ki
+  RANGE minf,hinf,iinf,mtau,htau,itau : testing
+}
+  
+PARAMETER {
+  celsius	    (degC)
+  g = 0.032   (mho/cm2)
+  e = 55	    (mV)
+  vi = -60    (mV)
+  ki = 0.8
+}
+ 
+STATE {
+  m
+  h
+  I
+}
+ 
+ASSIGNED {
+  i (mA/cm2)
+  ina	(mA/cm2) 
+  minf
+  mtau    (ms)
+  hinf
+  htau	(ms)
+  iinf
+  itau	(ms)
+  v	(mV) : testing
+}
+
+PROCEDURE iassign () { i=g*m*m*m*h*I*(v-e) ina=i}
+ 
+BREAKPOINT {
+  SOLVE states METHOD cnexp
+  iassign()
+}
+ 
+DERIVATIVE states { 
+  rates(v)
+  m' = (minf - m) / mtau
+  h' = (hinf - h) / htau
+  I' = (iinf - I) / itau	    
+}
+
+INITIAL { 
+  rates(v)
+  h = hinf
+  m = minf
+  I = iinf
+  iassign() : testing
+}
+
+PROCEDURE hrates(v (mV)) {
+  LOCAL  a, b
+  UNITSOFF
+  a = 0.03*(v+45)/(1-exp(-(v+45)/1.5))
+  b = 0.01*(v+45)/(exp((v+45)/1.5)-1)
+  htau=0.5/(a+b)
+  if (htau<0.5) {htau=0.5}
+  hinf=1/(1+exp((v+50)/4))
+  UNITSON
+}
+
+PROCEDURE Irates(v (mV)) {
+  LOCAL  a, b
+  UNITSOFF
+  a =	exp(0.45*(v+66))
+  b = exp(0.09*(v+66))
+  itau=3000*b/(1+a)
+  if (itau<10) {itau=10}
+  iinf=(1+ki*exp((v-vi)/2))/(1+exp((v-vi)/2))
+  UNITSON
+}
+
+PROCEDURE mrates(v (mV)) {
+  LOCAL  a, b
+  UNITSOFF
+  a = 0.4*(v+30)/(1-exp(-(v+30)/7.2))
+  b = 0.124*(v+30)/(exp((v+30)/7.2)-1) 	
+  mtau=0.5/(a+b)
+  if (mtau<0.02) {mtau=0.02}
+  minf=a/(a+b)
+  UNITSON
+}
+
+PROCEDURE rates(v (mV)) {
+  mrates(v)
+  hrates(v)
+  Irates(v)
+}
+
diff --git a/netpyne/tutorials/mod/naf.mod b/netpyne/tutorials/mod/naf.mod
new file mode 100755
index 000000000..6f936d048
--- /dev/null
+++ b/netpyne/tutorials/mod/naf.mod
@@ -0,0 +1,68 @@
+TITLE Sodium transient current for RD Traub, J Neurophysiol 89:909-921, 2003
+
+COMMENT
+
+	Implemented by Maciej Lazarewicz 2003 (mlazarew@seas.upenn.edu)
+
+ENDCOMMENT
+
+INDEPENDENT { t FROM 0 TO 1 WITH 1 (ms) }
+
+UNITS { 
+	(mV) = (millivolt) 
+	(mA) = (milliamp) 
+} 
+NEURON { 
+	SUFFIX naf
+	USEION na READ ena WRITE ina
+	RANGE gbar, ina
+}
+PARAMETER { 
+	fastNashift = -3.5 (mV)
+	gbar = 0.0 	   (mho/cm2)
+	v ena 		   (mV)  
+} 
+ASSIGNED { 
+	ina 		   (mA/cm2) 
+	minf hinf 	   (1)
+	mtau htau 	   (ms) 
+} 
+STATE {
+	m h
+}
+BREAKPOINT { 
+	SOLVE states METHOD cnexp
+	ina = gbar * m * m * m * h * ( v - ena ) 
+} 
+INITIAL { 
+	settables( v - fastNashift ) 
+	m = minf
+	m = 0
+	h  = hinf
+} 
+DERIVATIVE states { 
+	settables( v ) 
+	m' = ( minf - m ) / mtau 
+	h' = ( hinf - h ) / htau
+}
+
+UNITSOFF 
+
+PROCEDURE settables(v1(mV)) {
+
+	TABLE minf, hinf, mtau, htau  FROM -120 TO 40 WITH 641
+
+	minf  = 1 / ( 1 + exp( ( - ( v1 + fastNashift ) - 38 ) / 10 ) )
+	if( ( v1 + fastNashift ) < -30.0 ) {
+		mtau = 0.025 + 0.14 * exp( ( ( v1 + fastNashift ) + 30 ) / 10 )
+	} else{
+		mtau = 0.02 + 0.145 * exp( ( - ( v1 + fastNashift ) - 30 ) / 10 ) 
+	}
+
+	: hinf, and htau are shifted 3.5 mV comparing to the paper
+
+	hinf  = 1 / ( 1 + exp( ( ( v1 + fastNashift * 0 ) + 62.9 ) / 10.7 ) )
+	htau = 0.15 + 1.15 / ( 1 + exp( ( ( v1 + fastNashift * 0 ) + 37 ) / 15 ) )
+}
+
+UNITSON
\ No newline at end of file
diff --git a/netpyne/tutorials/mod/nafx.mod b/netpyne/tutorials/mod/nafx.mod
new file mode 100755
index 000000000..80ba6e79a
--- /dev/null
+++ b/netpyne/tutorials/mod/nafx.mod
@@ -0,0 +1,170 @@
+: Fast Na+ channel
+: added the 's' attenuation system from hha2.mod
+: Kiki Sidiropoulou
+: September 27, 2007
+
+NEURON {
+	SUFFIX Nafx
+	USEION na READ ena WRITE ina
+	RANGE gnafbar, ina, gna, ar2
+}
+
+UNITS {
+	(mA) = (milliamp)
+	(mV) = (millivolt)
+	
+}
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+PARAMETER {
+	v (mV)
+	dt (ms)
+	gnafbar	= 0 (mho/cm2)
+	:gnafbar= 0.086 (mho/cm2) <0,1e9>
+	ena = 55 (mV)
+	
+	:PARAMETERS FOR S ATTENUATION SYSTEM
+	taumin = 30 (ms)  :min activation time for "s" attenuation system
+        vhalfr =-60 (mV)       :half potential for "s" attenuation system, -60
+        vvh=-58		(mV) 
+ 	vvs = 2 (mV)
+	a0r = 0.0003 (/ms)
+        b0r = 0.0003 (/ms)
+       : a0r = 0.0003 (ms)
+        :b0r = 0.0003 (ms)
+        zetar = 12    
+	zetas = 12   
+        gmr = 0.2   
+	ar2 = 1.0               :initialized parameter for location-dependent
+                                :Na-conductance attenuation, "s", (ar=1 -> zero attenuation)
+}
+STATE {
+	m h s
+}
+ASSIGNED {
+	celsius (degC)
+	ina (mA/cm2)
+	minf 
+	hinf
+	sinf 
+	mtau (ms)
+	htau (ms)
+	stau (ms)
+	gna (mho/cm2)
+	
+}
+
+
+
+INITIAL {
+	rate(v, ar2)
+	m = minf
+	h = hinf
+	s = sinf
+}
+
+BREAKPOINT {
+	SOLVE states METHOD cnexp
+	gna = gnafbar*m*m*m*h*s
+	ina = gna*(v-55)
+	
+}
+
+DERIVATIVE states {
+	rate(v, ar2)
+	m' = (minf-m)/mtau
+	h' = (hinf-h)/htau
+	s' = (sinf-s)/stau
+}
+
+UNITSOFF
+
+FUNCTION malf( v){ LOCAL va 
+	va=v+28
+	:va=v+28
+	if (fabs(va)<1e-04){
+	   malf= -0.2816*(-9.3 + va*0.5)
+	   :malf= -0.2816*(-9.3 + va*0.5)
+	}else{
+	   malf = -0.2816*(v+28)/(-1+exp(-va/9.3))
+	}
+}
+
+
+FUNCTION mbet(v(mV))(/ms) { LOCAL vb 
+	vb=v+1
+	:vb=v+1
+	if (fabs(vb)<1e-04){
+	    mbet = 0.2464*(6+vb*0.5)
+	    :mbet = 0.2464*(6 + vb*0.5)
+	}else{
+	   mbet = 0.2464*(v+1)/(-1+exp(vb/6))	  :/(-1+exp((v+1)/6))
+	}
+	}	
+
+
+FUNCTION half(v(mV))(/ms) { LOCAL vc 
+	:vc=v+15.1
+	vc=v+40.1	:changed to 40.1 by kiki
+	if (fabs(vc)<1e-04){
+	   half=0.098*(20 + vc*0.5)
+	}else{
+	   half=0.098/exp(vc+43.1/20)  :43.1, also spike train attenuation
+}
+}
+
+
+FUNCTION hbet(v(mV))(/ms) { LOCAL vd
+	:vd=v+13.1
+	vd=v+13.1  :decreasing it increases the peak current
+	if (fabs(vd)<1e-04){
+	   hbet=1.4*(10 + vd*0.5)
+	}else{
+	   hbet=1.4/(1+exp(-(vd-13.1)/10))  :13.1 increasing it, increases the spike train attenuation and increases spike width
+} 
+}
+
+
+:FUNCTIONS FOR S 
+FUNCTION alpv(v(mV)) {
+         alpv = 1/(1+exp((v-vvh)/vvs))
+}
+
+
+FUNCTION alpr(v(mV)) {       :used in "s" activation system tau
+
+  alpr = exp(1.e-3*zetar*(v-vhalfr)*9.648e4/(8.315*(273.16+celsius))) 
+}
+
+FUNCTION betr(v(mV)) {       :used in "s" activation system tau
+
+  betr = exp(1.e-3*zetar*gmr*(v-vhalfr)*9.648e4/(8.315*(273.16+celsius))) 
+}
+
+
+
+PROCEDURE rate(v (mV),ar2) {LOCAL q10, msum, hsum, ma, mb, ha, hb,c
+	
+
+	ma=malf(v) mb=mbet(v) ha=half(v) hb=hbet(v)
+	
+	msum = ma+mb
+	minf = ma/msum
+	mtau = 1/(msum)
+	
+	
+	hsum=ha+hb
+	hinf=ha/hsum
+	htau = 1 / (hsum)
+
+	stau = betr(v)/(a0r*(1+alpr(v))) 
+	if (stau<taumin) {stau=taumin} :s activation tau
+	c = alpv(v)
+	sinf = c+ar2*(1-c) 	
+}
+
+	
+UNITSON
+
+
diff --git a/netpyne/tutorials/mod/nap.mod b/netpyne/tutorials/mod/nap.mod
new file mode 100755
index 000000000..867d016e3
--- /dev/null
+++ b/netpyne/tutorials/mod/nap.mod
@@ -0,0 +1,61 @@
+TITLE Sodium persistent current for RD Traub, J Neurophysiol 89:909-921, 2003
+
+COMMENT
+
+	Implemented by Maciej Lazarewicz 2003 (mlazarew@seas.upenn.edu)
+
+ENDCOMMENT
+
+INDEPENDENT { t FROM 0 TO 1 WITH 1 (ms) }
+
+UNITS { 
+	(mV) = (millivolt) 
+	(mA) = (milliamp) 
+} 
+NEURON { 
+	SUFFIX nap
+	USEION na READ ena WRITE ina
+	RANGE gbar, ina
+}
+
+PARAMETER { 
+	gbar = 0.0 	(mho/cm2)
+	v ena 		(mV)  
+} 
+ASSIGNED { 
+	ina 		(mA/cm2) 
+	minf 		(1)
+	mtau 		(ms) 
+} 
+STATE {
+	m
+}
+
+BREAKPOINT { 
+	SOLVE states METHOD cnexp
+	ina = gbar * m * ( v - ena ) 
+} 
+
+INITIAL { 
+	settables(v) 
+	m = minf
+	m = 0
+} 
+
+DERIVATIVE states { 
+	settables(v) 
+	m' = ( minf - m ) / mtau 
+}
+UNITSOFF
+ 
+PROCEDURE settables(v) { 
+	TABLE minf, mtau FROM -120 TO 40 WITH 641
+
+	minf  = 1 / ( 1 + exp( ( - v - 48 ) / 10 ) )
+	if( v < -40.0 ) {
+		mtau = 0.025 + 0.14 * exp( ( v + 40 ) / 10 )
+	}else{
+		mtau = 0.02 + 0.145 * exp( ( - v - 40 ) / 10 )
+	}
+}
+UNITSON
\ No newline at end of file
diff --git a/netpyne/tutorials/mod/nax_BS.mod b/netpyne/tutorials/mod/nax_BS.mod
new file mode 100644
index 000000000..b9b4cfd7b
--- /dev/null
+++ b/netpyne/tutorials/mod/nax_BS.mod
@@ -0,0 +1,115 @@
+TITLE nax
+: Na current for axon. No slow inact.
+: M.Migliore Jul. 1997
+: added sh to account for higher threshold M.Migliore, Apr.2002
+: thread-safe 2010-05-31 Ben Suter
+: 2010-11-07 Ben Suter reformatting, renaming thegna to g, setting sh = 0 (was 8 mV)
+:
+: :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
+: Copyright 2011, Benjamin Suter (for changes only)
+: Used in model of corticospinal neuron BS0284 and published as:
+:  "Intrinsic electrophysiology of mouse corticospinal neurons: a characteristic set of features embodied in a realistic computational model"
+:  by Benjamin Suter, Michele Migliore, and Gordon Shepherd
+:  Submitted September 2011
+: :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
+
+
+NEURON {
+    SUFFIX nax
+    USEION na READ ena WRITE ina
+    RANGE  gbar, ina, sh
+:   GLOBAL minf, hinf, mtau, htau,thinf, qinf, Rb, Rg, qg
+}
+
+PARAMETER {
+    v                   (mV)
+    celsius             (degC)
+    ena                 (mV)        : must be explicitly def. in hoc
+
+    sh      = 0         (mV)
+    gbar    = 0.010     (mho/cm2)
+
+    tha     = -30       (mV)        : v 1/2 for act
+    qa      = 7.2       (mV)        : act slope (4.5)
+    Ra      = 0.4       (/ms)       : open (v)
+    Rb      = 0.124     (/ms)       : close (v)
+
+    thi1    = -45       (mV)        : v 1/2 for inact
+    thi2    = -45       (mV)        : v 1/2 for inact
+    qd      = 1.5       (mV)        : inact tau slope
+    qg      = 1.5       (mV)
+    mmin    = 0.02
+    hmin    = 0.5
+    q10     = 2
+    Rg      = 0.01      (/ms)       : inact recov (v)
+    Rd      = 0.03      (/ms)       : inact (v)
+
+    thinf   = -50       (mV)        : inact inf slope
+    qinf    = 4         (mV)        : inact inf slope
+}
+
+
+UNITS {
+    (mA) = (milliamp)
+    (mV) = (millivolt)
+    (pS) = (picosiemens)
+    (um) = (micron)
+}
+
+ASSIGNED {
+    ina         (mA/cm2)
+    g           (mho/cm2)
+    minf
+    hinf
+    mtau        (ms)
+    htau        (ms)
+}
+
+STATE { m h}
+
+BREAKPOINT {
+    SOLVE states METHOD cnexp
+    g = gbar*m*m*m*h
+    ina = g * (v - ena)
+}
+
+INITIAL {
+    trates(v,sh)
+    m = minf
+    h = hinf
+}
+
+DERIVATIVE states {
+    trates(v,sh)
+    m' = (minf-m)/mtau
+    h' = (hinf-h)/htau
+}
+
+PROCEDURE trates(vm,sh2) {
+    LOCAL  a, b, qt
+    qt = q10^((celsius-24)/10)
+
+    a = trap0(vm,tha+sh2,Ra,qa)
+    b = trap0(-vm,-tha-sh2,Rb,qa)
+    mtau = 1/(a+b)/qt
+    if (mtau<mmin) {
+        mtau = mmin
+    }
+    minf = a/(a+b)
+
+    a = trap0(vm,thi1+sh2,Rd,qd)
+    b = trap0(-vm,-thi2-sh2,Rg,qg)
+    htau =  1/(a+b)/qt
+    if (htau<hmin) {
+        htau = hmin
+    }
+    hinf = 1/(1+exp((vm-thinf-sh2)/qinf))
+}
+
+FUNCTION trap0(v,th,a,q) {
+    if (fabs(v-th) > 1e-6) {
+        trap0 = a * (v - th) / (1 - exp(-(v - th)/q))
+    } else {
+        trap0 = a * q
+    }
+}
diff --git a/netpyne/tutorials/mod/naz.mod b/netpyne/tutorials/mod/naz.mod
new file mode 100644
index 000000000..1bad29078
--- /dev/null
+++ b/netpyne/tutorials/mod/naz.mod
@@ -0,0 +1,134 @@
+: $Id: naz.mod,v 1.8 2004/07/27 18:41:01 billl Exp $
+
+COMMENT
+26 Ago 2002 Modification of original channel to allow variable time step and to
+  correct an initialization error.
+Done by Michael Hines(michael.hines@yale.e) and Ruggero
+  Scorcioni(rscorcio@gmu.edu) at EU Advance Course in Computational
+  Neuroscience. Obidos, Portugal
+
+na.mod
+
+Sodium channel, Hodgkin-Huxley style kinetics.  
+
+Kinetics were fit to data from Huguenard et al. (1988) and Hamill et
+al. (1991)
+
+qi is not well constrained by the data, since there are no points
+between -80 and -55.  So this was fixed at 5 while the thi1,thi2,Rg,Rd
+were optimized using a simplex least square proc
+
+voltage dependencies are shifted approximately from the best
+fit to give higher threshold
+
+Author: Zach Mainen, Salk Institute, 1994, zach@salk.edu
+
+ENDCOMMENT
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+NEURON {
+  SUFFIX naz
+  USEION na READ ena WRITE ina
+  RANGE m, h, gna, gmax, i
+  GLOBAL tha, thi1, thi2, qa, qi, qinf, thinf
+  GLOBAL minf, hinf, mtau, htau
+  GLOBAL Ra, Rb, Rd, Rg
+  GLOBAL q10, temp, tadj, vmin, vmax, vshift
+}
+
+PARAMETER {
+  gmax = 1000   	(pS/um2)	: 0.12 mho/cm2
+  vshift = -10	(mV)		: voltage shift (affects all)
+  
+  tha  = -35	(mV)		: v 1/2 for act		(-42)
+  qa   = 9	(mV)		: act slope		
+  Ra   = 0.182	(/ms)		: open (v)		
+  Rb   = 0.124	(/ms)		: close (v)		
+
+  thi1  = -50	(mV)		: v 1/2 for inact 	
+  thi2  = -75	(mV)		: v 1/2 for inact 	
+  qi   = 5	(mV)	        : inact tau slope
+  thinf  = -65	(mV)		: inact inf slope	
+  qinf  = 6.2	(mV)		: inact inf slope
+  Rg   = 0.0091	(/ms)		: inact (v)	
+  Rd   = 0.024	(/ms)		: inact recov (v) 
+
+  temp = 23	(degC)		: original temp 
+  q10  = 2.3			: temperature sensitivity
+
+  v 		(mV)
+  dt		(ms)
+  celsius		(degC)
+  vmin = -120	(mV)
+  vmax = 100	(mV)
+}
+
+
+UNITS {
+  (mA) = (milliamp)
+  (mV) = (millivolt)
+  (pS) = (picosiemens)
+  (um) = (micron)
+} 
+
+ASSIGNED {
+  ina 		(mA/cm2)
+  i 		(mA/cm2)
+  gna		(pS/um2)
+  ena		(mV)
+  minf 		hinf
+  mtau (ms)	htau (ms)
+  tadj
+}
+
+
+STATE { m h }
+
+INITIAL { 
+  tadj = q10^((celsius - temp)/10)
+  rates(v+vshift)
+  m = minf
+  h = hinf
+}
+
+BREAKPOINT {
+  SOLVE states METHOD cnexp
+  gna = tadj*gmax*m*m*m*h
+  i = (1e-4) * gna * (v - ena)
+  ina = i
+} 
+
+LOCAL mexp, hexp 
+
+DERIVATIVE states {   :Computes state variables m, h, and n 
+  rates(v+vshift)      :             at the current v and dt.
+  m' =  (minf-m)/mtau
+  h' =  (hinf-h)/htau
+}
+
+PROCEDURE rates(vm) {  
+  LOCAL  a, b
+
+  a = trap0(vm,tha,Ra,qa)
+  b = trap0(-vm,-tha,Rb,qa)
+
+  mtau = 1/tadj/(a+b)
+  minf = a/(a+b)
+
+  :"h" inactivation 
+
+  a = trap0(vm,thi1,Rd,qi)
+  b = trap0(vm,thi2,-Rg,-qi)
+  htau = 1/tadj/(a+b)
+  hinf = 1/(1+exp((vm-thinf)/qinf))
+}
+
+
+FUNCTION trap0(v,th,a,q) {
+  if (fabs(v-th) > 1e-6) {
+    trap0 = a * (v - th) / (1 - exp(-(v - th)/q))
+  } else {
+    trap0 = a * q
+  }
+}	
diff --git a/netpyne/tutorials/mod/netcon.inc b/netpyne/tutorials/mod/netcon.inc
new file mode 100644
index 000000000..91150e785
--- /dev/null
+++ b/netpyne/tutorials/mod/netcon.inc
@@ -0,0 +1,145 @@
+: $Id: netcon.inc,v 1.16 2010/03/28 16:19:27 billl Exp $
+
+COMMENT
+USAGE: for most receptors
+ *****************************************************************************
+    NEURON {
+      POINT_PROCESS NAME
+    }
+
+    PARAMETER {
+      Cdur	= 1.08	(ms)		: transmitter duration (rising phase)
+      Alpha	= 1	(/ms mM)	: forward (binding) rate
+      Beta	= 0.02	(/ms)		: backward (unbinding) rate
+      Erev	= -80	(mV)		: reversal potential
+    }
+    
+    INCLUDE "netcon.inc"
+ *****************************************************************************
+
+USAGE: for NMDA receptor
+ *****************************************************************************
+    NEURON{ POINT_PROCESS NMDA
+      RANGE B 
+    }
+
+    PARAMETER {
+      mg        = 1.    (mM)     : external magnesium concentration
+      Cdur	= 1.	(ms)	 : transmitter duration (rising phase)
+      Alpha	= 4.	(/ms mM) : forward (binding) rate
+      Beta	= 0.0067 (/ms)	 : backward (unbinding) rate 1/150
+      Erev	= 0.	(mV)	 : reversal potential
+    }
+
+    ASSIGNED { B }
+
+    INCLUDE "netcon.inc"
+    : EXTRA BREAKPOINT MUST BE BELOW THE INCLUDE
+    BREAKPOINT {
+      rates(v)
+      g = g * B : but don't really need to readjust conductance
+      i = i * B : i = g*(v - Erev)
+    }
+
+    PROCEDURE rates(v(mV)) {
+      TABLE B
+      DEPEND mg
+      FROM -100 TO 80 WITH 180
+      B = 1 / (1 + Exp1(0.062 (/mV) * -v) * (mg / 3.57 (mM)))
+    }
+ *****************************************************************************
+ENDCOMMENT
+
+INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
+
+NEURON {
+  RANGE g, Erev, fflag
+  RANGE sid,cid
+  NONSPECIFIC_CURRENT i
+  GLOBAL Cdur, Alpha, Beta, Rinf, Rtau, gmax
+}
+
+UNITS {
+  (nA) = (nanoamp)
+  (mV) = (millivolt)
+  (umho) = (micromho)
+  (mM) = (milli/liter)
+}
+
+PARAMETER {
+  fflag = 0
+  sid = -1 (1) : synapse id, from cell template
+  cid = -1 (1) : id of cell to which this synapse is attached
+  gmax = 1 (S)
+}
+
+ASSIGNED {
+  v		(mV)		: postsynaptic voltage
+  i 		(nA)		: current = g*(v - Erev)
+  g 		(umho)		: conductance
+  Rinf				: steady state channels open
+  Rtau		(ms)		: time constant of channel binding
+  synon
+}
+
+STATE {Ron Roff}
+
+INITIAL {
+  PROTECT Rinf = Alpha / (Alpha + Beta)
+  PROTECT Rtau = 1 / (Alpha + Beta)
+  synon = 0
+}
+
+BREAKPOINT {
+  SOLVE release METHOD cnexp
+  g = (Ron + Roff)*gmax
+  i = g*(v - Erev)
+}
+
+DERIVATIVE release {
+  Ron' = (synon*Rinf - Ron)/Rtau
+  Roff' = -Beta*Roff
+}
+
+: following supports both saturation from single input and
+: summation from multiple inputs
+: if spike occurs during CDur then new off time is t + CDur
+: ie. transmitter concatenates but does not summate
+: Note: automatic initialization of all reference args to 0 except first
+
+NET_RECEIVE (weight, on, nspike, r0, t0 (ms)) {
+  : flag is an implicit argument of NET_RECEIVE and  normally 0
+  if (t>0) { : bug fix so that init doesn't send a false event
+    if (flag == 0) { : a spike, so turn on if not already in a Cdur pulse
+      nspike = nspike + 1
+      if (!on) {
+        r0 = r0*Exp1(-Beta*(t - t0))
+        t0 = t
+        on = 1
+        synon = synon + weight
+        Ron = Ron + r0
+        Roff = Roff - r0
+      }
+      : come again in Cdur with flag = current value of nspike
+      net_send(Cdur, nspike)
+    }
+    if (flag == nspike) { : if this associated with last spike then turn off
+      r0 = weight*Rinf + (r0 - weight*Rinf)*Exp1(-(t - t0)/Rtau)
+      t0 = t
+      synon = synon - weight
+      Ron = Ron - r0
+      Roff = Roff + r0
+      on = 0
+    }
+  }
+}
+
+FUNCTION Exp1(x) {   
+  if (x < -100) {
+    Exp1  = 0
+  } else if (x > 100) {
+    Exp1 = exp(100)
+  } else{
+    Exp1 = exp(x)
+  }
+}
diff --git a/netpyne/tutorials/mod/ofc.inc b/netpyne/tutorials/mod/ofc.inc
new file mode 100644
index 000000000..821b05e6e
--- /dev/null
+++ b/netpyne/tutorials/mod/ofc.inc
@@ -0,0 +1,54 @@
+: $Id: ofc.inc,v 1.9 2009/03/27 22:50:24 billl Exp $ 
+TITLE otto's channel includes
+
+UNITS {
+    (mV) = (millivolt)
+    (mA) = (milliamp)
+}
+
+NEURON {
+    RANGE i,g,erev,gmax,VhlfMaxm,VhlfMaxh,slopem,slopeh,taum,tauh
+  :   GLOBAL minf,hinf
+   RANGE minf,hinf
+}
+
+ASSIGNED { 
+    i (mA/cm2)
+    v (mV)
+    g (mho/cm2)
+    minf
+    hinf
+}
+
+STATE {
+  m h
+}
+
+BREAKPOINT {
+    SOLVE states METHOD cnexp
+    g = m * h * gmax
+    iassign()
+}
+
+INITIAL {
+    mh(v)
+    m = minf
+    h = hinf
+    g = m * h * gmax 
+    iassign()
+}
+
+DERIVATIVE states {  
+    mh(v)      
+    m' = ( minf - m ) / taum
+    h' = ( hinf - h ) / tauh
+}
+
+UNITSOFF
+
+PROCEDURE mh(v (mV)) {
+    minf = 1 / (1 + exp((VhlfMaxm - v)/ slopem ) )
+    hinf = 1 / (1 + exp((VhlfMaxh - v)/ slopeh ) )
+}
+
+UNITSON
diff --git a/netpyne/tutorials/mod/savedist.mod b/netpyne/tutorials/mod/savedist.mod
new file mode 100644
index 000000000..d4168565c
--- /dev/null
+++ b/netpyne/tutorials/mod/savedist.mod
@@ -0,0 +1,14 @@
+TITLE Mech to store distance from origin as workaround for multisplit non-uniform densities
+: 2011-09-18 Ben Suter, initial version per email from Michael Hines
+: 2016-11-31 Ernie Forzano, changed suffix dist to savedist because import3d() and this mod file
+: were referencing dist and causing compile error. 
+: ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
+
+NEURON {
+    SUFFIX savedist
+    RANGE x
+}
+
+PARAMETER {
+    x     = 0       (micron)
+}
diff --git a/netpyne/tutorials/mod/vecevent.mod b/netpyne/tutorials/mod/vecevent.mod
new file mode 100644
index 000000000..ce917ccf9
--- /dev/null
+++ b/netpyne/tutorials/mod/vecevent.mod
@@ -0,0 +1,80 @@
+:  Vector stream of events
+
+NEURON {
+	THREADSAFE
+	ARTIFICIAL_CELL VecStim
+	POINTER ptr
+}
+
+ASSIGNED {
+	index
+	etime (ms)
+	ptr
+}
+
+
+INITIAL {
+	index = 0
+	element()
+	if (index > 0) {
+		net_send(etime - t, 1)
+	}
+}
+
+NET_RECEIVE (w) {
+	if (flag == 1) {
+		net_event(t)
+		element()
+		if (index > 0) {
+			net_send(etime - t, 1)
+		}
+	}
+}
+
+DESTRUCTOR {
+VERBATIM
+	void* vv = (void*)(_p_ptr);  
+        if (vv) {
+		hoc_obj_unref(*vector_pobj(vv));
+	}
+ENDVERBATIM
+}
+
+PROCEDURE element() {
+VERBATIM	
+  { void* vv; int i, size; double* px;
+	i = (int)index;
+	if (i >= 0) {
+		vv = (void*)(_p_ptr);
+		if (vv) {
+			size = vector_capacity(vv);
+			px = vector_vec(vv);
+			if (i < size) {
+				etime = px[i];
+				index += 1.;
+			}else{
+				index = -1.;
+			}
+		}else{
+			index = -1.;
+		}
+	}
+  }
+ENDVERBATIM
+}
+
+PROCEDURE play() {
+VERBATIM
+	void** pv;
+	void* ptmp = NULL;
+	if (ifarg(1)) {
+		ptmp = vector_arg(1);
+		hoc_obj_ref(*vector_pobj(ptmp));
+	}
+	pv = (void**)(&_p_ptr);
+	if (*pv) {
+		hoc_obj_unref(*vector_pobj(*pv));
+	}
+	*pv = ptmp;
+ENDVERBATIM
+}
diff --git a/netpyne/tutorials/mod/vecstim.mod b/netpyne/tutorials/mod/vecstim.mod
new file mode 100644
index 000000000..03fcf3e3f
--- /dev/null
+++ b/netpyne/tutorials/mod/vecstim.mod
@@ -0,0 +1,83 @@
+: $Id: vecstim.mod,v 1.3 2010/12/13 21:29:27 samn Exp $ 
+:  Vector stream of events
+
+NEURON {
+  THREADSAFE
+       ARTIFICIAL_CELL VecStim_orig
+}
+
+ASSIGNED {
+	index
+	etime (ms)
+	space
+}
+
+INITIAL {
+	index = 0
+	element()
+	if (index > 0) {
+		if (etime - t>=0) {
+			net_send(etime - t, 1)
+		} else {
+			printf("Event in the stimulus vector at time %g is omitted since has value less than t=%g!\n", etime, t)
+			net_send(0, 2)
+		}
+	}
+}
+
+NET_RECEIVE (w) {
+	if (flag == 1) { net_event(t) }
+	if (flag == 1 || flag == 2) {
+		element()
+		if (index > 0) {	
+			if (etime - t>=0) {
+				net_send(etime - t, 1)
+			} else {
+				printf("Event in the stimulus vector at time %g is omitted since has value less than t=%g!\n", etime, t)
+				net_send(0, 2)
+			}
+		}
+	}
+}
+
+VERBATIM
+extern double* vector_vec();
+extern int vector_capacity();
+extern void* vector_arg();
+ENDVERBATIM
+
+PROCEDURE element() {
+VERBATIM	
+  { void* vv; int i, size; double* px;
+	i = (int)index;
+	if (i >= 0) {
+		vv = *((void**)(&space));
+		if (vv) {
+			size = vector_capacity(vv);
+			px = vector_vec(vv);
+			if (i < size) {
+				etime = px[i];
+				index += 1.;
+			}else{
+				index = -1.;
+			}
+		}else{
+			index = -1.;
+		}
+	}
+  }
+ENDVERBATIM
+}
+
+PROCEDURE play() {
+VERBATIM
+	void** vv;
+	vv = (void**)(&space);
+	*vv = (void*)0;
+	if (ifarg(1)) {
+		*vv = vector_arg(1);
+	}
+ENDVERBATIM
+}
+        
+
diff --git a/netpyne/tutorials/netpyne-course-2021/import_cells.ipynb b/netpyne/tutorials/netpyne-course-2021/import_cells.ipynb
deleted file mode 100644
index fc5169f0d..000000000
--- a/netpyne/tutorials/netpyne-course-2021/import_cells.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"import_cells.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"w9C_oSQMlOk6"},"source":[""]},{"cell_type":"markdown","metadata":{"id":"1D0ac1IoGgK3"},"source":["## [Importing Cells in NetPyNE](http://www.netpyne.org/advanced.html#importing-externally-defined-cell-models)"]},{"cell_type":"markdown","metadata":{"id":"kuadoljGzB42"},"source":["# (1) Clone repository and compile mod files"]},{"cell_type":"markdown","metadata":{"id":"2524ntBdiIX0"},"source":["**Determine your location in the directory structure**"]},{"cell_type":"code","metadata":{"id":"1rb_fiCxgHc7","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621994788576,"user_tz":240,"elapsed":9,"user":{"displayName":"Rammohan Shukla","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgfcDr2KHdiGwdZp-DCPyR3RvG0VfxRFktQ-0JlgQ=s64","userId":"13669639583442617912"}},"outputId":"21995b99-ee56-46c8-8801-638cb578d26f"},"source":["!pwd"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/content\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"EX3ttB4yiOEm"},"source":["**Move to (or stay in) the '/content' directory**"]},{"cell_type":"code","metadata":{"id":"9HvQwqOSgJCL","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621994790521,"user_tz":240,"elapsed":322,"user":{"displayName":"Rammohan Shukla","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgfcDr2KHdiGwdZp-DCPyR3RvG0VfxRFktQ-0JlgQ=s64","userId":"13669639583442617912"}},"outputId":"b836b622-70b2-4e76-afb0-313cec0c00ae"},"source":["%cd /content/"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/content\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"OTLrxa59idMP"},"source":["**Ensure you are in the correct directory** --> *Expected output: \"/content\"*"]},{"cell_type":"code","metadata":{"id":"US3QZfpGhJzw","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621994794327,"user_tz":240,"elapsed":231,"user":{"displayName":"Rammohan Shukla","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgfcDr2KHdiGwdZp-DCPyR3RvG0VfxRFktQ-0JlgQ=s64","userId":"13669639583442617912"}},"outputId":"218e78f4-698d-4b93-ac51-46cfe02da279"},"source":["!pwd"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/content\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"SaN1cXqxioFg"},"source":["**Install NEURON and NetPyNE, and import matplotlib**"]},{"cell_type":"code","metadata":{"id":"e7F4tIIhFpfJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1623830338498,"user_tz":-60,"elapsed":8749,"user":{"displayName":"BrandonSLockey","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg0jSuuZkatJnLda10TXNWBo11A7HwD0Sjx-YBeIA=s64","userId":"16501983811553306304"}},"outputId":"f169ff94-9441-4fd7-ec9e-5bfec013427b"},"source":["!pip install neuron\n","!pip install netpyne \n","import matplotlib\n","import os\n","import json"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Collecting neuron\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/14/f4/ea50608c7633c286859d6cce0aad621da22a8da7ff9787efc8bb71fe0597/NEURON-8.0.0-cp37-cp37m-manylinux1_x86_64.whl (12.6MB)\n","\u001b[K     |████████████████████████████████| 12.6MB 12.5MB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.9.3 in /usr/local/lib/python3.7/dist-packages (from neuron) (1.19.5)\n","Installing collected packages: neuron\n","Successfully installed neuron-8.0.0\n","Collecting netpyne\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9e/24/0f9d685a3fbcbca0d86d9ca6521465c43725d9e23760c91524fe77191f12/netpyne-1.0.0.2-py2.py3-none-any.whl (312kB)\n","\u001b[K     |████████████████████████████████| 317kB 24.9MB/s \n","\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from netpyne) (1.4.1)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from netpyne) (1.19.5)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from netpyne) (3.2.2)\n","Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from netpyne) (1.1.5)\n","Collecting matplotlib-scalebar\n","  Downloading https://files.pythonhosted.org/packages/51/a4/cd254234c35f3591361988e89ab132ee14789f2ebe1ede621d63f5241f00/matplotlib_scalebar-0.7.2-py2.py3-none-any.whl\n","Requirement already satisfied: bokeh in /usr/local/lib/python3.7/dist-packages (from netpyne) (2.3.2)\n","Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from netpyne) (0.16.0)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->netpyne) (0.10.0)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->netpyne) (2.8.1)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->netpyne) (2.4.7)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->netpyne) (1.3.1)\n","Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->netpyne) (2018.9)\n","Requirement already satisfied: packaging>=16.8 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (20.9)\n","Requirement already satisfied: typing-extensions>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (3.7.4.3)\n","Requirement already satisfied: pillow>=7.1.0 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (7.1.2)\n","Requirement already satisfied: PyYAML>=3.10 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (3.13)\n","Requirement already satisfied: Jinja2>=2.9 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (2.11.3)\n","Requirement already satisfied: tornado>=5.1 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (5.1.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from cycler>=0.10->matplotlib->netpyne) (1.15.0)\n","Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from Jinja2>=2.9->bokeh->netpyne) (2.0.1)\n","Installing collected packages: matplotlib-scalebar, netpyne\n","Successfully installed matplotlib-scalebar-0.7.2 netpyne-1.0.0.2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"svtYiGlOG9Tf","executionInfo":{"status":"ok","timestamp":1623830329757,"user_tz":-60,"elapsed":187,"user":{"displayName":"BrandonSLockey","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg0jSuuZkatJnLda10TXNWBo11A7HwD0Sjx-YBeIA=s64","userId":"16501983811553306304"}}},"source":["%matplotlib inline"],"execution_count":1,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"I4ZvwJm6fGey"},"source":["This next line will **detect if the directory already exists** (i.e. you are re-running this code), and will **delete it** to prevent future errors. "]},{"cell_type":"code","metadata":{"id":"1JsTSuHae_Oi"},"source":["if os.path.isdir('/content/cells_netpyne2021'):\n","  !rm -r /content/cells_netpyne2021"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"LDUdXYHmbkLR"},"source":["**Clone repository with the necessary cell and mod files**"]},{"cell_type":"code","metadata":{"id":"iZyTAo_5OYaA","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621994821653,"user_tz":240,"elapsed":1583,"user":{"displayName":"Rammohan Shukla","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgfcDr2KHdiGwdZp-DCPyR3RvG0VfxRFktQ-0JlgQ=s64","userId":"13669639583442617912"}},"outputId":"303c57d6-7dd9-42b1-c5f0-bab4d9b06a73"},"source":["!git clone https://github.com/ericaygriffith/cells_netpyne2021.git"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Cloning into 'cells_netpyne2021'...\n","remote: Enumerating objects: 33, done.\u001b[K\n","remote: Counting objects: 100% (33/33), done.\u001b[K\n","remote: Compressing objects: 100% (32/32), done.\u001b[K\n","remote: Total 33 (delta 9), reused 0 (delta 0), pack-reused 0\u001b[K\n","Unpacking objects: 100% (33/33), done.\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"lyOMLquOixOh"},"source":["**Move into the repository with all the necessary files**"]},{"cell_type":"code","metadata":{"id":"ZzbUv4yxQCi3","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621994831440,"user_tz":240,"elapsed":258,"user":{"displayName":"Rammohan Shukla","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgfcDr2KHdiGwdZp-DCPyR3RvG0VfxRFktQ-0JlgQ=s64","userId":"13669639583442617912"}},"outputId":"bbad0ef9-3c97-400c-dd9c-917f214ce7e9"},"source":["cd cells_netpyne2021/"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/content/cells_netpyne2021\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"_6zfYKQSbzeF"},"source":["**Ensure you are in the repository with the 'pwd' command** --> *Expected output*: '/content/cells_netpyne2021'"]},{"cell_type":"code","metadata":{"id":"TumQgyz_QIDa","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621994865607,"user_tz":240,"elapsed":235,"user":{"displayName":"Rammohan Shukla","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgfcDr2KHdiGwdZp-DCPyR3RvG0VfxRFktQ-0JlgQ=s64","userId":"13669639583442617912"}},"outputId":"baf81b2d-3723-4191-92ff-6be6f3f3e83c"},"source":["!pwd"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/content/cells_netpyne2021\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Fpcn-UpGb00x"},"source":["**Compile the mod files** --> *Expected output:* creation of an 'x86_64' directory "]},{"cell_type":"code","metadata":{"id":"Awib_5OmOzjS","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621994879029,"user_tz":240,"elapsed":2229,"user":{"displayName":"Rammohan Shukla","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgfcDr2KHdiGwdZp-DCPyR3RvG0VfxRFktQ-0JlgQ=s64","userId":"13669639583442617912"}},"outputId":"3b151e1c-93ff-41b2-b2fa-84463438be6e"},"source":["!nrnivmodl"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/content/cells_netpyne2021\n","Mod files: \"./cadad.mod\" \"./HH2.mod\" \"./htc.mod\" \"./IT2.mod\" \"./IT.mod\" \"./kl.mod\" \"./tia.mod\"\n","\n","Creating x86_64 directory for .o files.\n","\n","COBJS=''\n"," -> \u001b[32mCompiling\u001b[0m mod_func.c\n"," -> \u001b[32mNMODL\u001b[0m ../cadad.mod\n","x86_64-linux-gnu-gcc -O2   -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c mod_func.c -o mod_func.o\n","(cd \"..\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl cadad.mod -o \"/content/cells_netpyne2021/x86_64\")\n"," -> \u001b[32mNMODL\u001b[0m ../HH2.mod\n","(cd \"..\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl HH2.mod -o \"/content/cells_netpyne2021/x86_64\")\n"," -> \u001b[32mNMODL\u001b[0m ../htc.mod\n","(cd \"..\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl htc.mod -o \"/content/cells_netpyne2021/x86_64\")\n","Translating cadad.mod into /content/cells_netpyne2021/x86_64/cadad.c\n","Translating HH2.mod into /content/cells_netpyne2021/x86_64/HH2.c\n","Thread Safe\n","Thread Safe\n","Translating htc.mod into /content/cells_netpyne2021/x86_64/htc.c\n","NEURON's CVode method ignores conservation\n","Warning: celsius undefined. (declared within VERBATIM?)\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m ../IT2.mod\n","(cd \"..\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl IT2.mod -o \"/content/cells_netpyne2021/x86_64\")\n"," -> \u001b[32mNMODL\u001b[0m ../IT.mod\n","(cd \"..\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl IT.mod -o \"/content/cells_netpyne2021/x86_64\")\n","Translating IT2.mod into /content/cells_netpyne2021/x86_64/IT2.c\n"," -> \u001b[32mNMODL\u001b[0m ../kl.mod\n","(cd \"..\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl kl.mod -o \"/content/cells_netpyne2021/x86_64\")\n","Warning: celsius undefined. (declared within VERBATIM?)\n","Thread Safe\n","Translating IT.mod into /content/cells_netpyne2021/x86_64/IT.c\n"," -> \u001b[32mNMODL\u001b[0m ../tia.mod\n","(cd \"..\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl tia.mod -o \"/content/cells_netpyne2021/x86_64\")\n","Warning: Default 2 of PARAMETER cao will be ignored and set by NEURON.\n","Warning: Default 0.00024 of PARAMETER cai will be ignored and set by NEURON.\n","Thread Safe\n","Translating kl.mod into /content/cells_netpyne2021/x86_64/kl.c\n","Thread Safe\n"," -> \u001b[32mCompiling\u001b[0m cadad.c\n","x86_64-linux-gnu-gcc -O2   -I\"..\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c cadad.c -o cadad.o\n","Translating tia.mod into /content/cells_netpyne2021/x86_64/tia.c\n","Warning: celsius undefined. (declared within VERBATIM?)\n","Thread Safe\n"," -> \u001b[32mCompiling\u001b[0m HH2.c\n","x86_64-linux-gnu-gcc -O2   -I\"..\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c HH2.c -o HH2.o\n"," -> \u001b[32mCompiling\u001b[0m htc.c\n","x86_64-linux-gnu-gcc -O2   -I\"..\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c htc.c -o htc.o\n"," -> \u001b[32mCompiling\u001b[0m IT2.c\n","x86_64-linux-gnu-gcc -O2   -I\"..\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c IT2.c -o IT2.o\n"," -> \u001b[32mCompiling\u001b[0m IT.c\n","x86_64-linux-gnu-gcc -O2   -I\"..\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c IT.c -o IT.o\n"," -> \u001b[32mCompiling\u001b[0m kl.c\n","x86_64-linux-gnu-gcc -O2   -I\"..\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c kl.c -o kl.o\n"," -> \u001b[32mCompiling\u001b[0m tia.c\n","x86_64-linux-gnu-gcc -O2   -I\"..\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c tia.c -o tia.o\n"," => \u001b[32mLINKING\u001b[0m shared library ./libnrnmech.so\n","x86_64-linux-gnu-g++ -O2 -DVERSION_INFO='8.0.0' -std=c++11 -shared -fPIC  -I /usr/local/lib/python3.7/dist-packages/neuron/.data/include -o ./libnrnmech.so -Wl,-soname,libnrnmech.so \\\n","  ./mod_func.o ./cadad.o ./HH2.o ./htc.o ./IT2.o ./IT.o ./kl.o ./tia.o  -L/usr/local/lib/python3.7/dist-packages/neuron/.data/lib -lnrniv -Wl,-rpath,/usr/local/lib/python3.7/dist-packages/neuron/.data/lib   \n","rm -f ./.libs/libnrnmech.so ; mkdir -p ./.libs ; cp ./libnrnmech.so ./.libs/libnrnmech.so\n","Successfully created x86_64/special\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"nTu6o-Zny2oS"},"source":["# (2) Importing cells from different file formats"]},{"cell_type":"markdown","metadata":{"id":"s-1PVaqD0LOS"},"source":["**Set up netParams object**"]},{"cell_type":"code","metadata":{"id":"nQlqPAPAHHyW"},"source":["from netpyne import specs, sim   \n","\n","# Network parameters\n","netParams = specs.NetParams()  # object of class NetParams to store the network parameters"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Z5NOiaZW0QJn"},"source":["**2a. Import cell from *.json* format**"]},{"cell_type":"code","metadata":{"id":"-_zSnrr3JgE2"},"source":["netParams.loadCellParamsRule(label='TC_reduced', fileName = 'TC_reduced_cellParams.json')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"I7qosoz7ofIS","executionInfo":{"status":"ok","timestamp":1621994919156,"user_tz":240,"elapsed":224,"user":{"displayName":"Rammohan Shukla","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgfcDr2KHdiGwdZp-DCPyR3RvG0VfxRFktQ-0JlgQ=s64","userId":"13669639583442617912"}},"outputId":"481a8c47-2a78-439a-f768-162c193f1676"},"source":["netParams.cellParams['TC_reduced']"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'conds': {'cellModel': 'HH_reduced', 'cellType': 'TC'},\n"," 'globals': {'erev_kl': -95.0, 'q10m_ittc': 3.55, 'v_init': -70.0},\n"," 'secLists': {},\n"," 'secs': {'soma': {'geom': {'L': 96.0,\n","    'Ra': 100.0,\n","    'cm': 1.0,\n","    'diam': 96.0,\n","    'nseg': 1,\n","    'pt3d': [[0, 0, 0, 96.0], [0, 96.0, 0, 96.0]]},\n","   'ions': {'ca': {'e': 132.4579341637009, 'i': 5e-05, 'o': 2.0},\n","    'k': {'e': -95.0, 'i': 54.4, 'o': 2.5},\n","    'na': {'e': 50.0, 'i': 10.0, 'o': 140.0}},\n","   'mechs': {'cadad': {'cainf': 0.00024,\n","     'depth': 1.0,\n","     'kd': 0.0,\n","     'kt': 0.0,\n","     'taur': 5.0},\n","    'hh2ad': {'gkbar': 0.012, 'gnabar': 0.02, 'vtraub': -63.0},\n","    'htc': {'Pc': 0.01,\n","     'cac': 0.002,\n","     'exptemp': 36.0,\n","     'ginc': 2.0,\n","     'gmax': 1e-06,\n","     'k2': 0.0004,\n","     'k4': 0.001,\n","     'nca': 4.0,\n","     'nexp': 1.0,\n","     'shift': 0.0,\n","     'taum': 20.0},\n","    'ia': {'exptemp': 23.5, 'gmax': 0.007, 'q10': 3.0},\n","    'ittc': {'gmax': 0.002, 'shift': 3},\n","    'kl': {'gmax': 2.3e-05},\n","    'pas': {'e': -50, 'g': 1.8e-05}},\n","   'topol': {},\n","   'vinit': -70.0,\n","   'weightNorm': [0.002091023573256415]}}}"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"markdown","metadata":{"id":"mVos4-7Hq06y"},"source":["**2b. Import a detailed morphology from a *.swc* file**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4GuMb33-rP1f","executionInfo":{"status":"ok","timestamp":1621994946999,"user_tz":240,"elapsed":517,"user":{"displayName":"Rammohan Shukla","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgfcDr2KHdiGwdZp-DCPyR3RvG0VfxRFktQ-0JlgQ=s64","userId":"13669639583442617912"}},"outputId":"caea9114-4644-4427-cf82-3ea45b193b48"},"source":["netParams.importCellParams(\n","        label='PYR_HH3D_swc',\n","        conds={'cellType': 'PYR', 'cellModel': 'HH3D_swc'},\n","        fileName='BS0284.swc',\n","        cellName='swc_cell')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{conds: {cellType: 'PYR', cellModel: 'HH3D_swc'}, secs: {soma_0: {geom: {L: 38.71370003363001, nseg: 1, diam: 11.766242500611416, Ra: 35.4, cm: 1.0, pt3d: [(-8.3100004196167, -8.279999732971191, -19.309999465942383, 2.2491800785064697), (-6.970000386238098, -6.159999847412109, -16.769999504089355, 7.460279941558838), (-6.350000381469727, -4.999999761581421, -13.9399995803833, 9.697859764099121), (-5.600000381469727, -4.159999847412109, -11.169999122619629, 11.111539840698242), (-4.630000352859497, -2.8799996376037598, -8.459999084472656, 11.920280456542969), (-4.020000457763672, -2.179999828338623, -6.669999122619629, 12.933099746704102), (-3.090000629425049, -1.7699995040893555, -4.959999084472656, 13.913820266723633), (-1.330000400543213, -0.23999977111816406, -3.5799999237060547, 14.734780311584473), (-0.7300004959106445, -0.7499995231628418, -1.779998779296875, 14.915260314941406), (0.0, 0.0, 0.0, 15.22443962097168), (0.22999954223632812, -0.679999828338623, 1.9500007629394531, 15.816900253295898), (0.8999996185302734, -1.7499995231628418, 3.730001449584961, 15.573860168457031), (1.2899999618530273, -0.679999828338623, 5.620000839233398, 14.565959930419922), (2.4499998092651367, -0.13000011444091797, 7.239999771118164, 13.295160293579102), (3.0099992752075195, -0.2199993133544922, 9.060001373291016, 11.514579772949219), (3.9099998474121094, 0.21000003814697266, 9.760000228881836, 10.221460342407227), (4.0099992752075195, -0.1099996566772461, 10.739999771118164, 8.726200103759766), (5.069999694824219, 0.7200002670288086, 11.370000839233398, 7.113460063934326), (5.5099992752075195, 0.7300004959106445, 12.220001220703125, 5.794280052185059)]}, topol: {}, mechs: {}}, dend_0: {geom: {L: 31.64276390938154, nseg: 1, diam: 0.9099330269557575, Ra: 35.4, cm: 1.0, pt3d: [(-8.3100004196167, -8.279999732971191, -19.309999465942383, 0.9100000262260437), (-8.260000418871641, -1.299999713897705, -15.279999256134033, 0.9100000262260437), (-9.710000395774841, -0.07999992370605469, -14.739999294281006, 0.9100000262260437), (-13.110000610351562, 0.25, -17.53999948501587, 0.9100000262260437), (-14.820000648498535, 0.619999885559082, -17.929999470710754, 0.8500000238418579), (-16.170000553131104, 1.380000114440918, -18.43999946117401, 0.8500000238418579), (-17.510000228881836, 1.5600004196166992, -19.969999492168427, 1.0399999618530273), (-20.289999961853027, 2.280000686645508, -23.029999494552612, 0.9100000262260437), (-24.49000072479248, 3.520000457763672, -23.499999523162842, 0.8500000238418579), (-26.500000953674316, 4.199999809265137, -25.799999237060547, 0.9300000071525574)]}, topol: {parentSec: 'soma_0', parentX: 0.0, childX: 0.0}, mechs: {}}, dend_1: {geom: {L: 18.146493099962022, nseg: 1, diam: 0.8146932115385057, Ra: 35.4, cm: 1.0, pt3d: [(-26.500000953674316, 4.199999809265137, -25.799999237060547, 0.9300000071525574), (-27.640000343322754, 5.280000686645508, -28.449999809265137, 0.8299999833106995), (-29.42000102996826, 6.600000381469727, -32.86999988555908, 0.8299999833106995), (-30.9399995803833, 7.7699995040893555, -35.3799991607666, 0.8299999833106995), (-32.6899995803833, 8.760001182556152, -37.779998779296875, 0.75), (-33.6800012588501, 9.630000114440918, 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{}}"]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"92-GiR6tpRR8","executionInfo":{"status":"ok","timestamp":1621995005974,"user_tz":240,"elapsed":337,"user":{"displayName":"Rammohan Shukla","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgfcDr2KHdiGwdZp-DCPyR3RvG0VfxRFktQ-0JlgQ=s64","userId":"13669639583442617912"}},"outputId":"adaa23bd-bd5b-44c0-e66f-bc13ad93aa5d"},"source":["netParams.cellParams.keys()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["odict_keys(['TC_reduced', 'PYR_HH3D_swc'])"]},"metadata":{"tags":[]},"execution_count":18}]},{"cell_type":"markdown","metadata":{"id":"j3rnIKCg0hmE"},"source":["**2c. Import a cell from a *.hoc* (NEURON) file**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oXF2t0d22qlK","executionInfo":{"status":"ok","timestamp":1621995008637,"user_tz":240,"elapsed":448,"user":{"displayName":"Rammohan Shukla","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgfcDr2KHdiGwdZp-DCPyR3RvG0VfxRFktQ-0JlgQ=s64","userId":"13669639583442617912"}},"outputId":"2d275c0d-726d-439d-afc4-e16be969c6e4"},"source":["netParams.importCellParams(\n","        label='PYR_HH3D_hoc',\n","        conds={'cellType': 'PYR', 'cellModel': 'HH3D_hoc'},\n","        fileName='geom.hoc',\n","        cellName='E21',\n","        importSynMechs=False)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{conds: {cellType: 'PYR', cellModel: 'HH3D_hoc'}, secs: {soma: {geom: {L: 15.0996688705415, nseg: 1, diam: 12.0, Ra: 35.4, cm: 1.0, pt3d: [(10.0, -8.0, -8.0, 12.0), (0.0, 0.0, 0.0, 12.0)]}, topol: {}, mechs: {}}, 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{}}"]},"metadata":{"tags":[]},"execution_count":19}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NlsN9xQQpdpF","executionInfo":{"status":"ok","timestamp":1621995011201,"user_tz":240,"elapsed":300,"user":{"displayName":"Rammohan Shukla","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgfcDr2KHdiGwdZp-DCPyR3RvG0VfxRFktQ-0JlgQ=s64","userId":"13669639583442617912"}},"outputId":"53c944a3-73c1-4cd9-bdc1-6d94d554b605"},"source":["netParams.cellParams.keys()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["odict_keys(['TC_reduced', 'PYR_HH3D_swc', 'PYR_HH3D_hoc'])"]},"metadata":{"tags":[]},"execution_count":20}]},{"cell_type":"markdown","metadata":{"id":"qA6VzPq40tRn"},"source":["**2d. Import a cell from a *.py* (python) file**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VzlrqTat2sbW","executionInfo":{"status":"ok","timestamp":1621995022842,"user_tz":240,"elapsed":338,"user":{"displayName":"Rammohan Shukla","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgfcDr2KHdiGwdZp-DCPyR3RvG0VfxRFktQ-0JlgQ=s64","userId":"13669639583442617912"}},"outputId":"723f93c2-045e-46f5-c2b7-302be0e92da3"},"source":["netParams.importCellParams(\n","        label='sRE_py',\n","        conds={'cellType': 'sRE', 'cellModel': 'HH'},\n","        fileName='sRE.py',\n","        cellName='sRE',\n","        importSynMechs=False)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{conds: {cellType: 'sRE', cellModel: 'HH'}, secs: {soma: {geom: {L: 64.86, nseg: 1, diam: 70.0, Ra: 100.0, cm: 1.0}, topol: {}, mechs: {cadad: {depth: 1.0, taur: 5.0, cainf: 0.00024, kt: 0.0, kd: 0.0}, hh2ad: {gnabar: 0.09, gkbar: 0.01, vtraub: -50.0}, itre: {gmax: 0.002, shift: 2.0}, kl: {gmax: 3e-06}, pas: {g: 5e-05, e: -77.0}}, ions: {ca: {e: 132.4579341637009, i: 5e-05, o: 2.0}, k: {e: -95.0, i: 54.4, o: 2.5}, na: {e: 50.0, i: 10.0, o: 140.0}}}}, secLists: {SectionList[0]: [], SectionList[1]: []}, globals: {erev_kl: -95.0, q10h_itre: 3.0}, _repr_mimebundle_: {}}"]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pNA12sS_pw3R","executionInfo":{"status":"ok","timestamp":1621995024717,"user_tz":240,"elapsed":306,"user":{"displayName":"Rammohan Shukla","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgfcDr2KHdiGwdZp-DCPyR3RvG0VfxRFktQ-0JlgQ=s64","userId":"13669639583442617912"}},"outputId":"5175061a-8d0f-4565-910d-ebf82bca226d"},"source":["netParams.cellParams.keys()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["odict_keys(['TC_reduced', 'PYR_HH3D_swc', 'PYR_HH3D_hoc', 'sRE_py'])"]},"metadata":{"tags":[]},"execution_count":22}]},{"cell_type":"markdown","metadata":{"id":"Hdg8Rr5uABit"},"source":["**EXERCISE: import the other swc file contained in the cells_netpyne2021 directory**"]},{"cell_type":"code","metadata":{"id":"hRXwfJJP_cBG","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621995033600,"user_tz":240,"elapsed":408,"user":{"displayName":"Rammohan Shukla","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgfcDr2KHdiGwdZp-DCPyR3RvG0VfxRFktQ-0JlgQ=s64","userId":"13669639583442617912"}},"outputId":"58559607-5275-4d30-fb5b-36bb1acc9074"},"source":["netParams.importCellParams(\n","    label='mouse_hipp_swc',\n","    conds={'cellType': 'hipp','cellModel': 'HH3D'},\n","    fileName='mouseGABA_hipp.swc',\n","    cellName='swc_hippCell'\n",")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{conds: {cellType: 'hipp', cellModel: 'HH3D'}, secs: {soma_0: {geom: {L: 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(70.94000244140625, 71.70999908447266, 26.549999237060547, 0.5199999809265137), (70.19999694824219, 73.05000305175781, 26.68000030517578, 0.8600000143051147), (70.18000030517578, 72.77999877929688, 27.139999389648438, 0.9399999976158142)]}, topol: {parentSec: 'dend_17', parentX: 1.0, childX: 0.0}, mechs: {}}}, secLists: {SectionList[0]: [], SectionList[1]: []}, globals: {}, _repr_mimebundle_: {}}"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"code","metadata":{"id":"J3xQXC-ssTlr"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WBf8K34Er5tQ","executionInfo":{"status":"ok","timestamp":1621949294792,"user_tz":-180,"elapsed":81,"user":{"displayName":"Michael Mazar","photoUrl":"","userId":"15788570157356013099"}},"outputId":"a991cd93-d348-4e20-a0dc-93187f0e68e2"},"source":["netParams.cellParams.keys()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["odict_keys(['TC_reduced', 'PYR_HH3D_swc', 'PYR_HH3D_hoc', 'sRE_py', 'mouse_hipp_swc'])"]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"markdown","metadata":{"id":"dE0q2_sn04nd"},"source":["# (3) Explore and manipulate cell parameters"]},{"cell_type":"markdown","metadata":{"id":"leD5yrPY3Ts8"},"source":["**Explore the cell types located in the netParams.cellParams dictionary**"]},{"cell_type":"code","metadata":{"id":"Gn2sX8OerIy5","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621949294792,"user_tz":-180,"elapsed":41,"user":{"displayName":"Michael Mazar","photoUrl":"","userId":"15788570157356013099"}},"outputId":"17252aed-da2d-434d-8fc7-f4626707c447"},"source":["netParams.cellParams.keys()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["odict_keys(['TC_reduced', 'PYR_HH3D_swc', 'PYR_HH3D_hoc', 'sRE_py', 'mouse_hipp_swc'])"]},"metadata":{"tags":[]},"execution_count":22}]},{"cell_type":"markdown","metadata":{"id":"-Fzcm2gO3Yd4"},"source":["**EXERCISE: Find the geometry (length & diameter) of the soma compartment for each of the above cells**"]},{"cell_type":"code","metadata":{"id":"jQ0yPJJVKDut","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621949294793,"user_tz":-180,"elapsed":39,"user":{"displayName":"Michael Mazar","photoUrl":"","userId":"15788570157356013099"}},"outputId":"5d432812-e812-40e1-8637-3b2828f3d52e"},"source":["netParams.cellParams['TC_reduced']['secs']['soma']['geom']['L']"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["96.0"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"code","metadata":{"id":"znArEaThuY7v"},"source":["geom_TC = netParams.cellParams['TC_reduced']['secs']['soma']['geom']"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fKULr1CpuZcE","executionInfo":{"status":"ok","timestamp":1621949294793,"user_tz":-180,"elapsed":36,"user":{"displayName":"Michael Mazar","photoUrl":"","userId":"15788570157356013099"}},"outputId":"3731c720-6c23-4d76-b3b0-1a5e648e9d5f"},"source":["geom_TC['L']"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["96.0"]},"metadata":{"tags":[]},"execution_count":25}]},{"cell_type":"code","metadata":{"id":"ljq8xt0yuZ26"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"mVnuCQzREW_5"},"source":["**EXERCISE: List all of the channel mechanisms in the soma compartment of the thalamocortical cell model (TC_reduced)**"]},{"cell_type":"code","metadata":{"id":"9-iwTxF2Eblh","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621949294794,"user_tz":-180,"elapsed":33,"user":{"displayName":"Michael Mazar","photoUrl":"","userId":"15788570157356013099"}},"outputId":"ddf2d8d0-6e08-45de-d20d-5fb2b3acea5e"},"source":["netParams.cellParams['TC_reduced']['secs']['soma']['mechs'].keys()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["dict_keys(['cadad', 'hh2ad', 'htc', 'ia', 'ittc', 'kl', 'pas'])"]},"metadata":{"tags":[]},"execution_count":26}]},{"cell_type":"markdown","metadata":{"id":"7bJMkym3FAdS"},"source":["**Now we want to explore (and change) the values of a channel parameter in a given cell model**"]},{"cell_type":"code","metadata":{"id":"8NaWS8V4FlVt","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621949294795,"user_tz":-180,"elapsed":31,"user":{"displayName":"Michael Mazar","photoUrl":"","userId":"15788570157356013099"}},"outputId":"eb3df25a-8d2e-4104-a0e6-8c5ca13164b2"},"source":["netParams.cellParams['TC_reduced']['secs']['soma']['mechs'].keys()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["dict_keys(['cadad', 'hh2ad', 'htc', 'ia', 'ittc', 'kl', 'pas'])"]},"metadata":{"tags":[]},"execution_count":27}]},{"cell_type":"code","metadata":{"id":"WpUwp6wTFqoy","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621949294796,"user_tz":-180,"elapsed":29,"user":{"displayName":"Michael Mazar","photoUrl":"","userId":"15788570157356013099"}},"outputId":"66523fc2-7127-4d50-98dd-4827a1ffc41f"},"source":["netParams.cellParams['TC_reduced']['secs']['soma']['mechs']['pas'].keys()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["dict_keys(['e', 'g'])"]},"metadata":{"tags":[]},"execution_count":28}]},{"cell_type":"code","metadata":{"id":"HTQFhFEHFu5J"},"source":["netParams.cellParams['TC_reduced']['secs']['soma']['mechs']['pas']['g'] = 5.0e05"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qt8YfbwRwQty","executionInfo":{"status":"ok","timestamp":1621949294797,"user_tz":-180,"elapsed":26,"user":{"displayName":"Michael Mazar","photoUrl":"","userId":"15788570157356013099"}},"outputId":"b49bebaf-6c4c-4aa7-9e41-ea6a7e4e7b1b"},"source":["netParams.cellParams['TC_reduced']['secs']['soma']['mechs']['pas']['g']"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["500000.0"]},"metadata":{"tags":[]},"execution_count":30}]},{"cell_type":"markdown","metadata":{"id":"RBY_wzAXF8SD"},"source":["**EXERCISE: Change the conductance of the leak channel in the soma compartment of the reticular cell model (sRE.py)**\n","\n"]},{"cell_type":"code","metadata":{"id":"PaAPJSvOFzdb"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"uOVy3USkFex5"},"source":["**EXERCISE: Insert a passive leak channel ('pas') into the soma compartment of the mouseGABA_hipp.swc cell model**\n"]},{"cell_type":"code","metadata":{"id":"WGPmWuzsFdQi"},"source":["netParams.cellParams['mouse_hipp_swc']['secs']['soma_0']['mechs']['pas'] = {'g': 0.0000357, 'e': -70}"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"SuLplB9nFaLQ"},"source":["**EXERCISE: Change the capacitance of all compartments in the model defined by BS0284.swc (PYR_HH3D_swc)**"]},{"cell_type":"code","metadata":{"id":"7-nfCynAHBnO"},"source":["for sec in netParams.cellParams['PYR_HH3D_swc']['secs'].keys():\n","  netParams.cellParams['PYR_HH3D_swc']['secs'][sec]['geom']['cm'] = 1\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"zeLQKeKrIuCu"},"source":["**Now let's see how these changes affect the cell behavior by plotting cell's response to current input before and after param changes!**"]},{"cell_type":"markdown","metadata":{"id":"PvKwCh72JG_Q"},"source":["**EXERCISE: First create a population of thalamocortical cells**"]},{"cell_type":"code","metadata":{"id":"W3XadPAUJKsl"},"source":["netParams.popParams['TC_pop'] = {'cellType': 'TC', 'numCells': 1, 'cellModel': 'HH_reduced'}"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"HvaQTM27I_pN"},"source":["**EXERCISE: Add hyperpolarizing current clamp stimulation of -0.1 nA to thalamocortical cell pop** "]},{"cell_type":"code","metadata":{"id":"qVqIsBwCIyt7"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"dqvN7P87Jn5H"},"source":["netParams.stimTargetParams['Input->TC_pop'] = {'source': 'Input', 'sec':'soma', 'loc': 0.5, 'conds': {'pop':'TC_pop'}}"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"VmkX0HEWJuwL"},"source":["**Add cfg params**"]},{"cell_type":"code","metadata":{"id":"CcvbW5gjIteV"},"source":["## cfg  \n","cfg = specs.SimConfig()\t\t\t\t\t            # object of class SimConfig to store simulation configuration\n","cfg.duration = 2*1e3 \t\t\t\t\t\t            # Duration of the simulation, in ms\n","cfg.dt = 0.01\t\t\t\t\t\t\t\t                # Internal integration timestep to use\n","cfg.verbose = 1\t\t\t\t\t\t\t                # Show detailed messages \n","cfg.recordTraces = {'V_soma':{'sec':'soma','loc':0.5,'var':'v'}}  # Dict with traces to record\n","cfg.recordStep = 0.01 \t\t\t\n","cfg.filename = 'model_output'  \t\t\t# Set file output name\n","cfg.saveJson = False\n","cfg.analysis['plotTraces'] = {'include': [0], 'saveFig': True} # Plot recorded traces for this list of cells\n","cfg.hParams['celsius'] = 36\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"tTHJbn74J7lS"},"source":["**Create network and run simulation**"]},{"cell_type":"code","metadata":{"id":"FLFtnFMTJ9d2","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621949295064,"user_tz":-180,"elapsed":287,"user":{"displayName":"Michael Mazar","photoUrl":"","userId":"15788570157356013099"}},"outputId":"c70d23e5-f0f0-4238-fd49-a837077fdad5"},"source":["sim.createSimulateAnalyze(netParams = netParams, simConfig = cfg)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["\n","Start time:  2021-05-25 13:28:13.326063\n","\n","Creating network of 1 cell populations on 1 hosts...\n","Distributed population of 1 cells on 1 hosts: {0: [0]}, next: 0\n","Cell 0/0 (gid=0) of pop TC_pop, on node 0, \n","Instantiated 1 cells of population TC_pop\n","  Number of cells on node 0: 1 \n","  Done; cell creation time = 0.01 s.\n","Making connections...\n","  Number of connections on node 0: 0 \n","  Done; cell connection time = 0.00 s.\n","Adding stims...\n","  Added Input IClamp to cell gid=0, sec=soma, loc=0.5, del=500, dur=800, amp=-0.1\n","  Number of stims on node 0: 1 \n","  Done; cell stims creation time = 0.00 s.\n","  Recording  V_soma from cell  0  with parameters:  {'sec': 'soma', 'loc': 0.5, 'var': 'v'}\n","Vector[2]\n","   Recording: spkt:\n","   Recording: spkid:\n","   Recording: V_soma:\n","      cell_0\n","   Recording: t:\n","Recording 1 traces of 1 types on node 0\n","\n","Warning: global variable v_init=-65.0 differs from that set for each section in cellParams rule TC_reduced: -70.0\n","\n","Setting h global variables ...\n","  h.celsius = 36\n","  h.v_init = -65.0\n","  h.clamp_resist = 0.001\n","  h.erev_kl = -95.0\n","  h.q10m_ittc = 3.55\n","  h.q10h_itre = 3.0\n","  h.tstop = 2000.0\n","Minimum delay (time-step for queue exchange) is 10.00\n","\n","Running simulation for 2000.0 ms...\n","  Done; run time = 0.44 s; real-time ratio: 4.51.\n","\n","Gathering data...\n","  Done; gather time = 0.02 s.\n","\n","Analyzing...\n","  Cells: 1\n","  Connections: 0 (0.00 per cell)\n","  Spikes: 0 (0.00 Hz)\n","  Simulated time: 2.0 s; 1 workers\n","  Run time: 0.44 s\n","  Done; saving time = 0.02 s.\n","Plotting recorded cell traces ... cell\n","  Done; plotting time = 0.29 s\n","\n","Total time = 0.81 s\n","\n","End time:  2021-05-25 13:28:14.135416\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"l1hSjSpXRM3b"},"source":["**EXERCISE: We see a rebound burst! T-type calcium channels are normally considered responsible for this behavior. What happens if we set the conductance of this channel to 0?**"]},{"cell_type":"code","metadata":{"id":"QNBtYOL6RVx3"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"X4d0MxbtRqZw"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"TnePQWvmRxrk"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"P2dl3wsmRyhn"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"G4G4YQzsR8Ac"},"source":["**cfg params**"]},{"cell_type":"code","metadata":{"id":"b5si2h8_R2Q_"},"source":["## cfg  \n","cfg = specs.SimConfig()\t\t\t\t\t            # object of class SimConfig to store simulation configuration\n","cfg.duration = 2*1e3 \t\t\t\t\t\t            # Duration of the simulation, in ms\n","cfg.dt = 0.01\t\t\t\t\t\t\t\t                # Internal integration timestep to use\n","cfg.verbose = 1\t\t\t\t\t\t\t                # Show detailed messages \n","cfg.recordTraces = {'V_soma':{'sec':'soma','loc':0.5,'var':'v'}}  # Dict with traces to record\n","cfg.recordStep = 0.01 \t\t\t\n","cfg.filename = 'model_output'  \t\t\t# Set file output name\n","cfg.saveJson = False\n","cfg.analysis['plotTraces'] = {'include': [0], 'saveFig': True} # Plot recorded traces for this list of cells\n","cfg.hParams['celsius'] = 36"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ke3HznlYR9am"},"source":["**Run the sim**"]},{"cell_type":"code","metadata":{"id":"lDGpMPJZR3o3","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621949295067,"user_tz":-180,"elapsed":13,"user":{"displayName":"Michael Mazar","photoUrl":"","userId":"15788570157356013099"}},"outputId":"a95a5be5-1ab6-4cfc-c566-705a0d2f805b"},"source":["sim.createSimulateAnalyze(netParams = netParams, simConfig = cfg)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["\n","Start time:  2021-05-25 13:28:14.213259\n","\n","Creating network of 1 cell populations on 1 hosts...\n","Distributed population of 1 cells on 1 hosts: {0: [0]}, next: 0\n","Cell 0/0 (gid=0) of pop TC_pop, on node 0, \n","Instantiated 1 cells of population TC_pop\n","  Number of cells on node 0: 1 \n","  Done; cell creation time = 0.00 s.\n","Making connections...\n","  Number of connections on node 0: 0 \n","  Done; cell connection time = 0.00 s.\n","Adding stims...\n","  Added Input IClamp to cell gid=0, sec=soma, loc=0.5, del=500, dur=800, amp=-0.1\n","  Number of stims on node 0: 1 \n","  Done; cell stims creation time = 0.00 s.\n","  Recording  V_soma from cell  0  with parameters:  {'sec': 'soma', 'loc': 0.5, 'var': 'v'}\n","Vector[2]\n","   Recording: spkt:\n","   Recording: spkid:\n","   Recording: V_soma:\n","      cell_0\n","   Recording: t:\n","Recording 1 traces of 1 types on node 0\n","\n","Warning: global variable v_init=-65.0 differs from that set for each section in cellParams rule TC_reduced: -70.0\n","\n","Setting h global variables ...\n","  h.celsius = 36\n","  h.v_init = -65.0\n","  h.clamp_resist = 0.001\n","  h.erev_kl = -95.0\n","  h.q10m_ittc = 3.55\n","  h.q10h_itre = 3.0\n","  h.tstop = 2000.0\n","Minimum delay (time-step for queue exchange) is 10.00\n","\n","Running simulation for 2000.0 ms...\n","  Done; run time = 0.44 s; real-time ratio: 4.51.\n","\n","Gathering data...\n","  Done; gather time = 0.02 s.\n","\n","Analyzing...\n","  Cells: 1\n","  Connections: 0 (0.00 per cell)\n","  Spikes: 0 (0.00 Hz)\n","  Simulated time: 2.0 s; 1 workers\n","  Run time: 0.44 s\n","  Done; saving time = 0.02 s.\n","Plotting recorded cell traces ... cell\n","  Done; plotting time = 0.24 s\n","\n","Total time = 0.75 s\n","\n","End time:  2021-05-25 13:28:14.965426\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"DpQo7_2SSY9i"},"source":["# (4) Plotting Morphology"]},{"cell_type":"code","metadata":{"id":"QjACpPK5TNt0"},"source":["netParams.popParams['HH3D_pop_hoc'] = {'cellType': 'PYR', 'numCells': 1, 'cellModel': 'HH3D_hoc'}\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"3gT52ZdrTf5M","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621949296526,"user_tz":-180,"elapsed":1466,"user":{"displayName":"Michael Mazar","photoUrl":"","userId":"15788570157356013099"}},"outputId":"a77689dc-1bc5-4e86-d0ce-7930ca5c8cf1"},"source":["sim.createSimulateAnalyze(netParams = netParams, simConfig = cfg)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["\n","Start time:  2021-05-25 13:28:14.990431\n","\n","Creating network of 2 cell populations on 1 hosts...\n","Distributed population of 1 cells on 1 hosts: {0: [0]}, next: 0\n","Cell 0/0 (gid=0) of pop TC_pop, on node 0, \n","Instantiated 1 cells of population TC_pop\n","Distributed population of 1 cells on 1 hosts: {0: [0]}, next: 0\n","Cell 0/0 (gid=1) of pop HH3D_pop_hoc, on node 0, \n","Instantiated 1 cells of population HH3D_pop_hoc\n","  Number of cells on node 0: 2 \n","  Done; cell creation time = 0.02 s.\n","Making connections...\n","  Number of connections on node 0: 0 \n","  Done; cell connection time = 0.00 s.\n","Adding stims...\n","  Added Input IClamp to cell gid=0, sec=soma, loc=0.5, del=500, dur=800, amp=-0.1\n","  Number of stims on node 0: 1 \n","  Done; cell stims creation time = 0.00 s.\n","  Recording  V_soma from cell  0  with parameters:  {'sec': 'soma', 'loc': 0.5, 'var': 'v'}\n","Vector[2]\n","   Recording: spkt:\n","   Recording: spkid:\n","   Recording: V_soma:\n","      cell_0\n","   Recording: t:\n","Recording 1 traces of 1 types on node 0\n","\n","Warning: global variable v_init=-65.0 differs from that set for each section in cellParams rule TC_reduced: -70.0\n","\n","Setting h global variables ...\n","  h.celsius = 36\n","  h.v_init = -65.0\n","  h.clamp_resist = 0.001\n","  h.erev_kl = -95.0\n","  h.q10m_ittc = 3.55\n","  h.q10h_itre = 3.0\n","  h.tstop = 2000.0\n","Minimum delay (time-step for queue exchange) is 10.00\n","\n","Running simulation for 2000.0 ms...\n","  Done; run time = 0.98 s; real-time ratio: 2.04.\n","\n","Gathering data...\n","  Done; gather time = 0.06 s.\n","\n","Analyzing...\n","  Cells: 2\n","  Connections: 0 (0.00 per cell)\n","  Spikes: 0 (0.00 Hz)\n","  Simulated time: 2.0 s; 1 workers\n","  Run time: 0.98 s\n","  Done; saving time = 0.03 s.\n","Plotting recorded cell traces ... cell\n","  Done; plotting time = 0.26 s\n","\n","Total time = 1.36 s\n","\n","End time:  2021-05-25 13:28:16.347365\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"CnzDTabm6o-O"},"source":["%matplotlib inline"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"d4159PslSbqt","colab":{"base_uri":"https://localhost:8080/","height":627},"executionInfo":{"status":"ok","timestamp":1621949297155,"user_tz":-180,"elapsed":631,"user":{"displayName":"Michael Mazar","photoUrl":"","userId":"15788570157356013099"}},"outputId":"698eae75-7743-43ae-c138-eaeaff6072ed"},"source":["sim.analysis.plotShape(includePre = [], includePost=['HH3D_pop_hoc'], showSyns=False, figSize=(4,9), dist=0.8, saveFig=True)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Plotting 3D cell shape ...\n","  There was an exception in plotShape(): \n","    'Axes3DSubplot' object has no attribute 'set_box_aspect' \n","    (<class 'AttributeError'>, AttributeError(\"'Axes3DSubplot' object has no attribute 'set_box_aspect'\"), <traceback object at 0x7f0a93012280>)\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["-1"]},"metadata":{"tags":[]},"execution_count":43},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 288x648 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"cwHRwygbYNQN"},"source":["**EXERCISE: Try plotting the morphology of other cell models**"]},{"cell_type":"code","metadata":{"id":"tj77u5q6YSa8"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"FkxZ_tb8mtkh"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"MLjM4YqKa4oX"},"source":["# (5) Making a Network"]},{"cell_type":"markdown","metadata":{"id":"RPVG8lsNbvl8"},"source":["**EXERCISE: To begin creating a network, specify the geometry of the area you would like to model.**"]},{"cell_type":"code","metadata":{"id":"ADsqCBtUbzRu"},"source":["netParams.sizeX = 200"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"thRVXKC-b1S7"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"lTEU5QELb1jX"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"kbdQUbOVb489"},"source":["**Now let's set the propagation velocity and length constant:**"]},{"cell_type":"code","metadata":{"id":"A_6wo1c8b8eg"},"source":["netParams.propVelocity = 100.0 # propagation velocity (um/ms)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"sARIVS3Nb8WZ"},"source":["netParams.probLengthConst = 150.0 # length constant for conn probability (um)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"n9luKE_8bceU"},"source":["**EXERCISE: Now establish a few populations of cells**"]},{"cell_type":"code","metadata":{"id":"IOxompk7brVj"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"YGZB1FSUcFZk"},"source":["**Now we need some synaptic mechanism parameters**"]},{"cell_type":"code","metadata":{"id":"YEGu8fEAcJzZ"},"source":["netParams.synMechParams['exc'] = {'mod': 'Exp2Syn', 'tau1': 0.8, 'tau2': 5.3, 'e': 0} # NMDA synaptic mechanism"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"RfozdJr2caFZ"},"source":["netParams.synMechParams['inh'] = {'mod': 'Exp2Syn', 'tau1': 0.6, 'tau2': 8.5, 'e': -75}  # GABA synaptic mechanism\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"5zInMwbdcl5_"},"source":["**Add some network stimulation parameters**"]},{"cell_type":"code","metadata":{"id":"1iwUL9i7c5Uc"},"source":["netParams.stimSourceParams['bkg'] = {'type': 'NetStim', 'rate': 40, 'noise': 0.3}"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"p7em5PMjc-Hw"},"source":["**EXERCISE: modify the line below such that your stim object can target the populations in your network**"]},{"cell_type":"code","metadata":{"id":"VZynW-7Kc8hy"},"source":["netParams.stimTargetParams['bkg->all'] = {'source': 'bkg',\n","                                          'conds': {'cellType': ['E','I']}, \n","                                          'weight': 10.0, 'sec': 'soma', \n","                                          'delay': 'max(1, normal(5,2))', \n","                                          'synMech': 'exc'}\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"AHC-ARkjdHPK"},"source":["**Add cell connectivity rules**"]},{"cell_type":"markdown","metadata":{"id":"5KAMhC8idT4O"},"source":["**EXERCISE: modify the lines below to fit your network**"]},{"cell_type":"code","metadata":{"id":"8yPsOXjQdKpN"},"source":["netParams.connParams['E->all'] = {\n","  'preConds': {'cellType': 'E'}, 'postConds': {'y': [100,1000]},  #  E -> all (100-1000 um)\n","  'probability': 0.1 ,                  # probability of connection\n","  'weight': '5.0*post_ynorm',         # synaptic weight\n","  'delay': 'dist_3D/propVelocity',      # transmission delay (ms)\n","  'synMech': 'exc'}                     # synaptic mechanism"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"avZKTXNcd5LJ"},"source":["**EXERCISE: Add the appropriate line(s) to run the network and plot a 2D representation of your network w/ connectivity between cells**"]},{"cell_type":"code","metadata":{"id":"f-sUlmhEd4rW"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"lBbmWHJYdsua"},"source":["cfg.analysis['plot2Dnet'] = {'saveFig': True}                                                # plot 2D cell positions and connections\n","cfg.analysis['plotConn'] = {'saveFig': True}                                                 # plot connectivity matrix\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"kGeVPYC0eHA1"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/netpyne/tutorials/netpyne-course-2021/netpyne_analysis_plotting.ipynb b/netpyne/tutorials/netpyne-course-2021/netpyne_analysis_plotting.ipynb
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-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NetPyNE2021_Analysis_Plotting.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"RRlxTCVG35B0"},"source":["# Analysis and Plotting in NetPyNE\n","\n","## Install NEURON and NetPyNE"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Lb2FZBx63Z70","executionInfo":{"status":"ok","timestamp":1623997037989,"user_tz":-300,"elapsed":8199,"user":{"displayName":"Evgenia Karunus","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhA2ccNLhFnt0hUfl13RoguCJ6cDbCPCY8_SVjQAg=s64","userId":"04024508215281503990"}},"outputId":"8d3fd7fe-a34d-4bd4-9d54-a35a50dfd717"},"source":["!pip install neuron\n","!pip install netpyne"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting neuron\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/14/f4/ea50608c7633c286859d6cce0aad621da22a8da7ff9787efc8bb71fe0597/NEURON-8.0.0-cp37-cp37m-manylinux1_x86_64.whl (12.6MB)\n","\u001b[K     |████████████████████████████████| 12.6MB 220kB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.9.3 in /usr/local/lib/python3.7/dist-packages (from neuron) (1.19.5)\n","Installing collected packages: neuron\n","Successfully installed neuron-8.0.0\n","Collecting netpyne\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9e/24/0f9d685a3fbcbca0d86d9ca6521465c43725d9e23760c91524fe77191f12/netpyne-1.0.0.2-py2.py3-none-any.whl (312kB)\n","\u001b[K     |████████████████████████████████| 317kB 6.7MB/s \n","\u001b[?25hRequirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from netpyne) (0.16.0)\n","Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from netpyne) (1.1.5)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from netpyne) (3.2.2)\n","Collecting matplotlib-scalebar\n","  Downloading https://files.pythonhosted.org/packages/51/a4/cd254234c35f3591361988e89ab132ee14789f2ebe1ede621d63f5241f00/matplotlib_scalebar-0.7.2-py2.py3-none-any.whl\n","Requirement already satisfied: bokeh in /usr/local/lib/python3.7/dist-packages (from netpyne) (2.3.2)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from netpyne) (1.4.1)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from netpyne) (1.19.5)\n","Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->netpyne) (2018.9)\n","Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->netpyne) (2.8.1)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->netpyne) (1.3.1)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->netpyne) (2.4.7)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->netpyne) (0.10.0)\n","Requirement already satisfied: typing-extensions>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (3.7.4.3)\n","Requirement already satisfied: tornado>=5.1 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (5.1.1)\n","Requirement already satisfied: Jinja2>=2.9 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (2.11.3)\n","Requirement already satisfied: PyYAML>=3.10 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (3.13)\n","Requirement already satisfied: packaging>=16.8 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (20.9)\n","Requirement already satisfied: pillow>=7.1.0 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (7.1.2)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->netpyne) (1.15.0)\n","Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from Jinja2>=2.9->bokeh->netpyne) (2.0.1)\n","Installing collected packages: matplotlib-scalebar, netpyne\n","Successfully installed matplotlib-scalebar-0.7.2 netpyne-1.0.0.2\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"FhKgNeJo34gl"},"source":["## Clone the NetPyNE GUI workspace and compile the mod files"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qH-4GbDW_RWT","executionInfo":{"status":"ok","timestamp":1623997043114,"user_tz":-300,"elapsed":5129,"user":{"displayName":"Evgenia Karunus","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhA2ccNLhFnt0hUfl13RoguCJ6cDbCPCY8_SVjQAg=s64","userId":"04024508215281503990"}},"outputId":"728d7b04-ecda-45e9-84a3-6358879f467c"},"source":["!git clone https://github.com/Neurosim-lab/netpyne_workspace"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Cloning into 'netpyne_workspace'...\n","remote: Enumerating objects: 423, done.\u001b[K\n","remote: Counting objects: 100% (151/151), done.\u001b[K\n","remote: Compressing objects: 100% (105/105), done.\u001b[K\n","remote: Total 423 (delta 86), reused 108 (delta 46), pack-reused 272\u001b[K\n","Receiving objects: 100% (423/423), 79.12 MiB | 33.27 MiB/s, done.\n","Resolving deltas: 100% (235/235), done.\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"UvFYdtzv_eFZ"},"source":["Now we will change into that directory and compile the mod files."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VFm9iXoX4M2c","executionInfo":{"status":"ok","timestamp":1623997043115,"user_tz":-300,"elapsed":10,"user":{"displayName":"Evgenia Karunus","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhA2ccNLhFnt0hUfl13RoguCJ6cDbCPCY8_SVjQAg=s64","userId":"04024508215281503990"}},"outputId":"fecb5c0a-9c9d-4049-9de7-cfc46013a769"},"source":["cd netpyne_workspace"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/content/netpyne_workspace\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CwALCwzsHi-y","executionInfo":{"status":"ok","timestamp":1623997049734,"user_tz":-300,"elapsed":6626,"user":{"displayName":"Evgenia Karunus","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhA2ccNLhFnt0hUfl13RoguCJ6cDbCPCY8_SVjQAg=s64","userId":"04024508215281503990"}},"outputId":"4fb0ef38-d2fb-4e01-9881-6ec3f18c8524"},"source":["!nrnivmodl mod"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/content/netpyne_workspace\n","Mod files: \"mod/ar.mod\" \"mod/ar_traub.mod\" \"mod/beforestep_py.mod\" \"mod/cadad.mod\" \"mod/cad.mod\" \"mod/cadyn.mod\" \"mod/cagk.mod\" \"mod/cal_mh.mod\" \"mod/cal_mig.mod\" \"mod/ca.mod\" \"mod/canin.mod\" \"mod/can_mig.mod\" \"mod/catcb.mod\" \"mod/cat_mig.mod\" \"mod/cat.mod\" \"mod/cat_traub.mod\" \"mod/dipole.mod\" \"mod/dipole_pp.mod\" \"mod/gabab.mod\" \"mod/h_BS.mod\" \"mod/HCN1.mod\" \"mod/hh2.mod\" \"mod/hh3.mod\" \"mod/h_harnett.mod\" \"mod/hin.mod\" \"mod/h_kole.mod\" \"mod/h_migliore.mod\" \"mod/ican_sidi.mod\" \"mod/IC.mod\" \"mod/IKsin.mod\" \"mod/kap_BS.mod\" \"mod/kapcb.mod\" \"mod/kapin.mod\" \"mod/kBK.mod\" \"mod/kca.mod\" \"mod/kctin.mod\" \"mod/kdmc_BS.mod\" \"mod/kdr_BS.mod\" \"mod/kdrin.mod\" \"mod/km.mod\" \"mod/lfp.mod\" \"mod/mea.mod\" \"mod/nafx.mod\" \"mod/nap_sidi.mod\" \"mod/nax_BS.mod\" \"mod/savedist.mod\" \"mod/vecevent.mod\" \"mod/vecstim.mod\"\n","\n","Creating x86_64 directory for .o files.\n","\n","COBJS=''\n"," -> \u001b[32mCompiling\u001b[0m mod_func.c\n","x86_64-linux-gnu-gcc -O2   -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c mod_func.c -o mod_func.o\n"," -> \u001b[32mNMODL\u001b[0m ../mod/beforestep_py.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl beforestep_py.mod -o \"/content/netpyne_workspace/x86_64\")\n"," -> \u001b[32mNMODL\u001b[0m ../mod/ar.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl ar.mod -o \"/content/netpyne_workspace/x86_64\")\n","Translating beforestep_py.mod into /content/netpyne_workspace/x86_64/beforestep_py.c\n","Notice: Use of POINTER is not thread safe.\n","Notice: VERBATIM blocks are not thread safe\n"," -> \u001b[32mNMODL\u001b[0m ../mod/ar_traub.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl ar_traub.mod -o \"/content/netpyne_workspace/x86_64\")\n","Translating ar.mod into /content/netpyne_workspace/x86_64/ar.c\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m ../mod/cadad.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl cadad.mod -o \"/content/netpyne_workspace/x86_64\")\n","Translating ar_traub.mod into /content/netpyne_workspace/x86_64/ar_traub.c\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m ../mod/cad.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl cad.mod -o \"/content/netpyne_workspace/x86_64\")\n","Translating cadad.mod into /content/netpyne_workspace/x86_64/cadad.c\n"," -> \u001b[32mNMODL\u001b[0m ../mod/cadyn.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl cadyn.mod -o \"/content/netpyne_workspace/x86_64\")\n","Thread Safe\n","Translating cad.mod into /content/netpyne_workspace/x86_64/cad.c\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m ../mod/cagk.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl cagk.mod -o \"/content/netpyne_workspace/x86_64\")\n"," -> \u001b[32mNMODL\u001b[0m ../mod/cal_mh.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl cal_mh.mod -o \"/content/netpyne_workspace/x86_64\")\n","Translating cal_mh.mod into /content/netpyne_workspace/x86_64/cal_mh.c\n","Translating cagk.mod into /content/netpyne_workspace/x86_64/cagk.c\n","Translating cadyn.mod into /content/netpyne_workspace/x86_64/cadyn.c\n","Thread Safe\n","Thread Safe\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m ../mod/cal_mig.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl cal_mig.mod -o \"/content/netpyne_workspace/x86_64\")\n"," -> \u001b[32mNMODL\u001b[0m ../mod/ca.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl ca.mod -o \"/content/netpyne_workspace/x86_64\")\n"," -> \u001b[32mNMODL\u001b[0m ../mod/canin.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl canin.mod -o \"/content/netpyne_workspace/x86_64\")\n","Translating cal_mig.mod into /content/netpyne_workspace/x86_64/cal_mig.c\n","Warning: Default 2 of PARAMETER cao will be ignored and set by NEURON.\n","Translating ca.mod into /content/netpyne_workspace/x86_64/ca.c\n","Notice: Assignment to the GLOBAL variable, \"tadj\", is not thread safe\n","Warning: Default 5e-05 of PARAMETER cai will be ignored and set by NEURON.\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m ../mod/can_mig.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl can_mig.mod -o \"/content/netpyne_workspace/x86_64\")\n","Translating canin.mod into /content/netpyne_workspace/x86_64/canin.c\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m ../mod/catcb.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl catcb.mod -o \"/content/netpyne_workspace/x86_64\")\n"," -> \u001b[32mNMODL\u001b[0m ../mod/cat_mig.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl cat_mig.mod -o \"/content/netpyne_workspace/x86_64\")\n","Translating can_mig.mod into /content/netpyne_workspace/x86_64/can_mig.c\n","Translating catcb.mod into /content/netpyne_workspace/x86_64/catcb.c\n","Warning: Default 2 of PARAMETER cao will be ignored and set by NEURON.\n","Warning: Default 5e-05 of PARAMETER cai will be ignored and set by NEURON.\n","Notice: Assignment to the GLOBAL variable, \"hinf\", is not thread safe\n","Notice: Assignment to the GLOBAL variable, \"minf\", is not thread safe\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m ../mod/cat.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl cat.mod -o \"/content/netpyne_workspace/x86_64\")\n"," -> \u001b[32mNMODL\u001b[0m ../mod/cat_traub.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl cat_traub.mod -o \"/content/netpyne_workspace/x86_64\")\n","Translating cat_mig.mod into /content/netpyne_workspace/x86_64/cat_mig.c\n","Warning: Default 2 of PARAMETER cao will be ignored and set by NEURON.\n","Warning: Default 5e-05 of PARAMETER cai will be ignored and set by NEURON.\n","Warning: Default 25 of PARAMETER celsius will be ignored and set by NEURON.\n","Thread Safe\n","Translating cat.mod into /content/netpyne_workspace/x86_64/cat.c\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m ../mod/dipole.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl dipole.mod -o \"/content/netpyne_workspace/x86_64\")\n"," -> \u001b[32mNMODL\u001b[0m ../mod/dipole_pp.mod\n","Translating cat_traub.mod into /content/netpyne_workspace/x86_64/cat_traub.c\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl dipole_pp.mod -o \"/content/netpyne_workspace/x86_64\")\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m ../mod/gabab.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl gabab.mod -o \"/content/netpyne_workspace/x86_64\")\n","Translating dipole_pp.mod into /content/netpyne_workspace/x86_64/dipole_pp.c\n","Notice: Use of POINTER is not thread safe.\n","Notice: Use of POINTER is not thread safe.\n","Translating dipole.mod into /content/netpyne_workspace/x86_64/dipole.c\n","Notice: Use of POINTER is not thread safe.\n","Notice: Use of POINTER is not thread safe.\n","Notice: Use of POINTER is not thread safe.\n"," -> \u001b[32mNMODL\u001b[0m ../mod/h_BS.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl h_BS.mod -o \"/content/netpyne_workspace/x86_64\")\n"," -> \u001b[32mNMODL\u001b[0m ../mod/HCN1.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl HCN1.mod -o \"/content/netpyne_workspace/x86_64\")\n","Translating gabab.mod into /content/netpyne_workspace/x86_64/gabab.c\n","Translating h_BS.mod into /content/netpyne_workspace/x86_64/h_BS.c\n","Notice: Assignment to the GLOBAL variable, \"warn\", is not thread safe\n","Warning: Default 34 of PARAMETER celsius will be ignored and set by NEURON.\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m ../mod/hh3.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl hh3.mod -o \"/content/netpyne_workspace/x86_64\")\n"," -> \u001b[32mNMODL\u001b[0m ../mod/hh2.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl hh2.mod -o \"/content/netpyne_workspace/x86_64\")\n","Translating HCN1.mod into /content/netpyne_workspace/x86_64/HCN1.c\n","Thread Safe\n","Translating hh3.mod into /content/netpyne_workspace/x86_64/hh3.c\n","Notice: VERBATIM blocks are not thread safe\n"," -> \u001b[32mNMODL\u001b[0m ../mod/h_harnett.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl h_harnett.mod -o \"/content/netpyne_workspace/x86_64\")\n","Notice: This mechanism cannot be used with CVODE\n","Warning: Default 37 of PARAMETER celsius will be ignored and set by NEURON.\n","Warning: Default -80 of PARAMETER ek will be ignored and set by NEURON.\n","Warning: Default 40 of PARAMETER ena will be ignored and set by NEURON.\n","Translating hh2.mod into /content/netpyne_workspace/x86_64/hh2.c\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m ../mod/hin.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl hin.mod -o \"/content/netpyne_workspace/x86_64\")\n"," -> \u001b[32mNMODL\u001b[0m ../mod/h_kole.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl h_kole.mod -o \"/content/netpyne_workspace/x86_64\")\n","Translating h_harnett.mod into /content/netpyne_workspace/x86_64/h_harnett.c\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m ../mod/h_migliore.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl h_migliore.mod -o \"/content/netpyne_workspace/x86_64\")\n","Translating hin.mod into /content/netpyne_workspace/x86_64/hin.c\n","Warning: Default -10 of PARAMETER ehi will be ignored and set by NEURON.\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m ../mod/ican_sidi.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl ican_sidi.mod -o \"/content/netpyne_workspace/x86_64\")\n","Translating h_migliore.mod into /content/netpyne_workspace/x86_64/h_migliore.c\n","Thread Safe\n","Translating h_kole.mod into /content/netpyne_workspace/x86_64/h_kole.c\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m ../mod/IC.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl IC.mod -o \"/content/netpyne_workspace/x86_64\")\n","Translating ican_sidi.mod into /content/netpyne_workspace/x86_64/ican_sidi.c\n"," -> \u001b[32mNMODL\u001b[0m ../mod/IKsin.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl IKsin.mod -o \"/content/netpyne_workspace/x86_64\")\n","Warning: Default 36 of PARAMETER celsius will be ignored and set by NEURON.\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m ../mod/kap_BS.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl kap_BS.mod -o \"/content/netpyne_workspace/x86_64\")\n","Translating IC.mod into /content/netpyne_workspace/x86_64/IC.c\n","Thread Safe\n","Translating IKsin.mod into /content/netpyne_workspace/x86_64/IKsin.c\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m ../mod/kapcb.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl kapcb.mod -o \"/content/netpyne_workspace/x86_64\")\n","Translating kap_BS.mod into /content/netpyne_workspace/x86_64/kap_BS.c\n"," -> \u001b[32mNMODL\u001b[0m ../mod/kapin.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl kapin.mod -o \"/content/netpyne_workspace/x86_64\")\n","Thread Safe\n","Translating kapin.mod into /content/netpyne_workspace/x86_64/kapin.c\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m ../mod/kBK.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl kBK.mod -o \"/content/netpyne_workspace/x86_64\")\n"," -> \u001b[32mNMODL\u001b[0m ../mod/kca.mod\n","Translating kapcb.mod into /content/netpyne_workspace/x86_64/kapcb.c\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl kca.mod -o \"/content/netpyne_workspace/x86_64\")\n","Thread Safe\n","Translating kBK.mod into /content/netpyne_workspace/x86_64/kBK.c\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m ../mod/kctin.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl kctin.mod -o \"/content/netpyne_workspace/x86_64\")\n"," -> \u001b[32mNMODL\u001b[0m ../mod/kdmc_BS.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl kdmc_BS.mod -o \"/content/netpyne_workspace/x86_64\")\n","Translating kca.mod into /content/netpyne_workspace/x86_64/kca.c\n","Notice: Assignment to the GLOBAL variable, \"tadj\", is not thread safe\n","Translating kctin.mod into /content/netpyne_workspace/x86_64/kctin.c\n","NEURON's CVode method ignores conservation\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m ../mod/kdr_BS.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl kdr_BS.mod -o \"/content/netpyne_workspace/x86_64\")\n"," -> \u001b[32mNMODL\u001b[0m ../mod/kdrin.mod\n","Translating kdmc_BS.mod into /content/netpyne_workspace/x86_64/kdmc_BS.c\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl kdrin.mod -o \"/content/netpyne_workspace/x86_64\")\n","Thread Safe\n","Translating kdr_BS.mod into /content/netpyne_workspace/x86_64/kdr_BS.c\n"," -> \u001b[32mNMODL\u001b[0m ../mod/km.mod\n","Thread Safe\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl km.mod -o \"/content/netpyne_workspace/x86_64\")\n","Translating kdrin.mod into /content/netpyne_workspace/x86_64/kdrin.c\n"," -> \u001b[32mNMODL\u001b[0m ../mod/lfp.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl lfp.mod -o \"/content/netpyne_workspace/x86_64\")\n","Thread Safe\n","Translating km.mod into /content/netpyne_workspace/x86_64/km.c\n"," -> \u001b[32mNMODL\u001b[0m ../mod/mea.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl mea.mod -o \"/content/netpyne_workspace/x86_64\")\n","Notice: Assignment to the GLOBAL variable, \"tadj\", is not thread safe\n","Translating lfp.mod into /content/netpyne_workspace/x86_64/lfp.c\n","Notice: Use of POINTER is not thread safe.\n","Translating mea.mod into /content/netpyne_workspace/x86_64/mea.c\n","Notice: Use of POINTER is not thread safe.\n"," -> \u001b[32mNMODL\u001b[0m ../mod/nafx.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl nafx.mod -o \"/content/netpyne_workspace/x86_64\")\n"," -> \u001b[32mNMODL\u001b[0m ../mod/nap_sidi.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl nap_sidi.mod -o \"/content/netpyne_workspace/x86_64\")\n"," -> \u001b[32mNMODL\u001b[0m ../mod/nax_BS.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl nax_BS.mod -o \"/content/netpyne_workspace/x86_64\")\n","Translating nafx.mod into /content/netpyne_workspace/x86_64/nafx.c\n","Translating nax_BS.mod into /content/netpyne_workspace/x86_64/nax_BS.c\n","Warning: Default 55 of PARAMETER ena will be ignored and set by NEURON.\n","Thread Safe\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m ../mod/savedist.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl savedist.mod -o \"/content/netpyne_workspace/x86_64\")\n","Translating nap_sidi.mod into /content/netpyne_workspace/x86_64/nap_sidi.c\n"," -> \u001b[32mNMODL\u001b[0m ../mod/vecevent.mod\n","Thread Safe\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl vecevent.mod -o \"/content/netpyne_workspace/x86_64\")\n"," -> \u001b[32mNMODL\u001b[0m ../mod/vecstim.mod\n","(cd \"../mod\"; MODLUNIT=/usr/local/lib/python3.7/dist-packages/neuron/.data/share/nrn/lib/nrnunits.lib /usr/local/lib/python3.7/dist-packages/neuron/.data/bin/nocmodl vecstim.mod -o \"/content/netpyne_workspace/x86_64\")\n","Translating vecevent.mod into /content/netpyne_workspace/x86_64/vecevent.c\n","Notice: VERBATIM blocks are not thread safe\n","Translating savedist.mod into /content/netpyne_workspace/x86_64/savedist.c\n","Thread Safe\n"," -> \u001b[32mCompiling\u001b[0m ar.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c ar.c -o ar.o\n"," -> \u001b[32mCompiling\u001b[0m ar_traub.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c ar_traub.c -o ar_traub.o\n","Translating vecstim.mod into /content/netpyne_workspace/x86_64/vecstim.c\n","Thread Safe\n"," -> \u001b[32mCompiling\u001b[0m beforestep_py.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c beforestep_py.c -o beforestep_py.o\n"," -> \u001b[32mCompiling\u001b[0m cad.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c cad.c -o cad.o\n"," -> \u001b[32mCompiling\u001b[0m cadyn.c\n"," -> \u001b[32mCompiling\u001b[0m cadad.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c cadyn.c -o cadyn.o\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c cadad.c -o cadad.o\n"," -> \u001b[32mCompiling\u001b[0m cagk.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c cagk.c -o cagk.o\n"," -> \u001b[32mCompiling\u001b[0m cal_mh.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c cal_mh.c -o cal_mh.o\n"," -> \u001b[32mCompiling\u001b[0m cal_mig.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c cal_mig.c -o cal_mig.o\n"," -> \u001b[32mCompiling\u001b[0m ca.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c ca.c -o ca.o\n"," -> \u001b[32mCompiling\u001b[0m canin.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c canin.c -o canin.o\n"," -> \u001b[32mCompiling\u001b[0m can_mig.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c can_mig.c -o can_mig.o\n"," -> \u001b[32mCompiling\u001b[0m catcb.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c catcb.c -o catcb.o\n"," -> \u001b[32mCompiling\u001b[0m cat_mig.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c cat_mig.c -o cat_mig.o\n"," -> \u001b[32mCompiling\u001b[0m cat.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c cat.c -o cat.o\n"," -> \u001b[32mCompiling\u001b[0m cat_traub.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c cat_traub.c -o cat_traub.o\n"," -> \u001b[32mCompiling\u001b[0m dipole.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c dipole.c -o dipole.o\n"," -> \u001b[32mCompiling\u001b[0m dipole_pp.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c dipole_pp.c -o dipole_pp.o\n"," -> \u001b[32mCompiling\u001b[0m gabab.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c gabab.c -o gabab.o\n"," -> \u001b[32mCompiling\u001b[0m h_BS.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c h_BS.c -o h_BS.o\n"," -> \u001b[32mCompiling\u001b[0m HCN1.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c HCN1.c -o HCN1.o\n"," -> \u001b[32mCompiling\u001b[0m hh2.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c hh2.c -o hh2.o\n"," -> \u001b[32mCompiling\u001b[0m hh3.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c hh3.c -o hh3.o\n"," -> \u001b[32mCompiling\u001b[0m h_harnett.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c h_harnett.c -o h_harnett.o\n"," -> \u001b[32mCompiling\u001b[0m hin.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c hin.c -o hin.o\n"," -> \u001b[32mCompiling\u001b[0m h_kole.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c h_kole.c -o h_kole.o\n"," -> \u001b[32mCompiling\u001b[0m h_migliore.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c h_migliore.c -o h_migliore.o\n"," -> \u001b[32mCompiling\u001b[0m ican_sidi.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c ican_sidi.c -o ican_sidi.o\n"," -> \u001b[32mCompiling\u001b[0m IC.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c IC.c -o IC.o\n"," -> \u001b[32mCompiling\u001b[0m IKsin.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c IKsin.c -o IKsin.o\n"," -> \u001b[32mCompiling\u001b[0m kap_BS.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c kap_BS.c -o kap_BS.o\n"," -> \u001b[32mCompiling\u001b[0m kapcb.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c kapcb.c -o kapcb.o\n"," -> \u001b[32mCompiling\u001b[0m kapin.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c kapin.c -o kapin.o\n"," -> \u001b[32mCompiling\u001b[0m kBK.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c kBK.c -o kBK.o\n"," -> \u001b[32mCompiling\u001b[0m kca.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c kca.c -o kca.o\n"," -> \u001b[32mCompiling\u001b[0m kctin.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c kctin.c -o kctin.o\n"," -> \u001b[32mCompiling\u001b[0m kdmc_BS.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c kdmc_BS.c -o kdmc_BS.o\n"," -> \u001b[32mCompiling\u001b[0m kdr_BS.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c kdr_BS.c -o kdr_BS.o\n"," -> \u001b[32mCompiling\u001b[0m kdrin.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c kdrin.c -o kdrin.o\n"," -> \u001b[32mCompiling\u001b[0m km.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c km.c -o km.o\n"," -> \u001b[32mCompiling\u001b[0m lfp.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c lfp.c -o lfp.o\n"," -> \u001b[32mCompiling\u001b[0m mea.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c mea.c -o mea.o\n"," -> \u001b[32mCompiling\u001b[0m nafx.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c nafx.c -o nafx.o\n"," -> \u001b[32mCompiling\u001b[0m nap_sidi.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c nap_sidi.c -o nap_sidi.o\n"," -> \u001b[32mCompiling\u001b[0m nax_BS.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c nax_BS.c -o nax_BS.o\n"," -> \u001b[32mCompiling\u001b[0m savedist.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c savedist.c -o savedist.o\n"," -> \u001b[32mCompiling\u001b[0m vecevent.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c vecevent.c -o vecevent.o\n"," -> \u001b[32mCompiling\u001b[0m vecstim.c\n","x86_64-linux-gnu-gcc -O2   -I\"../mod\" -I.   -I/usr/local/lib/python3.7/dist-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c vecstim.c -o vecstim.o\n"," => \u001b[32mLINKING\u001b[0m shared library ./libnrnmech.so\n","x86_64-linux-gnu-g++ -O2 -DVERSION_INFO='8.0.0' -std=c++11 -shared -fPIC  -I /usr/local/lib/python3.7/dist-packages/neuron/.data/include -o ./libnrnmech.so -Wl,-soname,libnrnmech.so \\\n","  ./mod_func.o ./ar.o ./ar_traub.o ./beforestep_py.o ./cadad.o ./cad.o ./cadyn.o ./cagk.o ./cal_mh.o ./cal_mig.o ./ca.o ./canin.o ./can_mig.o ./catcb.o ./cat_mig.o ./cat.o ./cat_traub.o ./dipole.o ./dipole_pp.o ./gabab.o ./h_BS.o ./HCN1.o ./hh2.o ./hh3.o ./h_harnett.o ./hin.o ./h_kole.o ./h_migliore.o ./ican_sidi.o ./IC.o ./IKsin.o ./kap_BS.o ./kapcb.o ./kapin.o ./kBK.o ./kca.o ./kctin.o ./kdmc_BS.o ./kdr_BS.o ./kdrin.o ./km.o ./lfp.o ./mea.o ./nafx.o ./nap_sidi.o ./nax_BS.o ./savedist.o ./vecevent.o ./vecstim.o  -L/usr/local/lib/python3.7/dist-packages/neuron/.data/lib -lnrniv -Wl,-rpath,/usr/local/lib/python3.7/dist-packages/neuron/.data/lib   \n","rm -f ./.libs/libnrnmech.so ; mkdir -p ./.libs ; cp ./libnrnmech.so ./.libs/libnrnmech.so\n","Successfully created x86_64/special\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"VxNcuIfJ65LH"},"source":["## Load a tutorial\n","\n","We will be modifying `gut_tut3` and running that to explore analyses and plotting.\n"]},{"cell_type":"code","metadata":{"id":"ot3uw7v94Sw1"},"source":["from netpyne import specs\n","\n","\n","#------------------------------------------------------------------------------\n","#\n","# NETWORK PARAMETERS\n","#\n","#------------------------------------------------------------------------------\n","\n","netParams = specs.NetParams()  # object of class NetParams to store the network parameters\n","\n","netParams.sizeX = 100 # x-dimension (horizontal length) size in um\n","netParams.sizeY = 500 # y-dimension (vertical height or cortical depth) size in um\n","netParams.sizeZ = 100 # z-dimension (horizontal length) size in um\n","netParams.propVelocity = 100.0 # propagation velocity (um/ms)\n","netParams.probLengthConst = 150.0 # length constant for conn probability (um)\n","\n","#------------------------------------------------------------------------------\n","## Cell parameters\n","netParams.loadCellParams(label='E', fileName='cells/CSTR_cellParams.json')\n","netParams.importCellParams(label='I', fileName='cells/FScell.hoc', cellName='FScell')\n","\n","\n","#------------------------------------------------------------------------------\n","## Population parameters\n","netParams.popParams['E2'] = {'cellType': 'E', 'numCells': 10, 'yRange': [50,150]}\n","netParams.popParams['I2'] = {'cellType': 'I', 'numCells': 10, 'yRange': [50,150]}\n","netParams.popParams['E4'] = {'cellType': 'E', 'numCells': 10, 'yRange': [150,300]}\n","netParams.popParams['I4'] = {'cellType': 'I', 'numCells': 10, 'yRange': [150,300]}\n","netParams.popParams['E5'] = {'cellType': 'E', 'numCells': 10, 'ynormRange': [0.6,1.0]}\n","netParams.popParams['I5'] = {'cellType': 'I', 'numCells': 10, 'ynormRange': [0.6,1.0]}\n","\n","#------------------------------------------------------------------------------\n","## Synaptic mechanism parameters\n","netParams.synMechParams['exc'] = {'mod': 'Exp2Syn', 'tau1': 0.8, 'tau2': 5.3, 'e': 0}  # NMDA synaptic mechanism\n","netParams.synMechParams['inh'] = {'mod': 'Exp2Syn', 'tau1': 0.6, 'tau2': 8.5, 'e': -75}  # GABA synaptic mechanism\n","\n","#------------------------------------------------------------------------------\n","# Stimulation parameters\n","netParams.stimSourceParams['bkg'] = {'type': 'NetStim', 'rate': 20, 'noise': 0.3}\n","netParams.stimTargetParams['bkg->E'] = {'source': 'bkg', 'conds': {'cellType': ['E']}, 'weight': 0.02, 'sec': 'soma', 'delay': 'max(1, normal(5,2))', 'synMech': 'exc'}\n","netParams.stimTargetParams['bkg->I'] = {'source': 'bkg', 'conds': {'cellType': ['I']}, 'weight': 0.004, 'sec': 'soma', 'delay': 'max(1, normal(5,2))', 'synMech': 'exc'}\n","\n","#------------------------------------------------------------------------------\n","# Cell connectivity rules\n","netParams.connParams['E->all'] = {\n","  'preConds': {'cellType': 'E'}, 'postConds': {'y': [50,500]},  #  E -> all (100-1000 um)\n","  'probability': 0.1,                  # probability of connection\n","  'weight': '0.04*post_ynorm',         # synaptic weight \n","  'delay': 'dist_3D/propVelocity',      # transmission delay (ms) \n","  'synMech': 'exc'}                     # synaptic mechanism \n","\n","netParams.connParams['I->E'] = {\n","  'preConds': {'cellType': 'I'}, 'postConds': {'pop': ['E2','E4','E5']},       #  I -> E\n","  'probability': '0.3*exp(-dist_3D/probLengthConst)',   # probability of connection\n","  'weight': 0.01,                                      # synaptic weight \n","  'delay': 'dist_3D/propVelocity',                      # transmission delay (ms) \n","  'sec': ['soma','Bdend'], \n","  'synMech': 'inh'}                                     # synaptic mechanism \n","\n","\n","#------------------------------------------------------------------------------\n","## RxD params\n","\n","### constants\n","\n","## Change ip3_init from 0 to 0.1 to observe multiscale effect:  \n","## netParams.rxdParams['constants']['ip3_init'] = 0.1\n","## high ip3 -> ER Ca released to Cyt -> kBK channels open -> less firing \n","\n","constants = {'ip3_init': 0.0,  # initial ip3 concentration \n","            'caDiff': 0.08,  # calcium diffusion coefficient\n","            'ip3Diff': 1.41,  # ip3 diffusion coefficient\n","            'caci_init': 1e-5,  # intracellular calcium initial concentration\n","            'caco_init': 2.0,   # extracellular calcium initial concentration\n","            'gip3r': 12040 * 100,  # ip3 receptors density\n","            'gserca': 0.3913,  # SERCA conductance\n","            'gleak': 6.020,   # ER leak channel conductance\n","            'kserca': 0.1,  # SERCA reaction constant\n","            'kip3': 0.15,  # ip3 reaction constant\n","            'kact': 0.4,  #\n","            'ip3rtau': 2000,  # ip3 receptors time constant\n","            'fc': 0.8,  # fraction of cytosol\n","            'fe': 0.2,  # fraction of ER\n","            'margin': 20}  # extracellular volume additional margin \n","\n","netParams.rxdParams['constants'] = constants\n","\n","### regions\n","regions = {}\n","regions['cyt'] = {'cells': 'all', 'secs': 'all', 'nrn_region': 'i', 'geometry': {'class': 'FractionalVolume', 'args': {'volume_fraction': constants['fc'], 'surface_fraction': 1}}}\n","regions['er'] = {'cells': 'all', 'secs': 'all', 'geometry': {'class': 'FractionalVolume', 'args': {'volume_fraction': constants['fe']}}}\n","regions['cyt_er_membrane'] = {'cells': 'all', 'secs': 'all', 'geometry': {'class': 'ScalableBorder', 'args': {'scale': 1, 'on_cell_surface': False}}}\n","\n","margin = 20  # extracellular volume additional margin \n","x, y, z = [0-margin, 100+margin], [-500-margin, 0+margin], [0-margin, 100+margin]\n","regions['ecs'] = {'extracellular': True, 'xlo': x[0], 'ylo': y[0], 'zlo': z[0], 'xhi': x[1], 'yhi': y[1], 'zhi': z[1], 'dx': 5, 'volume_fraction': 0.2, 'tortuosity': 1.6} \n","\n","netParams.rxdParams['regions'] = regions\n","\n","### species \n","species = {}\n","species['ca'] = {'regions': ['cyt', 'er', 'ecs'], 'd': constants['caDiff'], 'charge': 2,\n","                'initial': 'caco_init if isinstance(node,rxd.node.NodeExtracellular) else (0.0017 - caci_init * fc) / fe if node.region == er else caci_init'}\n","species['ip3'] = {'regions': ['cyt'], 'd': constants['ip3Diff'], 'initial': constants['ip3_init']}\n","netParams.rxdParams['species'] = species\n","\n","### states\n","netParams.rxdParams['states'] = {'ip3r_gate_state': {'regions': ['cyt_er_membrane'], 'initial': 0.8}}\n","\n","### reactions\n","minf = 'ip3[cyt] * 1000. * ca[cyt] / (ip3[cyt] + kip3) / (1000. * ca[cyt] + kact)'\n","h_gate = 'ip3r_gate_state[cyt_er_membrane]'\n","kip3 = 'gip3r * (%s * %s) ** 3' % (minf, h_gate)\n","\n","mcReactions = {}\n","mcReactions['serca'] = {'reactant': 'ca[cyt]', 'product': 'ca[er]', 'rate_f': 'gserca / ((kserca / (1000. * ca[cyt])) ** 2 + 1)', 'membrane': 'cyt_er_membrane', 'custom_dynamics': True}\n","mcReactions['leak'] = {'reactant': 'ca[er]', 'product': 'ca[cyt]', 'rate_f': constants['gleak'], 'rate_b': constants['gleak'], 'membrane': 'cyt_er_membrane'}\n","mcReactions['ip3r'] = {'reactant': 'ca[er]', 'product': 'ca[cyt]', 'rate_f': kip3, 'rate_b': kip3, 'membrane': 'cyt_er_membrane'}\n","netParams.rxdParams['multicompartmentReactions'] = mcReactions\n","\n","### rates\n","netParams.rxdParams['rates'] = {'ip3rg': {'species': h_gate, 'rate': '(1. / (1 + 1000. * ca[cyt] / (0.3)) - %s) / ip3rtau'%(h_gate)}}\n","\n","\n","\n","\n","#------------------------------------------------------------------------------\n","#\n","# SIMULATION CONFIGURATION\n","#\n","#------------------------------------------------------------------------------\n","\n","# Run parameters\n","simConfig = specs.SimConfig()       # object of class simConfig to store simulation configuration\n","simConfig.duration = 1.0*1e3        # Duration of the simulation, in ms\n","simConfig.hParams['v_init'] = -65   # set v_init to -65 mV\n","simConfig.dt = 0.1                  # Internal integration timestep to use\n","simConfig.verbose = False            # Show detailed messages \n","simConfig.recordStep = 1             # Step size in ms to save data (eg. V traces, LFP, etc)\n","simConfig.filename = 'rxd_net'   # Set file output name\n","\n","\n","# Recording/plotting parameters\n","simConfig.recordTraces = {'V_soma':{'sec': 'soma','loc': 0.5,'var': 'v'},\n","                          'ik_soma': {'sec': 'soma', 'loc': 0.5, 'var': 'ik'},\n","                          'cai_soma': {'sec': 'soma', 'loc':0.5, 'var': 'cai'},\n","                          'cao_soma': {'sec': 'soma', 'loc': 0.5, 'var': 'cao'}}\n","\n","simConfig.recordLFP = [[-15, y, 1.0*netParams.sizeZ] for y in range(int(netParams.sizeY/3), int(netParams.sizeY), int(netParams.sizeY/3))]\n","\n","#simConfig.analysis['iplotTraces'] ={'include': [0]}\n","simConfig.analysis['plotTraces'] = {'include': [('E2', 0), ('I2', 0), ('E4', 0), ('I4', 0), ('E5', 0), ('I5', 0)]}\n","\n","#simConfig.analysis['iplotRaster'] = {'orderBy': 'y', 'orderInverse': True, 'saveFig': True, 'figSize': (9,3)}      # Plot a raster\n","#simConfig.analysis['iplotLFP'] = {'includeAxon': False, 'figSize': (6,10), 'saveFig': True} \n","#simConfig.analysis['iplotRxDConcentration'] = {'speciesLabel': 'ca', 'regionLabel': 'ecs'}\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"grBSV-Q7B7m0"},"source":["Now we will run the simulation."]},{"cell_type":"code","metadata":{"id":"YxETBf6o4ZKo"},"source":["from netpyne import sim\n","sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"YdFBW3FLCLnN"},"source":["Now we can begin exploring the analyses available in NetPyNE.\n","\n","## Analyses in NetPyNE\n","\n","Let's take a look at the NetPyNE Package Index for analysis:\n","http://netpyne.org/netpyne.analysis.html#module-netpyne.analysis"]},{"cell_type":"code","metadata":{"id":"y1KUEz4k5WRv"},"source":["%matplotlib inline"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"HnfgLlZ-I1BR"},"source":["sa = sim.analysis"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"6tdR7PeEJ7IN","colab":{"base_uri":"https://localhost:8080/","height":732},"executionInfo":{"status":"ok","timestamp":1623997129802,"user_tz":-300,"elapsed":1699,"user":{"displayName":"Evgenia Karunus","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhA2ccNLhFnt0hUfl13RoguCJ6cDbCPCY8_SVjQAg=s64","userId":"04024508215281503990"}},"outputId":"e005a06b-63a0-4039-c4e0-1c89ea61f510"},"source":["sa.plot2Dnet();"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Plotting 2D representation of network cell locations and connections...\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 864x864 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"W8gBC8ctJ6-X"},"source":["sa.plot2Dnet(include=['E2', 'E4', 'E5']);"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"mCZYDT0vUYKO"},"source":["sa.plot2Dnet(view='xz');"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"vxUwdfpcKAJa"},"source":["sa.plotConn();"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"8VfaAnOpKGCL"},"source":["sa.plotConn(includePre=['E2', 'E4', 'E5'], includePost=['I2', 'I4', 'I5']);"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"iSs0pXZL8UHl"},"source":["sa.plotConn(feature='numConns');"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"7zkzJ67eJN5F"},"source":["sa.plotConn(groupBy='cell', feature='weight');"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"cMdkvqfsWG_7"},"source":["sa.plotConn(groupBy='cell', feature='weight', orderBy='y');"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"pMOGSbkVG74P"},"source":["sa.plotRateSpectrogram();"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"AitZZEXRG8rC"},"source":["sa.plotRateSpectrogram(include=['allCells']);"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"jjQvp_vjXJoY"},"source":["sa.plotRateSpectrogram(include=['allCells'], timeRange=[0, 400]);"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"KaKF75Z7IJW5"},"source":["sa.plotSpikeHist();"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ezoVkKHBIJZf"},"source":["sa.plotSpikeHist(binSize=20);"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZmBxr458X1MD"},"source":["sa.plotSpikeHist(binSize=20);"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"pSpumAJSX9TR"},"source":["sa.plotSpikeHist(binSize=20, measure='count');"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"xiOjsRCIIJd5"},"source":["sa.plotSpikeStats();"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"buJdXx4GIJmI"},"source":["foo = sa.plotSpikeStats();"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Rrg-G0OvYcDD"},"source":["fig, data = sa.plotSpikeStats();"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZmVP0umxZBfe"},"source":["!pip install pyspike"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"7-Fy9mfaIJol"},"source":["sa.plotTraces();"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"p0HU13QRIJq-"},"source":["sa.plotTraces(oneFigPer='trace');"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Mj7oWGhNawI7","colab":{"base_uri":"https://localhost:8080/","height":164},"executionInfo":{"status":"error","timestamp":1624138083622,"user_tz":240,"elapsed":155,"user":{"displayName":"Jessica Feldman","photoUrl":"","userId":"14873178662011488670"}},"outputId":"cdb86540-1f11-461a-abd3-6fe26ee857e3"},"source":["sa.plotTraces(oneFigPer='trace', overlay=True);"],"execution_count":1,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-1-2c1e1c3c5608>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotTraces\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moneFigPer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'trace'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverlay\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'sa' is not defined"]}]},{"cell_type":"code","metadata":{"id":"gjjzCJA2a_Do"},"source":["sa.plotTraces(oneFigPer='trace', overlay=True, axis=False);"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"RGPPfq8HbReU"},"source":["sa.plotRaster();"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"6aX4Bz16bojv"},"source":["sa.plotRaster(orderBy='y');"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"AXTW0XTab29b"},"source":["sa.plotRaster(orderInverse=True, popRates=True);"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"rb2422fscOKG"},"source":["sa.plotRaster(orderInverse=True, labels='overlay');"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"FR1lkfY9cc2V"},"source":["sa.plotRaster(orderInverse=True, spikeHist='subplot');"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"I84uEulgcrow"},"source":["sa.plotRaster(orderInverse=True, syncLines=True);"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"z-2N6_5ic7Q9"},"source":["sa.plotRaster(orderInverse=True, marker='o');"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"xv6wtVSXdJSq"},"source":["colors = {'E2': 'red'}"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Y0yZIQ3TdUZt"},"source":["colors['E4'] = 'pink'\n","colors['E5'] = 'orange'\n","colors['I2'] = 'blue'\n","colors['I4'] = 'purple'\n","colors['I5'] = 'black'"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"c2ePsdhYdpdC"},"source":["sa.plotRaster(orderInverse=True, marker='o', popColors=colors);"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"f8c0KO_SIJta"},"source":["sa.plotLFP();"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"hG07GrYWIJv3"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"UbnIkgDiIJyP"},"source":["sa.plotRxDConcentration(speciesLabel='ca', regionLabel='ecs');"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Z073XSVZIJ0Z"},"source":["for species in ['ca', 'ip3']:\n","    for region in ['cyt', 'er', 'cyt_er_membrane', 'ecs']:\n","        sa.plotRxDConcentration(speciesLabel=species, regionLabel=region);"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"TKRh_vTPIJ6v"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"a2WthFkxIJ9E"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"XD7E_q-eIJ_U"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"djzxMEVZIKBz"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Mmv5t60aIKEJ"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"zYG8SJH3IKGP"},"source":["sim.simData"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"4Ay4wx7zgeAf"},"source":[""],"execution_count":null,"outputs":[]}]}
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diff --git a/netpyne/tutorials/netpyne-course-2021/netpyne_batch_evol.ipynb b/netpyne/tutorials/netpyne-course-2021/netpyne_batch_evol.ipynb
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-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"netpyne_batch_evol.ipynb","provenance":[{"file_id":"1Vywfic5grokY-kOE4nQLtCFiw9diDGgR","timestamp":1621558581181},{"file_id":"1P9Y-rLqpKTP_cJZmWQY8qYsUMfNnju4N","timestamp":1621556006673},{"file_id":"1xcqB5I_iBlz3TNopuNERCJ1StlvZZJw5","timestamp":1621531137101},{"file_id":"19y6MLKhDAdBxLUZm2sHOuQx-5bqSODs-","timestamp":1621524871397}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"V0cRyhp8YWl2","executionInfo":{"status":"ok","timestamp":1621615830328,"user_tz":240,"elapsed":12187,"user":{"displayName":"Rammohan Shukla","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgfcDr2KHdiGwdZp-DCPyR3RvG0VfxRFktQ-0JlgQ=s64","userId":"13669639583442617912"}},"outputId":"0b9549cd-37b6-4903-bc6a-aecad807d762"},"source":["!pip install neuron\n","!pip install netpyne\n","!pip install inspyred\n","import matplotlib"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Collecting neuron\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/14/f4/ea50608c7633c286859d6cce0aad621da22a8da7ff9787efc8bb71fe0597/NEURON-8.0.0-cp37-cp37m-manylinux1_x86_64.whl (12.6MB)\n","\u001b[K     |████████████████████████████████| 12.6MB 16.3MB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.9.3 in /usr/local/lib/python3.7/dist-packages (from neuron) (1.19.5)\n","Installing collected packages: neuron\n","Successfully installed neuron-8.0.0\n","Collecting netpyne\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9e/24/0f9d685a3fbcbca0d86d9ca6521465c43725d9e23760c91524fe77191f12/netpyne-1.0.0.2-py2.py3-none-any.whl (312kB)\n","\u001b[K     |████████████████████████████████| 317kB 30.4MB/s \n","\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from netpyne) (1.4.1)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from netpyne) (3.2.2)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from netpyne) (1.19.5)\n","Collecting matplotlib-scalebar\n","  Downloading https://files.pythonhosted.org/packages/51/a4/cd254234c35f3591361988e89ab132ee14789f2ebe1ede621d63f5241f00/matplotlib_scalebar-0.7.2-py2.py3-none-any.whl\n","Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from netpyne) (1.1.5)\n","Requirement already satisfied: bokeh in /usr/local/lib/python3.7/dist-packages (from netpyne) (2.3.2)\n","Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from netpyne) (0.16.0)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->netpyne) (1.3.1)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->netpyne) (0.10.0)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->netpyne) (2.8.1)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->netpyne) (2.4.7)\n","Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->netpyne) (2018.9)\n","Requirement already satisfied: typing-extensions>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (3.7.4.3)\n","Requirement already satisfied: pillow>=7.1.0 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (7.1.2)\n","Requirement already satisfied: PyYAML>=3.10 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (3.13)\n","Requirement already satisfied: Jinja2>=2.9 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (2.11.3)\n","Requirement already satisfied: packaging>=16.8 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (20.9)\n","Requirement already satisfied: tornado>=5.1 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (5.1.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from cycler>=0.10->matplotlib->netpyne) (1.15.0)\n","Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from Jinja2>=2.9->bokeh->netpyne) (2.0.0)\n","Installing collected packages: matplotlib-scalebar, netpyne\n","Successfully installed matplotlib-scalebar-0.7.2 netpyne-1.0.0.2\n","Collecting inspyred\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ec/83/95dc9cc74d802e52b6f33d43de791dcfb376b187269757cf3c945ac7e0bb/inspyred-1.0.1-py2.py3-none-any.whl (88kB)\n","\u001b[K     |████████████████████████████████| 92kB 8.2MB/s \n","\u001b[?25hInstalling collected packages: inspyred\n","Successfully installed inspyred-1.0.1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"QzblvwEt9Ovm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621615833377,"user_tz":240,"elapsed":205,"user":{"displayName":"Rammohan Shukla","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgfcDr2KHdiGwdZp-DCPyR3RvG0VfxRFktQ-0JlgQ=s64","userId":"13669639583442617912"}},"outputId":"53bb3ac1-a128-40a6-a25c-d05037dd94b2"},"source":["rm -r netpyne-course-2021"],"execution_count":2,"outputs":[{"output_type":"stream","text":["rm: cannot remove 'netpyne-course-2021': No such file or directory\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2RjvKVYDyuXM","executionInfo":{"status":"ok","timestamp":1621615835288,"user_tz":240,"elapsed":796,"user":{"displayName":"Rammohan Shukla","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgfcDr2KHdiGwdZp-DCPyR3RvG0VfxRFktQ-0JlgQ=s64","userId":"13669639583442617912"}},"outputId":"7a689579-6381-4b97-91e4-fb31e3228bee"},"source":["!git clone --single-branch --branch evol https://github.com/suny-downstate-medical-center/netpyne-course-2021.git"],"execution_count":3,"outputs":[{"output_type":"stream","text":["Cloning into 'netpyne-course-2021'...\n","remote: Enumerating objects: 75, done.\u001b[K\n","remote: Counting objects: 100% (75/75), done.\u001b[K\n","remote: Compressing objects: 100% (60/60), done.\u001b[K\n","remote: Total 75 (delta 14), reused 67 (delta 12), pack-reused 0\u001b[K\n","Unpacking objects: 100% (75/75), done.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xAt-SfNS8rQt","executionInfo":{"status":"ok","timestamp":1621615838144,"user_tz":240,"elapsed":99,"user":{"displayName":"Rammohan Shukla","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgfcDr2KHdiGwdZp-DCPyR3RvG0VfxRFktQ-0JlgQ=s64","userId":"13669639583442617912"}},"outputId":"8f15b253-0cdf-4a4d-add1-1e5666938673"},"source":["cd netpyne-course-2021"],"execution_count":4,"outputs":[{"output_type":"stream","text":["/content/netpyne-course-2021\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"v8BUc8UtTxI2","executionInfo":{"status":"ok","timestamp":1621615839665,"user_tz":240,"elapsed":168,"user":{"displayName":"Rammohan Shukla","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgfcDr2KHdiGwdZp-DCPyR3RvG0VfxRFktQ-0JlgQ=s64","userId":"13669639583442617912"}},"outputId":"804d5188-e5f4-43ef-de55-481f75fa5030"},"source":["ls"],"execution_count":5,"outputs":[{"output_type":"stream","text":["batchRun.py  cfg.py  init.py  netParams.py\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"f0P--qg5YUT6","executionInfo":{"status":"error","timestamp":1621615972335,"user_tz":240,"elapsed":131343,"user":{"displayName":"Rammohan Shukla","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgfcDr2KHdiGwdZp-DCPyR3RvG0VfxRFktQ-0JlgQ=s64","userId":"13669639583442617912"}},"outputId":"e63a68dd-05ae-4c36-f46a-eaf7b6550e02"},"source":["from netpyne import specs, sim\n","%matplotlib inline\n","from netpyne import specs\n","from netpyne.batch import Batch\n","\n","''' Example of evolutionary algorithm optimization of a network using NetPyNE\n","2 examples are provided: 'simple' and 'complex'\n","In 'simple', 3 parameters are optimized to match target firing rates in 2 populations\n","In 'complex', 6 parameters are optimized to match target firing rates in 6 populations\n","\n","To run use: mpiexec -np [num_cores] nrniv -mpi batchRun.py\n","'''\n","\n","def batchEvol(networkType):\n","\t# parameters space to explore\n","\n","\tif networkType == 'simple':\n","\t\t## simple net\n","\t\tparams = specs.ODict()\n","\t\tparams['prob'] = [0.01, 0.5]\n","\t\tparams['weight'] = [0.001, 0.1]\n","\t\tparams['delay'] = [1, 20]\n","\n","\t\tpops = {}\n","\t\tpops['S'] = {'target': 5, 'width': 2, 'min': 2}\n","\t\tpops['M'] = {'target': 15, 'width': 2, 'min': 0.2}\n","\n","\telif networkType == 'complex':\n","\t\t# complex net\n","\t\tparams = specs.ODict()\n","\t\tparams['probEall'] = [0.05, 0.2] # 0.1\n","\t\tparams['weightEall'] = [0.0025, 0.0075] #5.0\n","\t\tparams['probIE'] = [0.2, 0.6] #0.4\n","\t\tparams['weightIE'] = [0.0005, 0.002]\n","\t\tparams['probLengthConst'] = [100,200]\n","\t\tparams['stimWeight'] = [0.05, 0.2]\n","\n","\t\tpops = {}\n","\t\tpops['E2'] = {'target': 5, 'width': 2, 'min': 1}\n","\t\tpops['I2'] = {'target': 10, 'width': 5, 'min': 2}\n","\t\tpops['E4'] = {'target': 30, 'width': 10, 'min': 1}\n","\t\tpops['I4'] = {'target': 10, 'width': 3, 'min': 2}\n","\t\tpops['E5'] = {'target': 40, 'width': 4, 'min': 1}\n","\t\tpops['I5'] = {'target': 25, 'width': 5, 'min': 2}\n","\n","\t# fitness function\n","\tfitnessFuncArgs = {}\n","\tfitnessFuncArgs['pops'] = pops\n","\tfitnessFuncArgs['maxFitness'] = 1000\n","\n","\tdef fitnessFunc(simData, **kwargs):\n","\t\timport numpy as np\n","\t\tpops = kwargs['pops']\n","\t\tmaxFitness = kwargs['maxFitness']\n","\t\tpopFitness = [None for i in pops.items()]\n","\t\tpopFitness = [min(np.exp(  abs(v['target'] - simData['popRates'][k])  /  v['width']), maxFitness)\n","\t\t\t\tif simData[\"popRates\"][k]>v['min'] else maxFitness for k,v in pops.items()]\n","\t\tfitness = np.mean(popFitness)\n","\t\tpopInfo = '; '.join(['%s rate=%.1f fit=%1.f'%(p,r,f) for p,r,f in zip(list(simData['popRates'].keys()), list(simData['popRates'].values()), popFitness)])\n","\t\tprint('  '+popInfo)\n","\t\treturn fitness\n","\n","\t# create Batch object with paramaters to modify, and specifying files to use\n","\tb = Batch(params=params)\n","\n","\t# Set output folder, grid method (all param combinations), and run configuration\n","\tb.batchLabel = 'simple'\n","\tb.saveFolder = './'+b.batchLabel\n","\tb.method = 'evol'\n","\tb.runCfg = {\n","\t\t'type': 'mpi_bulletin',#'hpc_slurm',\n","\t\t'script': 'init.py',\n","\t\t# options required only for hpc\n","\t\t'mpiCommand': 'mpirun',\n","\t\t'nodes': 1,\n","\t\t'coresPerNode': 2,\n","\t\t'allocation': 'default',\n","\t\t'email': 'salvadordura@gmail.com',\n","\t\t'reservation': None,\n","\t\t'folder': '/home/salvadord/evol'\n","\t\t#'custom': 'export LD_LIBRARY_PATH=\"$HOME/.openmpi/lib\"' # only for conda users\n","\t}\n","\tb.evolCfg = {\n","\t\t'evolAlgorithm': 'custom',\n","\t\t'fitnessFunc': fitnessFunc, # fitness expression (should read simData)\n","\t\t'fitnessFuncArgs': fitnessFuncArgs,\n","\t\t'pop_size': 6,\n","\t\t'num_elites': 1, # keep this number of parents for next generation if they are fitter than children\n","\t\t'mutation_rate': 0.4,\n","\t\t'crossover': 0.5,\n","\t\t'maximize': False, # maximize fitness function?\n","\t\t'max_generations': 4,\n","\t\t'time_sleep': 5, # wait this time before checking again if sim is completed (for each generation)\n","\t\t'maxiter_wait': 40, # max number of times to check if sim is completed (for each generation)\n","\t\t'defaultFitness': 1000 # set fitness value in case simulation time is over\n","\t}\n","\t# Run batch simulations\n","\tb.run()\n","\n","# Main code\n","if __name__ == '__main__':\n","\tbatchEvol('simple')  # 'simple' or 'complex'\n","\n"],"execution_count":6,"outputs":[{"output_type":"stream","text":["Saving batch to ./simple/simple_batch.json ... \n","set prob=0.2244271249493722\n","set weight=0.013930141727217026\n","set delay=17.83365291678021\n","Saving simConfig to ./simple/gen_0/gen_0_cand_0_cfg.json ... \n","--------------------------------------------------------------------------------\n","set prob=0.1960434257742902\n","set weight=0.08734609303201052\n","set delay=12.90449993227687\n","Saving simConfig to ./simple/gen_0/gen_0_cand_1_cfg.json ... \n","--------------------------------------------------------------------------------\n","set prob=0.4277880653478038\n","set weight=0.07278293330863483\n","set delay=14.750730558574281\n","Saving simConfig to ./simple/gen_0/gen_0_cand_2_cfg.json ... \n","--------------------------------------------------------------------------------\n","set prob=0.41773078005213476\n","set weight=0.05907113433018287\n","set delay=1.5125700770149608\n","Saving simConfig to ./simple/gen_0/gen_0_cand_3_cfg.json ... \n","--------------------------------------------------------------------------------\n","set prob=0.18154745983852852\n","set weight=0.002668172646550387\n","set delay=9.909359199096265\n","Saving simConfig to ./simple/gen_0/gen_0_cand_4_cfg.json ... \n","--------------------------------------------------------------------------------\n","set prob=0.2073167290592023\n","set weight=0.0344440092848988\n","set delay=5.614112869941837\n","Saving simConfig to ./simple/gen_0/gen_0_cand_5_cfg.json ... \n","--------------------------------------------------------------------------------\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_0/gen_0_cand_0_cfg.json netParams=./simple/simple_netParams.py\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_0/gen_0_cand_1_cfg.json netParams=./simple/simple_netParams.py\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_0/gen_0_cand_2_cfg.json netParams=./simple/simple_netParams.py\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_0/gen_0_cand_3_cfg.json netParams=./simple/simple_netParams.py\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_0/gen_0_cand_4_cfg.json netParams=./simple/simple_netParams.py\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_0/gen_0_cand_5_cfg.json netParams=./simple/simple_netParams.py\n","Waiting for jobs from generation 0/4 ...\n","completed: 0\n","  M rate=31.8 fit=12; S rate=10.0 fit=1000\n","  Candidate 0 fitness = 506.2\n","completed: 1\n","  M rate=24.4 fit=12; S rate=10.0 fit=108\n","  Candidate 1 fitness = 60.3\n","  M rate=23.1 fit=12; S rate=10.0 fit=58\n","  Candidate 2 fitness = 35.4\n","completed: 3\n","  M rate=26.3 fit=12; S rate=10.0 fit=284\n","  Candidate 3 fitness = 148.3\n","  M rate=10.2 fit=12; S rate=10.0 fit=11\n","  Candidate 4 fitness = 11.7\n","  M rate=29.1 fit=12; S rate=10.0 fit=1000\n","  Candidate 5 fitness = 506.2\n","completed: 6\n","--------------------------------------------------------------------------------\n","  Completed a generation  \n","--------------------------------------------------------------------------------\n","Generation Evaluation      Worst       Best     Median    Average    Std Dev\n","---------- ---------- ---------- ---------- ---------- ---------- ----------\n","         0          6 506.193618 11.7052066 104.298961 211.344172 212.716494\n","\n","set prob=0.287899967405212\n","set weight=0.015472820135311635\n","set delay=18.075314796498777\n","Saving simConfig to ./simple/gen_1/gen_1_cand_0_cfg.json ... \n","--------------------------------------------------------------------------------\n","set prob=0.3929070137156907\n","set weight=0.0664111990292724\n","set delay=17.027649574674808\n","Saving simConfig to ./simple/gen_1/gen_1_cand_1_cfg.json ... \n","--------------------------------------------------------------------------------\n","set prob=0.35050870545413915\n","set weight=0.09732126292673299\n","set delay=3.402954816478534\n","Saving simConfig to ./simple/gen_1/gen_1_cand_2_cfg.json ... \n","--------------------------------------------------------------------------------\n","set prob=0.09724497431133669\n","set weight=0.0020326385630046375\n","set delay=12.5654716782918\n","Saving simConfig to ./simple/gen_1/gen_1_cand_3_cfg.json ... \n","--------------------------------------------------------------------------------\n","set prob=0.040221531991434106\n","set weight=0.07922801233317922\n","set delay=11.190805401895718\n","Saving simConfig to ./simple/gen_1/gen_1_cand_4_cfg.json ... \n","--------------------------------------------------------------------------------\n","set prob=0.21928904275003677\n","set weight=0.06781090731998043\n","set delay=4.5063658916933935\n","Saving simConfig to ./simple/gen_1/gen_1_cand_5_cfg.json ... \n","--------------------------------------------------------------------------------\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_1/gen_1_cand_0_cfg.json netParams=./simple/simple_netParams.py\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_1/gen_1_cand_1_cfg.json netParams=./simple/simple_netParams.py\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_1/gen_1_cand_2_cfg.json netParams=./simple/simple_netParams.py\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_1/gen_1_cand_3_cfg.json netParams=./simple/simple_netParams.py\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_1/gen_1_cand_4_cfg.json netParams=./simple/simple_netParams.py\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_1/gen_1_cand_5_cfg.json netParams=./simple/simple_netParams.py\n","Waiting for jobs from generation 1/4 ...\n","completed: 0\n","  M rate=34.8 fit=12; S rate=10.0 fit=1000\n","  Candidate 0 fitness = 506.2\n","completed: 1\n","  M rate=25.3 fit=12; S rate=10.0 fit=172\n","  Candidate 1 fitness = 92.4\n","  M rate=22.6 fit=12; S rate=10.0 fit=44\n","  Candidate 2 fitness = 28.2\n","  M rate=10.2 fit=12; S rate=10.0 fit=11\n","  Candidate 3 fitness = 11.8\n","  M rate=13.8 fit=12; S rate=10.0 fit=2\n","  Candidate 4 fitness = 7.1\n","completed: 5\n","  M rate=26.2 fit=12; S rate=10.0 fit=275\n","  Candidate 5 fitness = 143.7\n","completed: 6\n","--------------------------------------------------------------------------------\n","  Completed a generation  \n","--------------------------------------------------------------------------------\n","Generation Evaluation      Worst       Best     Median    Average    Std Dev\n","---------- ---------- ---------- ---------- ---------- ---------- ----------\n","         1         12 143.679259 7.10467788 19.9863120 49.1451887 51.3358528\n","\n","set prob=0.468273218768516\n","set weight=0.0017163957883616341\n","set delay=2.939118224486556\n","Saving simConfig to ./simple/gen_2/gen_2_cand_0_cfg.json ... \n","--------------------------------------------------------------------------------\n","set prob=0.3397675066035317\n","set weight=0.0400303642590575\n","set delay=13.754017241499477\n","Saving simConfig to ./simple/gen_2/gen_2_cand_1_cfg.json ... \n","--------------------------------------------------------------------------------\n","set prob=0.22922723248263058\n","set weight=0.0025941538440594747\n","set delay=2.244601792513973\n","Saving simConfig to ./simple/gen_2/gen_2_cand_2_cfg.json ... \n","--------------------------------------------------------------------------------\n","set prob=0.024839554383897594\n","set weight=0.07973513765994793\n","set delay=11.233656598424304\n","Saving simConfig to ./simple/gen_2/gen_2_cand_3_cfg.json ... \n","--------------------------------------------------------------------------------\n","set prob=0.24428682786088646\n","set weight=0.09119821492836394\n","set delay=16.274148196755288\n","Saving simConfig to ./simple/gen_2/gen_2_cand_4_cfg.json ... \n","--------------------------------------------------------------------------------\n","set prob=0.03957198647913131\n","set weight=0.0021578934775137704\n","set delay=12.12091212103526\n","Saving simConfig to ./simple/gen_2/gen_2_cand_5_cfg.json ... \n","--------------------------------------------------------------------------------\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_2/gen_2_cand_0_cfg.json netParams=./simple/simple_netParams.py\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_2/gen_2_cand_1_cfg.json netParams=./simple/simple_netParams.py\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_2/gen_2_cand_2_cfg.json netParams=./simple/simple_netParams.py\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_2/gen_2_cand_3_cfg.json netParams=./simple/simple_netParams.py\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_2/gen_2_cand_4_cfg.json netParams=./simple/simple_netParams.py\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_2/gen_2_cand_5_cfg.json netParams=./simple/simple_netParams.py\n","Waiting for jobs from generation 2/4 ...\n","completed: 0\n","  M rate=10.2 fit=12; S rate=10.0 fit=11\n","  Candidate 0 fitness = 11.8\n","completed: 1\n","  M rate=31.3 fit=12; S rate=10.0 fit=1000\n","  Candidate 1 fitness = 506.2\n","  M rate=10.2 fit=12; S rate=10.0 fit=11\n","  Candidate 2 fitness = 11.7\n","  M rate=13.7 fit=12; S rate=10.0 fit=2\n","  Candidate 3 fitness = 7.2\n","  M rate=10.2 fit=12; S rate=10.0 fit=11\n","  Candidate 5 fitness = 11.8\n","completed: 5\n","  M rate=24.5 fit=12; S rate=10.0 fit=118\n","  Candidate 4 fitness = 65.0\n","completed: 6\n","--------------------------------------------------------------------------------\n","  Completed a generation  \n","--------------------------------------------------------------------------------\n","Generation Evaluation      Worst       Best     Median    Average    Std Dev\n","---------- ---------- ---------- ---------- ---------- ---------- ----------\n","         2         18 64.9570345 7.10467788 11.7515214 19.0883461 20.6174102\n","\n","set prob=0.20593702294872684\n","set weight=0.06048357830473081\n","set delay=12.72166195784245\n","Saving simConfig to ./simple/gen_3/gen_3_cand_0_cfg.json ... \n","--------------------------------------------------------------------------------\n","set prob=0.024873364743508222\n","set weight=0.08575282629634727\n","set delay=1.3217663150914385\n","Saving simConfig to ./simple/gen_3/gen_3_cand_1_cfg.json ... \n","--------------------------------------------------------------------------------\n","set prob=0.03262520964768452\n","set weight=0.0804868956309073\n","set delay=5.759791310706834\n","Saving simConfig to ./simple/gen_3/gen_3_cand_2_cfg.json ... \n","--------------------------------------------------------------------------------\n","set prob=0.02110118122544956\n","set weight=0.02220384127603675\n","set delay=13.31850149309247\n","Saving simConfig to ./simple/gen_3/gen_3_cand_3_cfg.json ... \n","--------------------------------------------------------------------------------\n","set prob=0.3768443841464425\n","set weight=0.08964762947828141\n","set delay=8.459168667448736\n","Saving simConfig to ./simple/gen_3/gen_3_cand_4_cfg.json ... \n","--------------------------------------------------------------------------------\n","set prob=0.4879817213462239\n","set weight=0.0016524106420045545\n","set delay=2.376752603889159\n","Saving simConfig to ./simple/gen_3/gen_3_cand_5_cfg.json ... \n","--------------------------------------------------------------------------------\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_3/gen_3_cand_0_cfg.json netParams=./simple/simple_netParams.py\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_3/gen_3_cand_1_cfg.json netParams=./simple/simple_netParams.py\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_3/gen_3_cand_2_cfg.json netParams=./simple/simple_netParams.py\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_3/gen_3_cand_3_cfg.json netParams=./simple/simple_netParams.py\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_3/gen_3_cand_4_cfg.json netParams=./simple/simple_netParams.py\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_3/gen_3_cand_5_cfg.json netParams=./simple/simple_netParams.py\n","Waiting for jobs from generation 3/4 ...\n","completed: 0\n","  M rate=26.1 fit=12; S rate=10.0 fit=253\n","  Candidate 0 fitness = 132.7\n","  M rate=13.6 fit=12; S rate=10.0 fit=2\n","  Candidate 1 fitness = 7.2\n","completed: 2\n","  M rate=13.7 fit=12; S rate=10.0 fit=2\n","  Candidate 2 fitness = 7.2\n","  M rate=13.4 fit=12; S rate=10.0 fit=2\n","  Candidate 3 fitness = 7.3\n","  M rate=22.7 fit=12; S rate=10.0 fit=46\n","  Candidate 4 fitness = 29.3\n","  M rate=10.2 fit=12; S rate=10.0 fit=11\n","  Candidate 5 fitness = 11.8\n","completed: 6\n","--------------------------------------------------------------------------------\n","  Completed a generation  \n","--------------------------------------------------------------------------------\n","Generation Evaluation      Worst       Best     Median    Average    Std Dev\n","---------- ---------- ---------- ---------- ---------- ---------- ----------\n","         3         24 29.3017865 7.10467788 7.23592455 11.6406058 8.07602761\n","\n","set prob=0.24003093274441611\n","set weight=0.048934252805191945\n","set delay=15.459537249847216\n","Saving simConfig to ./simple/gen_4/gen_4_cand_0_cfg.json ... \n","--------------------------------------------------------------------------------\n","set prob=0.019944293841170125\n","set weight=0.03051155927466046\n","set delay=13.917850068286576\n","Saving simConfig to ./simple/gen_4/gen_4_cand_1_cfg.json ... \n","--------------------------------------------------------------------------------\n","set prob=0.012536765349865737\n","set weight=0.030098568068958192\n","set delay=11.884955355499677\n","Saving simConfig to ./simple/gen_4/gen_4_cand_2_cfg.json ... \n","--------------------------------------------------------------------------------\n","set prob=0.23670938404905922\n","set weight=0.09968584174701281\n","set delay=1.0630470612079888\n","Saving simConfig to ./simple/gen_4/gen_4_cand_3_cfg.json ... \n","--------------------------------------------------------------------------------\n","set prob=0.034207941618807365\n","set weight=0.001062692914686697\n","set delay=9.354257828941575\n","Saving simConfig to ./simple/gen_4/gen_4_cand_4_cfg.json ... \n","--------------------------------------------------------------------------------\n","set prob=0.19017208717751283\n","set weight=0.08920380547505279\n","set delay=16.239261165938892\n","Saving simConfig to ./simple/gen_4/gen_4_cand_5_cfg.json ... \n","--------------------------------------------------------------------------------\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_4/gen_4_cand_0_cfg.json netParams=./simple/simple_netParams.py\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_4/gen_4_cand_1_cfg.json netParams=./simple/simple_netParams.py\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_4/gen_4_cand_2_cfg.json netParams=./simple/simple_netParams.py\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_4/gen_4_cand_3_cfg.json netParams=./simple/simple_netParams.py\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_4/gen_4_cand_4_cfg.json netParams=./simple/simple_netParams.py\n","\n","Job in rank id:  0\n","nrniv init.py simConfig=./simple/gen_4/gen_4_cand_5_cfg.json netParams=./simple/simple_netParams.py\n","Waiting for jobs from generation 4/4 ...\n","completed: 0\n","  M rate=27.8 fit=12; S rate=10.0 fit=592\n","  Candidate 0 fitness = 302.1\n","  M rate=13.4 fit=12; S rate=10.0 fit=2\n","  Candidate 1 fitness = 7.3\n","completed: 2\n","  M rate=12.3 fit=12; S rate=10.0 fit=4\n","  Candidate 2 fitness = 8.2\n","  M rate=23.8 fit=12; S rate=10.0 fit=81\n","  Candidate 3 fitness = 46.9\n","  M rate=10.2 fit=12; S rate=10.0 fit=11\n","  Candidate 4 fitness = 11.8\n","  M rate=23.8 fit=12; S rate=10.0 fit=81\n","  Candidate 5 fitness = 46.9\n","completed: 6\n","--------------------------------------------------------------------------------\n","  Completed a generation  \n","--------------------------------------------------------------------------------\n","Generation Evaluation      Worst       Best     Median    Average    Std Dev\n","---------- ---------- ---------- ---------- ---------- ---------- ----------\n","         4         30 46.9190528 7.10467788 9.97629104 21.3669590 18.1340470\n","\n","Best Solution: \n","[0.040221531991434106, 0.07922801233317922, 11.190805401895718] : 7.1046778897630976\n","--------------------------------------------------------------------------------\n","   Completed evolutionary algorithm parameter optimization   \n","--------------------------------------------------------------------------------\n"],"name":"stdout"},{"output_type":"error","ename":"SystemExit","evalue":"ignored","traceback":["An exception has occurred, use %tb to see the full traceback.\n","\u001b[0;31mSystemExit\u001b[0m\n"]}]},{"cell_type":"code","metadata":{"id":"aij9ZSq0UnLd"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/netpyne/tutorials/netpyne-course-2021/netpyne_batch_tut8.ipynb b/netpyne/tutorials/netpyne-course-2021/netpyne_batch_tut8.ipynb
deleted file mode 100644
index 8476a7f96..000000000
--- a/netpyne/tutorials/netpyne-course-2021/netpyne_batch_tut8.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"netpyne_batch_tut8.ipynb","provenance":[{"file_id":"1P9Y-rLqpKTP_cJZmWQY8qYsUMfNnju4N","timestamp":1621556006673},{"file_id":"1xcqB5I_iBlz3TNopuNERCJ1StlvZZJw5","timestamp":1621531137101},{"file_id":"19y6MLKhDAdBxLUZm2sHOuQx-5bqSODs-","timestamp":1621524871397}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"V0cRyhp8YWl2","executionInfo":{"status":"ok","timestamp":1622453709042,"user_tz":-480,"elapsed":8462,"user":{"displayName":"Samuel Bolland","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjhWpP7mf88kT980MPiR0spUh3By9DWIm5EYVUoaA=s64","userId":"06928499349223853710"}},"outputId":"2a9475f7-d791-450b-9dea-f7935828a312"},"source":["!pip install neuron\n","!pip install netpyne\n","import matplotlib"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting neuron\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/14/f4/ea50608c7633c286859d6cce0aad621da22a8da7ff9787efc8bb71fe0597/NEURON-8.0.0-cp37-cp37m-manylinux1_x86_64.whl (12.6MB)\n","\u001b[K     |████████████████████████████████| 12.6MB 9.5MB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.9.3 in /usr/local/lib/python3.7/dist-packages (from neuron) (1.19.5)\n","Installing collected packages: neuron\n","Successfully installed neuron-8.0.0\n","Collecting netpyne\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9e/24/0f9d685a3fbcbca0d86d9ca6521465c43725d9e23760c91524fe77191f12/netpyne-1.0.0.2-py2.py3-none-any.whl (312kB)\n","\u001b[K     |████████████████████████████████| 317kB 15.9MB/s \n","\u001b[?25hCollecting matplotlib-scalebar\n","  Downloading https://files.pythonhosted.org/packages/51/a4/cd254234c35f3591361988e89ab132ee14789f2ebe1ede621d63f5241f00/matplotlib_scalebar-0.7.2-py2.py3-none-any.whl\n","Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from netpyne) (1.4.1)\n","Requirement already satisfied: bokeh in /usr/local/lib/python3.7/dist-packages (from netpyne) (2.3.2)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from netpyne) (3.2.2)\n","Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from netpyne) (0.16.0)\n","Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from netpyne) (1.1.5)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from netpyne) (1.19.5)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (2.8.1)\n","Requirement already satisfied: typing-extensions>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (3.7.4.3)\n","Requirement already satisfied: tornado>=5.1 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (5.1.1)\n","Requirement already satisfied: Jinja2>=2.9 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (2.11.3)\n","Requirement already satisfied: pillow>=7.1.0 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (7.1.2)\n","Requirement already satisfied: PyYAML>=3.10 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (3.13)\n","Requirement already satisfied: packaging>=16.8 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (20.9)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->netpyne) (2.4.7)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->netpyne) (1.3.1)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->netpyne) (0.10.0)\n","Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->netpyne) (2018.9)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->bokeh->netpyne) (1.15.0)\n","Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from Jinja2>=2.9->bokeh->netpyne) (2.0.1)\n","Installing collected packages: matplotlib-scalebar, netpyne\n","Successfully installed matplotlib-scalebar-0.7.2 netpyne-1.0.0.2\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"JWcri3tKdzkW"},"source":[""]},{"cell_type":"markdown","metadata":{"id":"w4d-nZ3WuXxK"},"source":["# New Section"]},{"cell_type":"code","metadata":{"id":"QzblvwEt9Ovm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1622453709043,"user_tz":-480,"elapsed":7,"user":{"displayName":"Samuel Bolland","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjhWpP7mf88kT980MPiR0spUh3By9DWIm5EYVUoaA=s64","userId":"06928499349223853710"}},"outputId":"08058c2a-9a36-4d16-a311-ba586a125efb"},"source":["rm -r netpyne-course-2021"],"execution_count":null,"outputs":[{"output_type":"stream","text":["rm: cannot remove 'netpyne-course-2021': No such file or directory\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2RjvKVYDyuXM","executionInfo":{"status":"ok","timestamp":1622453711673,"user_tz":-480,"elapsed":903,"user":{"displayName":"Samuel Bolland","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjhWpP7mf88kT980MPiR0spUh3By9DWIm5EYVUoaA=s64","userId":"06928499349223853710"}},"outputId":"fac2a104-cb4d-4ce8-b2db-d266d1f35a31"},"source":["!git clone --single-branch --branch batch https://github.com/suny-downstate-medical-center/netpyne-course-2021.git"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Cloning into 'netpyne-course-2021'...\n","remote: Enumerating objects: 68, done.\u001b[K\n","remote: Counting objects: 100% (68/68), done.\u001b[K\n","remote: Compressing objects: 100% (53/53), done.\u001b[K\n","remote: Total 68 (delta 12), reused 62 (delta 12), pack-reused 0\u001b[K\n","Unpacking objects: 100% (68/68), done.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xAt-SfNS8rQt","executionInfo":{"status":"ok","timestamp":1622453714013,"user_tz":-480,"elapsed":550,"user":{"displayName":"Samuel Bolland","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjhWpP7mf88kT980MPiR0spUh3By9DWIm5EYVUoaA=s64","userId":"06928499349223853710"}},"outputId":"310bd66b-2a4b-47cc-a038-c8c7d58b1e98"},"source":["cd netpyne-course-2021"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/content/netpyne-course-2021\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"peCntM_qv1mh","executionInfo":{"status":"ok","timestamp":1622453715792,"user_tz":-480,"elapsed":5,"user":{"displayName":"Samuel Bolland","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjhWpP7mf88kT980MPiR0spUh3By9DWIm5EYVUoaA=s64","userId":"06928499349223853710"}},"outputId":"c6679746-f1c3-4aa6-bcf1-9cfde4d58549"},"source":["pwd"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'/content/netpyne-course-2021'"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"v8BUc8UtTxI2","executionInfo":{"status":"ok","timestamp":1622453717851,"user_tz":-480,"elapsed":550,"user":{"displayName":"Samuel Bolland","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjhWpP7mf88kT980MPiR0spUh3By9DWIm5EYVUoaA=s64","userId":"06928499349223853710"}},"outputId":"69fd7101-97f8-404d-f297-0a3877f6861d"},"source":["ls"],"execution_count":null,"outputs":[{"output_type":"stream","text":["tut8_analysis.py  tut8_batch.py  tut8_cfg.py  tut8_init.py  tut8_netParams.py\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"f0P--qg5YUT6","executionInfo":{"status":"ok","timestamp":1622453741538,"user_tz":-480,"elapsed":21953,"user":{"displayName":"Samuel Bolland","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjhWpP7mf88kT980MPiR0spUh3By9DWIm5EYVUoaA=s64","userId":"06928499349223853710"}},"outputId":"2b853a24-a62f-4826-b261-386654379f50"},"source":["%matplotlib inline\n","from netpyne import specs\n","from netpyne.batch import Batch\n","\n","# Create variable of type ordered dictionary (NetPyNE's customized version)\n","params = specs.ODict()\n","\n","# fill in with parameters to explore and range of values (key has to coincide with a variable in simConfig)\n","params['synMechTau2'] = [10, 25]\n","params['connWeight'] = [0.005, 0.01]\n","\n","# create Batch object with parameters to modify, and specifying files to use\n","b = Batch(params=params, cfgFile='tut8_cfg.py', netParamsFile='tut8_netParams.py',)\n","\n","# Set output folder, grid method (all param combinations), and run configuration\n","b.batchLabel = 'tauWeight'\n","b.saveFolder = 'tut8_data'\n","b.method = 'grid' \n","b.runCfg = {'type': 'mpi_bulletin',\n","                    'script': 'tut8_init.py',\n","                    'skip': True}\n","\n","# Run batch simulations\n","b.run()\n","\n","\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Saving batch to tut8_data/tauWeight_batch.json ... \n","(0, 0) (10, 0.005)\n","synMechTau2 = 10\n","connWeight = 0.005\n","Saving simConfig to tut8_data/tauWeight_0_0_cfg.json ... \n","Submitting job  tut8_data/tauWeight_0_0\n","(0, 1) (10, 0.01)\n","synMechTau2 = 10\n","connWeight = 0.01\n","Saving simConfig to tut8_data/tauWeight_0_1_cfg.json ... \n","Submitting job  tut8_data/tauWeight_0_1\n","(1, 0) (25, 0.005)\n","synMechTau2 = 25\n","connWeight = 0.005\n","Saving simConfig to tut8_data/tauWeight_1_0_cfg.json ... \n","Submitting job  tut8_data/tauWeight_1_0\n","(1, 1) (25, 0.01)\n","synMechTau2 = 25\n","connWeight = 0.01\n","Saving simConfig to tut8_data/tauWeight_1_1_cfg.json ... \n","Submitting job  tut8_data/tauWeight_1_1\n","--------------------------------------------------------------------------------\n","   Finished submitting jobs for grid parameter exploration   \n","--------------------------------------------------------------------------------\n","\n","Job in rank id:  0\n","nrniv tut8_init.py simConfig=tut8_data/tauWeight_0_0_cfg.json netParams=tut8_data/tauWeight_netParams.py\n","\n","\n","Reading command line arguments using syntax: python file.py [simConfig=filepath] [netParams=filepath]\n","Loading file tut8_data/tauWeight_0_0_cfg.json ... \n","Loading simConfig...\n","Importing netParams from tut8_data/tauWeight_netParams.py\n","\n","Start time:  2021-05-31 09:35:25.742537\n","\n","Creating network of 2 cell populations on 1 hosts...\n","  Number of cells on node 0: 40 \n","  Done; cell creation time = 0.00 s.\n","Making connections...\n","  Number of connections on node 0: 203 \n","  Done; cell connection time = 0.02 s.\n","Adding stims...\n","  Number of stims on node 0: 40 \n","  Done; cell stims creation time = 0.00 s.\n","Recording 1 traces of 1 types on node 0\n","\n","Running simulation for 1000.0 ms...\n","  Done; run time = 0.76 s; real-time ratio: 1.32.\n","\n","Gathering data...\n","  Done; gather time = 0.01 s.\n","\n","Analyzing...\n","  Cells: 40\n","  Connections: 243 (6.08 per cell)\n","  Spikes: 625 (15.62 Hz)\n","  Simulated time: 1.0 s; 1 workers\n","  Run time: 0.76 s\n","   S : 10.200 Hz\n","   M : 21.050 Hz\n","Saving output as tut8_data/tauWeight_0_0.json  ... \n","Finished saving!\n","  Done; saving time = 0.04 s.\n","Plotting raster...\n","Plotting recorded cell traces ... cell\n","  Done; plotting time = 0.46 s\n","\n","Total time = 1.32 s\n","\n","End time:  2021-05-31 09:35:27.058482\n","first instance of v_init\n","first instance of tstop\n","\n","\n","Job in rank id:  0\n","nrniv tut8_init.py simConfig=tut8_data/tauWeight_0_1_cfg.json netParams=tut8_data/tauWeight_netParams.py\n","\n","\n","Reading command line arguments using syntax: python file.py [simConfig=filepath] [netParams=filepath]\n","Loading file tut8_data/tauWeight_0_1_cfg.json ... \n","Loading simConfig...\n","Importing netParams from tut8_data/tauWeight_netParams.py\n","\n","Start time:  2021-05-31 09:35:29.859321\n","\n","Creating network of 2 cell populations on 1 hosts...\n","  Number of cells on node 0: 40 \n","  Done; cell creation time = 0.00 s.\n","Making connections...\n","  Number of connections on node 0: 203 \n","  Done; cell connection time = 0.02 s.\n","Adding stims...\n","  Number of stims on node 0: 40 \n","  Done; cell stims creation time = 0.00 s.\n","Recording 1 traces of 1 types on node 0\n","\n","Running simulation for 1000.0 ms...\n","  Done; run time = 0.76 s; real-time ratio: 1.31.\n","\n","Gathering data...\n","  Done; gather time = 0.01 s.\n","\n","Analyzing...\n","  Cells: 40\n","  Connections: 243 (6.08 per cell)\n","  Spikes: 697 (17.43 Hz)\n","  Simulated time: 1.0 s; 1 workers\n","  Run time: 0.76 s\n","   S : 10.200 Hz\n","   M : 24.650 Hz\n","Saving output as tut8_data/tauWeight_0_1.json  ... \n","Finished saving!\n","  Done; saving time = 0.04 s.\n","Plotting raster...\n","Plotting recorded cell traces ... cell\n","  Done; plotting time = 0.43 s\n","\n","Total time = 1.28 s\n","\n","End time:  2021-05-31 09:35:31.142546\n","first instance of v_init\n","first instance of tstop\n","\n","\n","Job in rank id:  0\n","nrniv tut8_init.py simConfig=tut8_data/tauWeight_1_0_cfg.json netParams=tut8_data/tauWeight_netParams.py\n","\n","\n","Reading command line arguments using syntax: python file.py [simConfig=filepath] [netParams=filepath]\n","Loading file tut8_data/tauWeight_1_0_cfg.json ... \n","Loading simConfig...\n","Importing netParams from tut8_data/tauWeight_netParams.py\n","\n","Start time:  2021-05-31 09:35:33.934245\n","\n","Creating network of 2 cell populations on 1 hosts...\n","  Number of cells on node 0: 40 \n","  Done; cell creation time = 0.00 s.\n","Making connections...\n","  Number of connections on node 0: 203 \n","  Done; cell connection time = 0.02 s.\n","Adding stims...\n","  Number of stims on node 0: 40 \n","  Done; cell stims creation time = 0.00 s.\n","Recording 1 traces of 1 types on node 0\n","\n","Running simulation for 1000.0 ms...\n","  Done; run time = 0.75 s; real-time ratio: 1.33.\n","\n","Gathering data...\n","  Done; gather time = 0.01 s.\n","\n","Analyzing...\n","  Cells: 40\n","  Connections: 243 (6.08 per cell)\n","  Spikes: 307 (7.67 Hz)\n","  Simulated time: 1.0 s; 1 workers\n","  Run time: 0.75 s\n","   S : 10.200 Hz\n","   M : 5.150 Hz\n","Saving output as tut8_data/tauWeight_1_0.json  ... \n","Finished saving!\n","  Done; saving time = 0.04 s.\n","Plotting raster...\n","Plotting recorded cell traces ... cell\n","  Done; plotting time = 0.42 s\n","\n","Total time = 1.26 s\n","\n","End time:  2021-05-31 09:35:35.196970\n","first instance of v_init\n","first instance of tstop\n","\n","\n","Job in rank id:  0\n","nrniv tut8_init.py simConfig=tut8_data/tauWeight_1_1_cfg.json netParams=tut8_data/tauWeight_netParams.py\n","\n","\n","Reading command line arguments using syntax: python file.py [simConfig=filepath] [netParams=filepath]\n","Loading file tut8_data/tauWeight_1_1_cfg.json ... \n","Loading simConfig...\n","Importing netParams from tut8_data/tauWeight_netParams.py\n","\n","Start time:  2021-05-31 09:35:38.001805\n","\n","Creating network of 2 cell populations on 1 hosts...\n","  Number of cells on node 0: 40 \n","  Done; cell creation time = 0.00 s.\n","Making connections...\n","  Number of connections on node 0: 203 \n","  Done; cell connection time = 0.02 s.\n","Adding stims...\n","  Number of stims on node 0: 40 \n","  Done; cell stims creation time = 0.00 s.\n","Recording 1 traces of 1 types on node 0\n","\n","Running simulation for 1000.0 ms...\n","  Done; run time = 0.75 s; real-time ratio: 1.33.\n","\n","Gathering data...\n","  Done; gather time = 0.01 s.\n","\n","Analyzing...\n","  Cells: 40\n","  Connections: 243 (6.08 per cell)\n","  Spikes: 247 (6.17 Hz)\n","  Simulated time: 1.0 s; 1 workers\n","  Run time: 0.75 s\n","   S : 10.200 Hz\n","   M : 2.150 Hz\n","Saving output as tut8_data/tauWeight_1_1.json  ... \n","Finished saving!\n","  Done; saving time = 0.04 s.\n","Plotting raster...\n","Plotting recorded cell traces ... cell\n","  Done; plotting time = 0.43 s\n","\n","Total time = 1.27 s\n","\n","End time:  2021-05-31 09:35:39.266894\n","first instance of v_init\n","first instance of tstop\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"BR1mtK7kMbvF","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"error","timestamp":1622319663224,"user_tz":-180,"elapsed":2967,"user":{"displayName":"Michael Mazar","photoUrl":"","userId":"15788570157356013099"}},"outputId":"9d69f447-61d5-4ca0-bfb8-df6c06e13a65"},"source":["\"\"\"\n","analysis.py\n","\n","Functions to read and plot figures from the batch simulation results.\n","\"\"\"\n","\n","import json\n","import pandas as pd\n","import seaborn as sb\n","import matplotlib.pyplot as plt\n","import pickle\n","import numpy as np\n","from pylab import *\n","from itertools import product\n","from pprint import pprint\n","from netpyne import specs\n","from collections import OrderedDict\n","\n","\n","\n","#--------------------------------------------------------------------\n","# Function to read batch data\n","#--------------------------------------------------------------------\n","def readBatchData(dataFolder, batchLabel, loadAll=False, saveAll=True, vars=None, maxCombs=None, listCombs=None):\n","    # load from previously saved file with all data\n","    if loadAll:\n","        print('\\nLoading single file with all data...')\n","        filename = '%s/%s/%s_allData.json' % (dataFolder, batchLabel, batchLabel)\n","        with open(filename, 'r') as fileObj:\n","            dataLoad = json.load(fileObj, object_pairs_hook=OrderedDict)\n","        params = dataLoad['params']\n","        data = dataLoad['data']\n","        return params, data\n","\n","    if isinstance(listCombs, str):\n","        filename = str(listCombs)\n","        with open(filename, 'r') as fileObj:\n","            dataLoad = json.load(fileObj)\n","        listCombs = dataLoad['paramsMatch']\n","\n","    # read the batch file and cfg\n","    batchFile = '%s/%s_batch.json' % (dataFolder, batchLabel)\n","    with open(batchFile, 'r') as fileObj:\n","        b = json.load(fileObj)['batch']\n","\n","    # read params labels and ranges\n","    params = b['params']\n","\n","    # reorder so grouped params come first\n","    preorder = [p for p in params if 'group' in p and p['group']]\n","    for p in params:\n","        if p not in preorder: preorder.append(p)\n","    params = preorder\n","\n","    # read vars from all files - store in dict\n","    if b['method'] == 'grid':\n","        labelList, valuesList = list(zip(*[(p['label'], p['values']) for p in params]))\n","        valueCombinations = product(*(valuesList))\n","        indexCombinations = product(*[list(range(len(x))) for x in valuesList])\n","        data = {}\n","        print('Reading data...')\n","        missing = 0\n","        for i,(iComb, pComb) in enumerate(zip(indexCombinations, valueCombinations)):\n","            if (not maxCombs or i<= maxCombs) and (not listCombs or list(pComb) in listCombs):\n","                print(i, iComb)\n","                # read output file\n","                iCombStr = ''.join([''.join('_'+str(i)) for i in iComb])\n","                simLabel = b['batchLabel']+iCombStr\n","                outFile = b['saveFolder']+'/'+simLabel+'.json'\n","                try:\n","                    with open(outFile, 'r') as fileObj:\n","                        output = json.load(fileObj, object_pairs_hook=OrderedDict)\n","                    # save output file in data dict\n","                    data[iCombStr] = {}\n","                    data[iCombStr]['paramValues'] = pComb  # store param values\n","                    if not vars: vars = list(output.keys())\n","\n","                    for key in vars:\n","                        if isinstance(key, tuple):\n","                            container = output\n","                            for ikey in range(len(key)-1):\n","                                container = container[key[ikey]]\n","                            data[iCombStr][key[1]] = container[key[-1]]\n","\n","                        elif isinstance(key, str):\n","                            data[iCombStr][key] = output[key]\n","\n","                except:\n","                    print('... file missing')\n","                    missing = missing + 1\n","                    output = {}\n","            else:\n","                missing = missing + 1\n","\n","        print('%d files missing' % (missing))\n","\n","        # save\n","        if saveAll:\n","            print('Saving to single file with all data')\n","            filename = '%s/%s_allData.json' % (dataFolder, batchLabel)\n","            dataSave = {'params': params, 'data': data}\n","            with open(filename, 'w') as fileObj:\n","                json.dump(dataSave, fileObj)\n","\n","        return params, data\n","\n","#--------------------------------------------------------------------\n","# Function to convert data to Pandas\n","#--------------------------------------------------------------------\n","def toPandas(params, data):\n","    if 'simData' in data[list(data.keys())[0]]:\n","        rows = [list(d['paramValues'])+[s for s in list(d['simData'].values())] for d in list(data.values())]\n","        cols = [str(d['label']) for d in params]+[s for s in list(data[list(data.keys())[0]]['simData'].keys())]\n","    else:\n","        rows = [list(d['paramValues'])+[s for s in list(d.values())] for d in list(data.values())]\n","        cols = [str(d['label']) for d in params]+[s for s in list(data[list(data.keys())[0]].keys())]\n","\n","    df = pd.DataFrame(rows, columns=cols)\n","    df['simLabel'] = list(data.keys())\n","\n","    colRename=[]\n","    for col in list(df.columns):\n","        if col.startswith(\"[u'\"):\n","            colName = col.replace(\", u'\",\"_'\").replace(\"[u\",\"\").replace(\"'\",\"\").replace(\"]\",\"\").replace(\", \",\"_\")\n","            colRename.append(colName)\n","        else:\n","            colRename.append(col)\n","    #print(colRename)\n","    df.columns = colRename\n","\n","    return df\n","\n","#--------------------------------------------------------------------\n","# Function to colors and style of figures\n","#--------------------------------------------------------------------\n","def setPlotFormat(numColors=8):\n","    plt.style.use('seaborn-whitegrid')\n","\n","    plt.rcParams['font.size'] = 12\n","    plt.rcParams['axes.titlesize'] = 14\n","    plt.rcParams['axes.labelsize'] = 12\n","    plt.rcParams['legend.fontsize'] = 'large'\n","\n","    NUM_COLORS = numColors\n","    colormap = plt.get_cmap('nipy_spectral')\n","    colorlist = [colormap(1.*i/NUM_COLORS) for i in range(NUM_COLORS)]\n","\n","    plt.rc('axes', prop_cycle=(cycler('color', colorlist)))\n","\n","\n","#--------------------------------------------------------------------\n","# Function to plot relation between parameters (tau2 and weight) and firing rate\n","#--------------------------------------------------------------------\n","def plot2DRate(dataFolder, batchLabel, params, data, par1, par2, val, valLabel, graphType='matrix', saveFile=None):\n","    df = toPandas(params, data)\n","    # dfpop = dfPopRates(df1, 7)\n","\n","    dfpop = df.iloc[:,0:5] # get param columns of all rows\n","    # dfpop['simLabel'] = df['simLabel']\n","    for k in list(df.popRates[0].keys()): dfpop[k] = [r[k] for r in df.popRates]\n","    #return dfpop\n","\n","    #print(dfpop)\n","    # if not valLabel: valLabel = val\n","    dfsubset = dfpop[[par1,par2,val]]\n","        # dfgroup = dfsubset.groupby(by=[par1,par2])\n","        # if groupStat=='first':\n","        #     dfgroup2 = dfgroup.first()\n","        # elif groupStat=='last':\n","        #     dfgroup2 = dfgroup.last()\n","        # elif groupStat=='mean':\n","        #     dfgroup2 = dfgroup.mean()\n","        # elif groupStat=='sum':\n","        #     dfgroup2 = dfgroup.sum()\n","        # dffinal = pd.DataFrame(dfgroup2).reset_index()\n","\n","    dfpiv = pd.pivot_table(dfsubset, index=par1, columns=par2, values=val)\n","#    pandas.pivot_table(df,values='count',index='site_id',columns='week')\n","    if graphType=='matrix':\n","        sb.heatmap(dfpiv, square=True, cbar_kws={'label': valLabel})\n","    elif graphType=='line':\n","        setPlotFormat(numColors = len(dfpiv.columns))\n","        #dfpiv = dfpiv[['IT2','IT4','IT5A','IT5B','PT5B','IT6','CT6']]\n","        dfpiv.plot(marker='o')\n","    try:\n","        if saveFile:\n","            plt.savefig(saveFile)\n","        else:\n","            plt.savefig(dataFolder+'/'+batchLabel+'_matrix_'+par1+'_'+par2+'_'+val+'.png')\n","    except:\n","        print('Error saving figure...')\n","\n","    plt.show()\n","\n","#--------------------------------------------------------------------\n","# Function to read batch data and plot figure\n","#--------------------------------------------------------------------\n","def readPlot():\n","    dataFolder = 'tut8_data/'\n","    batchLabel = 'tauWeight'\n","\n","    params, data = readBatchData(dataFolder, batchLabel, loadAll=0, saveAll=1, vars=None, maxCombs=None)\n","    plot2DRate(dataFolder, batchLabel, params, data, 'synMechTau2', 'connWeight', 'M', \"'M' pop rate (Hz)\")\n","\n","\n","# Main code\n","if __name__ == '__main__':\n","    readPlot()\n"],"execution_count":null,"outputs":[{"output_type":"error","ename":"FileNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-4-ff4b990fac7e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    206\u001b[0m \u001b[0;31m# Main code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'__main__'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m     \u001b[0mreadPlot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m<ipython-input-4-ff4b990fac7e>\u001b[0m in \u001b[0;36mreadPlot\u001b[0;34m()\u001b[0m\n\u001b[1;32m    200\u001b[0m     \u001b[0mbatchLabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'tauWeight'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 202\u001b[0;31m     \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreadBatchData\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataFolder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatchLabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloadAll\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaveAll\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvars\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxCombs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    203\u001b[0m     \u001b[0mplot2DRate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataFolder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatchLabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'synMechTau2'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'connWeight'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'M'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"'M' pop rate (Hz)\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m<ipython-input-4-ff4b990fac7e>\u001b[0m in \u001b[0;36mreadBatchData\u001b[0;34m(dataFolder, batchLabel, loadAll, saveAll, vars, maxCombs, listCombs)\u001b[0m\n\u001b[1;32m     41\u001b[0m     \u001b[0;31m# read the batch file and cfg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0mbatchFile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'%s/%s_batch.json'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdataFolder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatchLabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatchFile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfileObj\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m         \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileObj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'batch'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'tut8_data//tauWeight_batch.json'"]}]},{"cell_type":"code","metadata":{"id":"aij9ZSq0UnLd"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/netpyne/tutorials/netpyne-course-2021/tut_netpyne_artif_stim.ipynb b/netpyne/tutorials/netpyne-course-2021/tut_netpyne_artif_stim.ipynb
deleted file mode 100644
index f20470985..000000000
--- a/netpyne/tutorials/netpyne-course-2021/tut_netpyne_artif_stim.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"tut_netpyne_artif_stim.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python383jvsc74a57bd0926c0c110681b7c59a95dac5f69d49e264669da9dd5192e867629e0fc8ffa65a","display_name":"Python 3.8.3 64-bit ('base': conda)"},"language_info":{"name":"python","version":"3.8.3-final"}},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"V0cRyhp8YWl2","executionInfo":{"status":"ok","timestamp":1623827899285,"user_tz":-300,"elapsed":9039,"user":{"displayName":"Evgenia Karunus","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhA2ccNLhFnt0hUfl13RoguCJ6cDbCPCY8_SVjQAg=s64","userId":"04024508215281503990"}},"outputId":"c7fb2a1a-c3cc-4a4a-b6d2-4c767521f319"},"source":["!pip install neuron\n","!pip install netpyne\n","import matplotlib"],"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: neuron in /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages (7.8.1.1)\n","Requirement already satisfied: numpy>=1.9.3 in /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages (from neuron) (1.19.2)\n","\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.3 is available.\n","You should consider upgrading via the '/u/salvadord/venvs/neuron78/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n","Requirement already satisfied: netpyne in /Users/salvadord/Documents/ISB/Models/netpyne_repo (0.9.5)\n","Requirement already satisfied: numpy in /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages (from netpyne) (1.19.2)\n","Requirement already satisfied: scipy in /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages (from netpyne) (1.5.2)\n","Collecting matplotlib<=2.2.4,>2.2\n","  Downloading matplotlib-2.2.4.tar.gz (37.0 MB)\n","\u001b[K     |████████████████████████████████| 37.0 MB 6.8 MB/s \n","\u001b[?25h\u001b[31m    ERROR: Command errored out with exit status 1:\n","     command: /u/salvadord/venvs/neuron78/bin/python3 -c 'import sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/private/var/folders/bw/wjc7k1z10vlbs6f_cypxhnb40000gn/T/pip-install-u4lds2qm/matplotlib/setup.py'\"'\"'; __file__='\"'\"'/private/var/folders/bw/wjc7k1z10vlbs6f_cypxhnb40000gn/T/pip-install-u4lds2qm/matplotlib/setup.py'\"'\"';f=getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__);code=f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' egg_info --egg-base /private/var/folders/bw/wjc7k1z10vlbs6f_cypxhnb40000gn/T/pip-pip-egg-info-4ijcqipq\n","         cwd: /private/var/folders/bw/wjc7k1z10vlbs6f_cypxhnb40000gn/T/pip-install-u4lds2qm/matplotlib/\n","    Complete output (156 lines):\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    ============================================================================\n","    Edit setup.cfg to change the build options\n","    \n","    BUILDING MATPLOTLIB\n","                matplotlib: yes [2.2.4]\n","                    python: yes [3.8.3 (default, Jul  2 2020, 11:26:31)  [Clang\n","                            10.0.0 ]]\n","                  platform: yes [darwin]\n","    \n","    REQUIRED DEPENDENCIES AND EXTENSIONS\n","                     numpy: yes [version 1.19.2]\n","          install_requires: yes [handled by setuptools]\n","                    libagg: yes [pkg-config information for 'libagg' could not\n","                            be found. Using local copy.]\n","                  freetype: no  [The C/C++ header for freetype2 (ft2build.h)\n","                            could not be found.  You may need to install the\n","                            development package.]\n","                       png: yes [version 1.6.37]\n","                     qhull: yes [pkg-config information for 'libqhull' could not\n","                            be found. Using local copy.]\n","    \n","    OPTIONAL SUBPACKAGES\n","               sample_data: yes [installing]\n","                  toolkits: yes [installing]\n","                     tests: no  [skipping due to configuration]\n","            toolkits_tests: no  [skipping due to configuration]\n","    \n","    OPTIONAL BACKEND EXTENSIONS\n","                    macosx: yes [installing, darwin]\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","                    qt5agg: no  [PySide2 not found; PyQt5 not found]\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","                    qt4agg: no  [PySide not found; PyQt4 not found]\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","                   gtk3agg: no  [Requires pygobject to be installed.]\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","    IMPORTANT WARNING:\n","        pkg-config is not installed.\n","        matplotlib may not be able to find some of its dependencies\n","                 gtk3cairo: no  [Requires cairocffi or pycairo to be installed.]\n","                    gtkagg: no  [Requires pygtk]\n","                     tkagg: yes [installing; run-time loading from Python Tcl /\n","                            Tk]\n","                     wxagg: no  [requires wxPython]\n","                       gtk: no  [Requires pygtk]\n","                       agg: yes [installing]\n","                     cairo: no  [cairocffi or pycairo not found]\n","                 windowing: no  [Microsoft Windows only]\n","    \n","    OPTIONAL LATEX DEPENDENCIES\n","                    dvipng: no\n","               ghostscript: no\n","                     latex: no\n","                   pdftops: no\n","    \n","    OPTIONAL PACKAGE DATA\n","                      dlls: no  [skipping due to configuration]\n","    \n","    ============================================================================\n","                            * The following required packages can not be built:\n","                            * freetype\n","                            * Try installing freetype with `brew install\n","                            * freetype` and pkg-config with `brew install pkg-\n","                            * config`\n","    ----------------------------------------\u001b[0m\n","\u001b[31mERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.\u001b[0m\n","\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.3 is available.\n","You should consider upgrading via the '/u/salvadord/venvs/neuron78/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n"]}]},{"cell_type":"code","metadata":{"id":"466270WEBaoy","executionInfo":{"status":"ok","timestamp":1623827899288,"user_tz":-300,"elapsed":13,"user":{"displayName":"Evgenia Karunus","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhA2ccNLhFnt0hUfl13RoguCJ6cDbCPCY8_SVjQAg=s64","userId":"04024508215281503990"}}},"source":[],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"id":"OYCcv_JY2dm8","tags":["outputPrepend"]},"source":["!git clone https://github.com/suny-downstate-medical-center/netpyne-course-2021.git\n","#!rm x86_64\n","!nrnivmodl netpyne-course-2021\n","#from neuron import load_mechanisms\n","#try:\n","#  load_mechanisms('.')\n","#except:\n","#  print('already loaded')"],"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["/site-packages/neuron/.data/bin/nocmodl Nca.mod\n"," -> \u001b[32mNMODL\u001b[0m OFThpo.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl OFThpo.mod\n","Translating NMDA.mod into NMDA.c\n","Translating Nca.mod into Nca.c\n","INCLUDEing netcon.inc\n","Translating OFThpo.mod into OFThpo.c\n","Notice: Assignment to the GLOBAL variable, \"tadj\", is not thread safe\n","Thread Safe\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m OFThresh.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl OFThresh.mod\n"," -> \u001b[32mNMODL\u001b[0m ar.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl ar.mod\n"," -> \u001b[32mNMODL\u001b[0m cad.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl cad.mod\n","Translating OFThresh.mod into OFThresh.c\n","Translating ar.mod into ar.c\n","Translating cad.mod into cad.c\n","Thread Safe\n","Thread Safe\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m cat.mod\n"," -> \u001b[32mNMODL\u001b[0m cal.mod\n"," -> \u001b[32mNMODL\u001b[0m cadad.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl cat.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl cal.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl cadad.mod\n","Translating cadad.mod into cadad.c\n","Translating cat.mod into cat.c\n","Translating cal.mod into cal.c\n","Thread Safe\n","Thread Safe\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m iholmw.mod\n"," -> \u001b[32mNMODL\u001b[0m expsynstdp.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl iholmw.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl expsynstdp.mod\n"," -> \u001b[32mNMODL\u001b[0m izhi2003a.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl izhi2003a.mod\n","Translating expsynstdp.mod into expsynstdp.c\n","Translating iholmw.mod into iholmw.c\n","Translating izhi2003a.mod into izhi2003a.c\n","INCLUDEing aux_fun.inc\n","Thread Safe\n","Thread Safe\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m izhi2007a.mod\n"," -> \u001b[32mNMODL\u001b[0m izhi2003b.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl izhi2007a.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl izhi2003b.mod\n"," -> \u001b[32mNMODL\u001b[0m izhi2007b.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl izhi2007b.mod\n","Translating izhi2007a.mod into izhi2007a.c\n","Translating izhi2003b.mod into izhi2003b.c\n","Notice: VERBATIM blocks are not thread safe\n","Translating izhi2007b.mod into izhi2007b.c\n","Notice: VERBATIM blocks are not thread safe\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m k2.mod\n"," -> \u001b[32mNMODL\u001b[0m ka.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl k2.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl ka.mod\n"," -> \u001b[32mNMODL\u001b[0m kacurrent.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl kacurrent.mod\n","Translating k2.mod into k2.c\n","Translating ka.mod into ka.c\n","Translating kacurrent.mod into kacurrent.c\n","Thread Safe\n","Thread Safe\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m kahp.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl kahp.mod\n"," -> \u001b[32mNMODL\u001b[0m kc.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl kc.mod\n"," -> \u001b[32mNMODL\u001b[0m kca.mod\n"," -> \u001b[32mNMODL\u001b[0m kdr.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl kdr.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl kca.mod\n","Translating kahp.mod into kahp.c\n","Thread Safe\n","Translating kc.mod into kc.c\n","Translating kdr.mod into kdr.c\n","Thread Safe\n","Translating kca.mod into kca.c\n","Thread Safe\n"," -> \u001b[32mNMODL\u001b[0m kdrcurrent.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl kdrcurrent.mod\n","Notice: Assignment to the GLOBAL variable, \"tadj\", is not thread safe\n"," -> \u001b[32mNMODL\u001b[0m km.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl km.mod\n"," -> \u001b[32mNMODL\u001b[0m km2.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl km2.mod\n"," -> \u001b[32mNMODL\u001b[0m kv.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl kv.mod\n","Translating kdrcurrent.mod into kdrcurrent.c\n","Thread Safe\n","Translating km.mod into km.c\n","Translating km2.mod into km2.c\n","Thread Safe\n","Translating kv.mod into kv.c\n"," -> \u001b[32mNMODL\u001b[0m nacurrent.mod\n","Thread Safe\n","Notice: Assignment to the GLOBAL variable, \"ntau\", is not thread safe\n","Notice: Assignment to the GLOBAL variable, \"ninf\", is not thread safe\n","Notice: Assignment to the GLOBAL variable, \"tadj\", is not thread safe\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl nacurrent.mod\n"," -> \u001b[32mNMODL\u001b[0m naf.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl naf.mod\n"," -> \u001b[32mNMODL\u001b[0m nap.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl nap.mod\n"," -> \u001b[32mNMODL\u001b[0m naz.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl naz.mod\n","Translating nacurrent.mod into nacurrent.c\n","Thread Safe\n","Translating naf.mod into naf.c\n","Thread Safe\n","Translating nap.mod into nap.c\n"," -> \u001b[32mNMODL\u001b[0m vecevent.mod\n","MODLUNIT=/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib \\\n","\t  /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/bin/nocmodl vecevent.mod\n","Translating naz.mod into naz.c\n","Thread Safe\n","Notice: Assignment to the GLOBAL variable, \"htau\", is not thread safe\n","Notice: Assignment to the GLOBAL variable, \"hinf\", is not thread safe\n","Notice: Assignment to the GLOBAL variable, \"mtau\", is not thread safe\n","Notice: Assignment to the GLOBAL variable, \"minf\", is not thread safe\n","Notice: Assignment to the GLOBAL variable, \"tadj\", is not thread safe\n"," -> \u001b[32mCompiling\u001b[0m x86_64/A.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/A.c -o x86_64/A.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/AMPA.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/AMPA.c -o x86_64/AMPA.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/ElectSyn.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/ElectSyn.c -o x86_64/ElectSyn.o)\n","Translating vecevent.mod into vecevent.c\n","Notice: ARTIFICIAL_CELL is a synonym for POINT_PROCESS which hints that it\n","only affects and is affected by discrete events. As such it is not\n","located in a section and is not associated with an integrator\n","Thread Safe\n"," -> \u001b[32mCompiling\u001b[0m x86_64/GABAa.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/GABAa.c -o x86_64/GABAa.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/MyExp2SynBB.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/MyExp2SynBB.c -o x86_64/MyExp2SynBB.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/MyExp2SynNMDABB.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/MyExp2SynNMDABB.c -o x86_64/MyExp2SynNMDABB.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/NMDA.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/NMDA.c -o x86_64/NMDA.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/Nca.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/Nca.c -o x86_64/Nca.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/OFThpo.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/OFThpo.c -o x86_64/OFThpo.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/OFThresh.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/OFThresh.c -o x86_64/OFThresh.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/ar.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/ar.c -o x86_64/ar.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/cad.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/cad.c -o x86_64/cad.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/cadad.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/cadad.c -o x86_64/cadad.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/cal.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/cal.c -o x86_64/cal.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/cat.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/cat.c -o x86_64/cat.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/expsynstdp.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/expsynstdp.c -o x86_64/expsynstdp.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/iholmw.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/iholmw.c -o x86_64/iholmw.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/izhi2003a.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/izhi2003a.c -o x86_64/izhi2003a.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/izhi2003b.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/izhi2003b.c -o x86_64/izhi2003b.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/izhi2007a.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/izhi2007a.c -o x86_64/izhi2007a.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/izhi2007b.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/izhi2007b.c -o x86_64/izhi2007b.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/k2.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/k2.c -o x86_64/k2.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/ka.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/ka.c -o x86_64/ka.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/kacurrent.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/kacurrent.c -o x86_64/kacurrent.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/kahp.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/kahp.c -o x86_64/kahp.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/kc.c\n"," -> \u001b[32mCompiling\u001b[0m x86_64/kca.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/kc.c -o x86_64/kc.o)\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/kca.c -o x86_64/kca.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/kdr.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/kdr.c -o x86_64/kdr.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/kdrcurrent.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/kdrcurrent.c -o x86_64/kdrcurrent.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/km.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/km.c -o x86_64/km.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/km2.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/km2.c -o x86_64/km2.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/kv.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/kv.c -o x86_64/kv.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/nacurrent.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/nacurrent.c -o x86_64/nacurrent.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/naf.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/naf.c -o x86_64/naf.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/nap.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/nap.c -o x86_64/nap.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/naz.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/naz.c -o x86_64/naz.o)\n"," -> \u001b[32mCompiling\u001b[0m x86_64/vecevent.c\n","(cd .. ; gcc -O2   -I. -I..   -I/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include  -I/usr/local/Cellar/open-mpi/4.0.4_1/include -fPIC -c x86_64/vecevent.c -o x86_64/vecevent.o)\n"," => \u001b[32mLINKING\u001b[0m library x86_64/libnrnmech.dylib Mod files: A.mod AMPA.mod ElectSyn.mod GABAa.mod MyExp2SynBB.mod MyExp2SynNMDABB.mod NMDA.mod Nca.mod OFThpo.mod OFThresh.mod ar.mod cad.mod cadad.mod cal.mod cat.mod expsynstdp.mod iholmw.mod izhi2003a.mod izhi2003b.mod izhi2007a.mod izhi2007b.mod k2.mod ka.mod kacurrent.mod kahp.mod kc.mod kca.mod kdr.mod kdrcurrent.mod km.mod km2.mod kv.mod nacurrent.mod naf.mod nap.mod naz.mod vecevent.mod\n","(cd .. ; g++ -O2 -DVERSION_INFO='7.8.1.1' -std=c++11 -dynamiclib -Wl,-headerpad_max_install_names -undefined dynamic_lookup -fPIC  -I /Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/include -o x86_64/libnrnmech.dylib -Wl,-install_name,@rpath/libnrnmech.dylib \\\n","\t  x86_64/mod_func.o x86_64/A.o x86_64/AMPA.o x86_64/ElectSyn.o x86_64/GABAa.o x86_64/MyExp2SynBB.o x86_64/MyExp2SynNMDABB.o x86_64/NMDA.o x86_64/Nca.o x86_64/OFThpo.o x86_64/OFThresh.o x86_64/ar.o x86_64/cad.o x86_64/cadad.o x86_64/cal.o x86_64/cat.o x86_64/expsynstdp.o x86_64/iholmw.o x86_64/izhi2003a.o x86_64/izhi2003b.o x86_64/izhi2007a.o x86_64/izhi2007b.o x86_64/k2.o x86_64/ka.o x86_64/kacurrent.o x86_64/kahp.o x86_64/kc.o x86_64/kca.o x86_64/kdr.o x86_64/kdrcurrent.o x86_64/km.o x86_64/km2.o x86_64/kv.o x86_64/nacurrent.o x86_64/naf.o x86_64/nap.o x86_64/naz.o x86_64/vecevent.o -L/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/lib -lnrniv -Wl,-rpath,/Users/salvadord/venvs/neuron78/lib/python3.8/site-packages/neuron/.data/lib    -lreadline)\n","(cd .. ; rm -f x86_64/.libs/libnrnmech.so ; mkdir -p x86_64/.libs ; ln -s ../../x86_64/libnrnmech.dylib x86_64/.libs/libnrnmech.so)\n","Successfully created x86_64/special\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"f0P--qg5YUT6","executionInfo":{"status":"ok","timestamp":1623827912611,"user_tz":-300,"elapsed":6667,"user":{"displayName":"Evgenia Karunus","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhA2ccNLhFnt0hUfl13RoguCJ6cDbCPCY8_SVjQAg=s64","userId":"04024508215281503990"}},"outputId":"95a7bccb-22dd-401f-e8bc-b017e7b7ba00"},"source":["\"\"\"\n","tut_artif.py\n","\n","Tutorial on artificial cells (no sections)\n","\"\"\"\n","\n","from netpyne import specs, sim\n","from netpyne.specs import Dict\n","\n","netParams = specs.NetParams()  # object of class NetParams to store the network parameters\n","simConfig = specs.SimConfig()  # dictionary to store sets of simulation configurations\n","\n","\n","###############################################################################\n","# NETWORK PARAMETERS\n","###############################################################################\n","# Cell parameters\n","## PYR cell properties\n","cellParams = Dict()\n","cellParams.secs.soma.geom = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0}\n","cellParams.secs.soma.mechs.hh = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70}\n","netParams.cellParams['PYR'] = cellParams\n","\n","## IntFire2 artificial cell\n","netParams.cellParams['artif_IntFire2'] = {\n","    'cellModel': 'IntFire2', \n","    'params': {'ib': 0.0}}  \n","\n","## IntFire4 artificial cell\n","netParams.cellParams['artif_IntFire4'] = {\n","    'cellModel': 'IntFire4', \n","    'params': {'taue': 1.0}}\n","\n","## NetStim artificial spike generator\n","netParams.cellParams['artif_NetStim'] = {\n","    'cellModel': 'NetStim'}\n","    \n","## VecStim artificial spike generator\n","netParams.cellParams['artif_VecStim'] = {\n","    'cellModel': 'VecStim'}  # pop of Vecstims with 2 pulses\n","\n","\n","# Population parameters\n","netParams.popParams['PYR1'] = {\n","    'cellType': 'PYR', \n","    'numCells': 100} # pop of HH cells\n","\n","netParams.popParams['pop_IntFire2'] = {\n","    'cellType': 'artif_IntFire2', \n","    'numCells': 100}  # pop of IntFire2\n","\n","netParams.popParams['pop_IntFire4'] = {\n","    'cellType': 'artif_IntFire4', \n","    'numCells': 100}  # pop of IntFire4\n","\n","netParams.popParams['pop_NetStim'] = {\n","    'cellType': 'artif_NetStim', \n","    'numCells': 100,\n","    'rate': 10, \n","    'noise': 0.8, \n","    'start': 1, \n","    'seed': 2}  # pop of NEtSims\n","\n","netParams.popParams['pop_VecStim'] = {\n","    'cellType': 'artif_VecStim', \n","    'numCells': 100, \n","    'rate': 5, \n","    'noise': 0.5, \n","    'start': 50,\n","    'pulses': [{'start': 200, 'end': 300, 'rate': 60, 'noise':0.2}, \n","               {'start': 500, 'end': 800, 'rate': 30, 'noise': 0.5}]}  # pop of Vecstims with 2 pulses\n","\n","\n","# Synaptic mechanism parameters\n","netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.1, 'tau2': 1.0, 'e': 0}\n","\n","\n","\n","# Connections\n","netParams.connParams['NetStim->PYR1'] = {\n","    'preConds': {'pop': 'pop_NetStim'}, \n","    'postConds': {'pop': 'PYR1'},\n","    'convergence': 3,\n","    'weight': 0.002,\n","    'synMech': 'AMPA',\n","    'delay': 'uniform(1,5)'}\n","\n","\n","netParams.connParams['VecStim->PYR1'] = {\n","    'preConds': {'pop': 'pop_VecStim'}, \n","    'postConds': {'pop': 'PYR1'},\n","    'probability': 0.4,\n","    'weight': 0.005,\n","    'synMech': 'AMPA',\n","    'delay': 'uniform(1,5)'}\n","\n","netParams.connParams['PYR1->IntFire2'] = {\n","    'preConds': {'pop': 'PYR1'}, \n","    'postConds': {'pop': 'pop_IntFire2'},\n","    'probability': 0.2,\n","    'weight': 0.05,\n","    'synMech': 'AMPA',\n","    'delay': 'uniform(1,5)'}\n","\n","\n","netParams.connParams['IntFire2->IntFire4'] = {\n","    'preConds': {'pop': 'pop_IntFire2'}, \n","    'postConds': {'pop': 'pop_IntFire4'},\n","    'probability': 0.1,\n","    'weight': 0.2,\n","    'delay': 'uniform(1,5)'}\n","\n","\n","###############################################################################\n","# SIMULATION PARAMETERS\n","###############################################################################\n","\n","# Simulation parameters\n","simConfig.duration = 1*1e3 # Duration of the simulation, in ms\n","simConfig.dt = 0.1 # Internal integration timestep to use\n","simConfig.createNEURONObj = 1  # create HOC objects when instantiating network\n","simConfig.createPyStruct = 1  # create Python structure (simulator-independent) when instantiating network\n","simConfig.verbose = 0 #False  # show detailed messages\n","\n","# Recording\n","simConfig.recordTraces = {'Vsoma':{'sec':'soma','loc':0.5,'var':'v'}}\n","\n","# # Analysis and plotting\n","simConfig.analysis['plotRaster'] = {'orderInverse': True}\n","\n","\n","###############################################################################\n","# RUN SIM\n","###############################################################################\n","\n","sim.createSimulateAnalyze()\n"],"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Start time:  2021-07-09 17:21:16.531056\n","\n","Creating network of 5 cell populations on 1 hosts...\n","  Number of cells on node 0: 500 \n","  Done; cell creation time = 0.08 s.\n","Making connections...\n","  Number of connections on node 0: 7227 \n","  Done; cell connection time = 0.92 s.\n","  Number of stims on node 0: 0 \n","  Done; cell stims creation time = 0.00 s.\n","Recording 0 traces of 0 types on node 0\n","\n","Running simulation for 1000.0 ms...\n","  Done; run time = 1.65 s; real-time ratio: 0.61.\n","\n","Gathering data...\n","  Done; gather time = 0.22 s.\n","\n","Analyzing...\n","  Cells: 500\n","  Connections: 7227 (14.45 per cell)\n","  Spikes: 4340 (8.68 Hz)\n","  Simulated time: 1.0 s; 1 workers\n","  Run time: 1.65 s\n","  Done; saving time = 0.65 s.\n","Plotting raster...\n"]},{"output_type":"display_data","data":{"text/plain":"<Figure size 720x576 with 1 Axes>","image/svg+xml":"<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["  Done; plotting time = 1.05 s\n\nTotal time = 4.57 s\n\nEnd time:  2021-07-09 17:21:21.099059\n"]}]},{"cell_type":"code","metadata":{"id":"G1Z-Kd9jZ6lp","executionInfo":{"status":"ok","timestamp":1623827912613,"user_tz":-300,"elapsed":9,"user":{"displayName":"Evgenia Karunus","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhA2ccNLhFnt0hUfl13RoguCJ6cDbCPCY8_SVjQAg=s64","userId":"04024508215281503990"}}},"source":[],"execution_count":5,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"P8deeT59anO8"},"source":["1) Modify the network to show that you can use the IntFire2 population to provide inhibition to another population (probably using negative weights)."]}]}
diff --git a/netpyne/tutorials/netpyne-course-2021/tut_netpyne_import.ipynb b/netpyne/tutorials/netpyne-course-2021/tut_netpyne_import.ipynb
deleted file mode 100644
index e1bb564e8..000000000
--- a/netpyne/tutorials/netpyne-course-2021/tut_netpyne_import.ipynb
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-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"tut_netpyne_import.ipynb","provenance":[{"file_id":"1xcqB5I_iBlz3TNopuNERCJ1StlvZZJw5","timestamp":1621531137101},{"file_id":"19y6MLKhDAdBxLUZm2sHOuQx-5bqSODs-","timestamp":1621524871397}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","metadata":{"id":"V0cRyhp8YWl2"},"source":["!pip install neuron\n","!pip install netpyne\n","import matplotlib"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"QzblvwEt9Ovm"},"source":["rm -r netpyne-course-2021"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2RjvKVYDyuXM","executionInfo":{"status":"ok","timestamp":1622054253624,"user_tz":180,"elapsed":949,"user":{"displayName":"Heitor Sanchez Fernandes","photoUrl":"https://lh4.googleusercontent.com/-rA27vcXArxo/AAAAAAAAAAI/AAAAAAAAD7g/nfG-Ur_4g14/s64/photo.jpg","userId":"02869219792670315781"}},"outputId":"08bb8ed9-d88a-4ac3-9c82-11e8163a0d0c"},"source":["!git clone https://github.com/suny-downstate-medical-center/netpyne-course-2021.git\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Cloning into 'netpyne-course-2021'...\n","remote: Enumerating objects: 100, done.\u001b[K\n","remote: Counting objects: 100% (100/100), done.\u001b[K\n","remote: Compressing objects: 100% (79/79), done.\u001b[K\n","remote: Total 100 (delta 30), reused 77 (delta 18), pack-reused 0\u001b[K\n","Receiving objects: 100% (100/100), 116.59 KiB | 5.30 MiB/s, done.\n","Resolving deltas: 100% (30/30), done.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"xAt-SfNS8rQt"},"source":["cd netpyne-course-2021"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"HAYqWzwz8IBy"},"source":["!nrnivmodl ."],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"f0P--qg5YUT6"},"source":["from netpyne import specs, sim\n","%matplotlib inline\n","\n","from netpyne import specs, sim\n","\n","# Network parameters\n","netParams = specs.NetParams()  # object of class NetParams to store the network parameters\n","\n","\n","\n","### HH\n","netParams.importCellParams(\n","    label='PYR_HH', \n","    fileName='HHCellFile.py', \n","    cellName='HHCellClass', \n","    importSynMechs=True,\n","    )\n","\n","\n","### HH3D HOC\n","cellRule = netParams.importCellParams(\n","    label='PYR_HH3D_hoc', \n","    fileName='geom.hoc', \n","    cellName='E21', \n","    importSynMechs=False,\n","    )\n","cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70}\n","for secName in cellRule['secs']:\n","    cellRule['secs'][secName]['mechs']['pas'] = {'g': 0.0000357, 'e': -70}\n","    cellRule['secs'][secName]['geom']['cm'] = 1\n","\n","\n","\n","\n","### HH3D SWC\n","cellRule = netParams.importCellParams(\n","    label='PYR_HH3D_swc', \n","    conds={'cellType': 'PYR', 'cellModel': 'HH3D_swc'},\n","    fileName='BS0284.swc', \n","    cellName='BS0284',\n","    )\n","netParams.renameCellParamsSec('PYR_HH3D_swc', 'soma_0', 'soma')  # rename imported section 'soma_0' to 'soma'\n","for secName in cellRule['secs']:\n","    cellRule['secs'][secName]['mechs']['pas'] = {'g': 0.0000357, 'e': -70}\n","    cellRule['secs'][secName]['geom']['cm'] = 1\n","    if secName.startswith('soma'):\n","        cellRule['secs'][secName]['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70}\n","\n","\n","### Traub\n","cellRule = netParams.importCellParams(\n","    label='PYR_Traub', \n","    fileName='pyr3_traub.hoc', \n","    cellName='pyr3',\n","    )\n","somaSec = cellRule['secLists']['Soma'][0]\n","cellRule['secs'][somaSec]['spikeGenLoc'] = 0.5\n","\n","\n","### Mainen\n","netParams.importCellParams(\n","    label='PYR_Mainen_rule', \n","    conds={'cellType': 'PYR', 'cellModel': 'Mainen'},\n","    fileName='mainen.py', \n","    cellName='PYR2',\n","    )\n","\n","\n","### Friesen\n","cellRule = netParams.importCellParams(\n","    label='PYR_Friesen_rule', \n","    conds={'cellType': 'PYR', 'cellModel': 'Friesen'},\n","    fileName='friesen.py', \n","    cellName='MakeRSFCELL',\n","    )\n","cellRule['secs']['axon']['spikeGenLoc'] = 0.5\n","\n","\n","### Izhi2003a (independent voltage)\n","cellRule = netParams.importCellParams(\n","    label='PYR_Izhi03a_rule', \n","    conds={'cellType': 'PYR', 'cellModel':'Izh2003a'},\n","    fileName='izhi2003Wrapper.py', \n","    cellName='IzhiCell',\n","    cellArgs={'type':'tonic spiking', 'host':'dummy'},\n","    )\n","netParams.renameCellParamsSec('PYR_Izhi03a_rule', 'sec', 'soma')  # rename imported section 'sec' to 'soma'\n","cellRule['secs']['soma']['pointps']['Izhi2003a_0']['vref'] = 'V' # specify that uses its own voltage V\n","\n","\n","### Izhi2003b (section voltage)\n","netParams.importCellParams(\n","    label='PYR_Izhi03b_rule', \n","    conds={'cellType': 'PYR', 'cellModel':'Izh2003b'},\n","    fileName='izhi2003Wrapper.py', \n","    cellName='IzhiCell',  \n","    cellArgs={'type':'tonic spiking'},\n","    )\n","\n","\n","### Izhi2007a (independent voltage)\n","cellRule = netParams.importCellParams(\n","    label='PYR_Izhi07a_rule', \n","    conds={'cellType': 'PYR', 'cellModel':'Izh2007a'},\n","    fileName='izhi2007Wrapper.py', \n","    cellName='IzhiCell',  \n","    cellArgs={'type':'RS', 'host':'dummy'},\n","    )\n","netParams.renameCellParamsSec('PYR_Izhi07a_rule', 'sec', 'soma')  # rename imported section 'sec' to 'soma'\n","cellRule['secs']['soma']['pointps']['Izhi2007a_0']['vref'] = 'V' # specify that uses its own voltage V\n","cellRule['secs']['soma']['pointps']['Izhi2007a_0']['synList'] = ['AMPA', 'NMDA', 'GABAA', 'GABAB']  # specify its own synapses\n","\n","\n","### Izhi2007b (section voltage)\n","netParams.importCellParams(\n","    label='PYR_Izhi07b_rule', \n","    conds={'cellType': 'PYR', 'cellModel':'Izh2007b'},\n","    fileName='izhi2007Wrapper.py', \n","    cellName='IzhiCell',  \n","    cellArgs={'type':'RS'},\n","    )\n","\n","\n","## Population parameters\n","netParams.popParams['HH_pop'] = {'cellType': 'PYR_HH', 'numCells': 5}\n","netParams.popParams['HH3D_pop_hoc'] = {'cellType': 'PYR_HH3D_hoc', 'numCells': 5}\n","netParams.popParams['HH3D_pop_swc'] = {'cellType': 'PYR_HH3D_swc', 'numCells': 5}\n","netParams.popParams['Traub_pop'] = {'cellType': 'PYR_Traub', 'numCells': 5}\n","netParams.popParams['Mainen_pop'] = {'cellType': 'PYR', 'numCells': 5}\n","netParams.popParams['Friesen_pop'] = {'cellType': 'PYR', 'numCells': 5}\n","netParams.popParams['Izhi03a_pop'] = {'cellType': 'PYR', 'numCells': 5}\n","netParams.popParams['Izhi03b_pop'] = {'cellType': 'PYR', 'numCells': 5}\n","netParams.popParams['Izhi07a_pop'] = {'cellType': 'PYR', 'numCells': 5}\n","netParams.popParams['Izhi07b_pop'] = {'cellType': 'PYR', 'numCells': 5}\n","\n","\n","\n","## Synaptic mechanism parameters\n","netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 1.0, 'tau2': 5.0, 'e': 0}  # soma NMDA synapse\n","\n","\n","# Stimulation parameters\n","netParams.stimSourceParams['bkg'] = {'type': 'NetStim', 'rate': 50, 'noise': 0.5}\n","netParams.stimTargetParams['bg1'] = {'source': 'bkg', 'conds': {'cellType': 'PYR', 'cellModel': ['Traub', 'HH', 'HH3D_hoc', 'HH3D_swc', 'Mainen', 'Izh2003b', 'Izh2007b']}, 'weight': 0.1, 'delay': 5, 'sec': 'soma'}\n","netParams.stimTargetParams['bg2'] = {'source': 'bkg', 'conds': {'cellType': 'PYR', 'cellModel': ['Friesen','Izh2003a', 'Izh2007a']}, 'weight': 5, 'delay': 5, 'sec': 'soma'}\n","\n","\n","## Connectivity params\n","netParams.connParams['recurrent'] = {\n","    'preConds': {'cellType': 'PYR'}, 'postConds': {'cellType': 'PYR'},  #  PYR -> PYR random\n","    'connFunc': 'convConn',           # connectivity function (random)\n","    'convergence': 'uniform(0,10)',   # max number of incoming conns to cell\n","    'weight': 0.001,                  # synaptic weight\n","    'delay': 5,                       # transmission delay (ms)\n","    'sec': 'soma'}                    # section to connect to\n","\n","netParams.connParams['HH->izhi07a'] = {\n","    'preConds': {'pop': 'HH_pop'}, 'postConds': {'pop': 'Izhi07a_pop'}, # background -> PYR (weight=0.1)\n","    'connFunc': 'fullConn',     # connectivity function (all-to-all)\n","    'weight': 5,                # synaptic weight\n","    'delay': 5,                 # transmission delay (ms)\n","    'sec': 'soma'}              # section to connect to\n","\n","netParams.connParams['izhi07a->HH'] = {\n","    'preConds': {'pop': 'Izhi07a_pop'}, 'postConds': {'pop': 'HH_pop'}, # background -> PYR (weight=0.1)\n","    'connFunc': 'fullConn',     # connectivity function (all-to-all)\n","    'weight': 0.1,              # synaptic weight\n","    'delay': 5,                 # transmission delay (ms)\n","    'sec': 'soma'}              # section to connect to\n","\n","\n","# Simulation options\n","simConfig = specs.SimConfig()       # object of class SimConfig to store simulation configuration\n","simConfig.duration = 1*1e3          # Duration of the simulation, in ms\n","simConfig.dt = 0.025                # Internal integration timestep to use\n","simConfig.verbose = False           # Show detailed messages\n","simConfig.recordTraces = {'V_soma': {'sec': 'soma', 'loc': 0.5, 'var': 'v'}}\n","simConfig.recordStep = 1            # Step size in ms to save data (eg. V traces, LFP, etc)\n","simConfig.filename = 'model_output' # Set file output name\n","simConfig.savePickle = False        # Save params, network and sim output to pickle file\n","simConfig.analysis['plotRaster'] = {'orderInverse': True, 'saveFig': 'tut_import_raster.png'}           \n","#simConfig.analysis['plotTraces'] = {'include': ['HH3D_pop_hoc', 'HH3D_pop_swc']}            \n","\n","\n","# Create network and run simulation\n","sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig)\n","\n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"G1Z-Kd9jZ6lp"},"source":["from pprint import pprint\n","pprint(netParams.cellParams['PYR_HH3D_hoc'])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"BR1mtK7kMbvF"},"source":[""],"execution_count":null,"outputs":[]}]}
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diff --git a/netpyne/tutorials/netpyne-course-2021/tut_netpyne_osc_start.ipynb b/netpyne/tutorials/netpyne-course-2021/tut_netpyne_osc_start.ipynb
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-{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9373,"status":"ok","timestamp":1622092945104,"user":{"displayName":"Samuel Bolland","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjhWpP7mf88kT980MPiR0spUh3By9DWIm5EYVUoaA=s64","userId":"06928499349223853710"},"user_tz":-480},"id":"V0cRyhp8YWl2","outputId":"7ce06ab2-358b-4748-c324-30d36ee2ab1c"},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting neuron\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/14/f4/ea50608c7633c286859d6cce0aad621da22a8da7ff9787efc8bb71fe0597/NEURON-8.0.0-cp37-cp37m-manylinux1_x86_64.whl (12.6MB)\n","\u001b[K     |████████████████████████████████| 12.6MB 22.6MB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.9.3 in /usr/local/lib/python3.7/dist-packages (from neuron) (1.19.5)\n","Installing collected packages: neuron\n","Successfully installed neuron-8.0.0\n","Collecting netpyne\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9e/24/0f9d685a3fbcbca0d86d9ca6521465c43725d9e23760c91524fe77191f12/netpyne-1.0.0.2-py2.py3-none-any.whl (312kB)\n","\u001b[K     |████████████████████████████████| 317kB 23.6MB/s \n","\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from netpyne) (1.4.1)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from netpyne) (1.19.5)\n","Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from netpyne) (0.16.0)\n","Requirement already satisfied: bokeh in /usr/local/lib/python3.7/dist-packages (from netpyne) (2.3.2)\n","Collecting matplotlib-scalebar\n","  Downloading https://files.pythonhosted.org/packages/51/a4/cd254234c35f3591361988e89ab132ee14789f2ebe1ede621d63f5241f00/matplotlib_scalebar-0.7.2-py2.py3-none-any.whl\n","Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from netpyne) (1.1.5)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from netpyne) (3.2.2)\n","Requirement already satisfied: PyYAML>=3.10 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (3.13)\n","Requirement already satisfied: typing-extensions>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (3.7.4.3)\n","Requirement already satisfied: Jinja2>=2.9 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (2.11.3)\n","Requirement already satisfied: pillow>=7.1.0 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (7.1.2)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (2.8.1)\n","Requirement already satisfied: packaging>=16.8 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (20.9)\n","Requirement already satisfied: tornado>=5.1 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (5.1.1)\n","Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->netpyne) (2018.9)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->netpyne) (2.4.7)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->netpyne) (0.10.0)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->netpyne) (1.3.1)\n","Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from Jinja2>=2.9->bokeh->netpyne) (2.0.1)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->bokeh->netpyne) (1.15.0)\n","Installing collected packages: matplotlib-scalebar, netpyne\n","Successfully installed matplotlib-scalebar-0.7.2 netpyne-1.0.0.2\n"]}],"source":["!pip install neuron\n","!pip install -U netpyne\n","import matplotlib"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YxTciw4Pf7Z8"},"outputs":[],"source":["%matplotlib inline"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":5480,"status":"ok","timestamp":1621462283899,"user":{"displayName":"Salvador Dura-Bernal","photoUrl":"","userId":"10473966374056868820"},"user_tz":240},"id":"f0P--qg5YUT6","outputId":"a0d41a9f-ac05-434f-dc1f-2182ad919206"},"outputs":[{"name":"stdout","output_type":"stream","text":["\n","Start time:  2021-05-19 22:11:18.605467\n","\n","Creating network of 2 cell populations on 1 hosts...\n","  Number of cells on node 0: 40 \n","  Done; cell creation time = 0.00 s.\n","Making connections...\n","  Number of connections on node 0: 379 \n","  Done; cell connection time = 0.04 s.\n","Adding stims...\n","  Number of stims on node 0: 20 \n","  Done; cell stims creation time = 0.00 s.\n","Recording 1 traces of 1 types on node 0\n","\n","Running simulation for 1000.0 ms...\n","  Done; run time = 1.58 s; real-time ratio: 0.63.\n","\n","Gathering data...\n","  Done; gather time = 0.02 s.\n","\n","Analyzing...\n","  Cells: 40\n","  Connections: 399 (9.97 per cell)\n","  Spikes: 859 (21.48 Hz)\n","  Simulated time: 1.0 s; 1 workers\n","  Run time: 1.58 s\n","  Done; saving time = 0.02 s.\n","Plotting raster...\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 720x576 with 1 Axes>"]},"metadata":{"needs_background":"light","tags":[]},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Plotting recorded cell traces ... cell\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 720x576 with 1 Axes>"]},"metadata":{"needs_background":"light","tags":[]},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Plotting firing rate spectrogram ...\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 720x576 with 2 Axes>"]},"metadata":{"needs_background":"light","tags":[]},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Plotting spike histogram...\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 720x576 with 1 Axes>"]},"metadata":{"needs_background":"light","tags":[]},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["  Done; plotting time = 3.33 s\n","\n","Total time = 5.02 s\n","\n","End time:  2021-05-19 22:11:23.625533\n"]}],"source":["from netpyne import specs, sim\n","\n","# Network parameters\n","netParams = specs.NetParams()  # object of class NetParams to store the network parameters\n","\n","## Cell parameters/rules\n","PYRcell = {'secs': {}}\n","PYRcell['secs']['soma'] = {'geom': {}, 'mechs': {}}\n","PYRcell['secs']['soma']['geom'] = {\n","    'diam': 18.8,   \n","    'L': 18.8, \n","    'Ra': 123.0}  # soma geometry\n","PYRcell['secs']['soma']['mechs']['hh'] = {\n","    'gnabar': 0.12, \n","    'gkbar': 0.036, \n","    'gl': 0.003, \n","    'el': -70}  # soma hh mechanism\n","netParams.cellParams['PYR'] = PYRcell\n","\n","## Population parameters\n","netParams.popParams['S'] = {\n","    'cellType': 'PYR', \n","    'numCells': 20}\n","netParams.popParams['I'] = {\n","    'cellType': 'PYR', \n","    'numCells': 20}\n","\n","## Synaptic mechanism parameters\n","netParams.synMechParams['exc'] = {\n","    'mod': 'Exp2Syn', \n","    'tau1': 0.1, \n","    'tau2': 5.0, \n","    'e': 0}  # excitatory synaptic mechanism\n","\n","netParams.synMechParams['inh'] = {\n","    'mod': 'Exp2Syn', \n","    'tau1': 0.1, \n","    'tau2': 5.0, \n","    'e': -70}  # inhibitory synaptic mechanism\n","\n","\n","# Stimulation parameters\n","netParams.stimSourceParams['bkg'] = {\n","    'type': 'NetStim', \n","    'rate': 50, \n","    'noise': 0.5}\n","    \n","netParams.stimTargetParams['bkg->S'] = {\n","    'source': 'bkg', \n","    'conds': {'pop': 'S'}, \n","    'weight': 0.01, \n","    'delay': 5, \n","    'synMech': 'exc'}\n","\n","## Cell connectivity rules\n","netParams.connParams['S->I'] = {    #  S -> I label\n","    'preConds': {'pop': 'S'},       # conditions of presyn cells\n","    'postConds': {'pop': 'I'},      # conditions of postsyn cells\n","    'divergence': 5,               # probability of connection\n","    'weight': 0.01,                 # synaptic weight\n","    'delay': 5,                     # transmission delay (ms)\n","    'synMech': 'exc'}               # synaptic mechanism\n","\n","netParams.connParams['I->S'] = {    #  I -> S label\n","    'preConds': {'pop': 'I'},       # conditions of presyn cells\n","    'postConds': {'pop': 'S'},      # conditions of postsyn cells\n","    'probability': 0.7,               # probability of connection\n","    'weight': 0.02,                 # synaptic weight\n","    'delay': 5,                     # transmission delay (ms)\n","    'synMech': 'inh'}               # synaptic mechanism\n","\n","\n","# Simulation options\n","simConfig = specs.SimConfig()       # object of class SimConfig to store simulation configuration\n","\n","simConfig.duration = 1*1e3          # Duration of the simulation, in ms\n","simConfig.dt = 0.025                # Internal integration timestep to use\n","simConfig.verbose = False           # Show detailed messages\n","simConfig.recordTraces = {'V_soma':{'sec':'soma','loc':0.5,'var':'v'}}  # Dict with traces to record\n","simConfig.recordStep = 0.1          # Step size in ms to save data (eg. V traces, LFP, etc)\n","simConfig.filename = 'tut2'  # Set file output name\n","simConfig.savePickle = False        # Save params, network and sim output to pickle file\n","simConfig.saveJson = False\n","\n","simConfig.analysis['plotTraces'] = {'include': [1], 'saveFig': True}  # Plot recorded traces for this list of cells\n","simConfig.analysis['plotRaster'] = {'showFig': True}                  # Plot a raster\n","simConfig.analysis['plotSpikeHist'] = {'showFig':True, 'include': ['S', 'I']}\n","simConfig.analysis['plotRateSpectrogram'] = {'include': ['all']}\n","\n","\n","\n","# Create network and run simulation\n","sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig)\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":752,"status":"ok","timestamp":1621460922468,"user":{"displayName":"Salvador Dura-Bernal","photoUrl":"","userId":"10473966374056868820"},"user_tz":240},"id":"G1Z-Kd9jZ6lp","outputId":"40e8882c-0e42-4e48-f3f8-86dd466abe29"},"outputs":[{"name":"stdout","output_type":"stream","text":["Plotting spike stats...\n"]},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAaQAAAI0CAYAAABSwl3rAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAc/klEQVR4nO3dfZRkd13n8c83DOEhEzIRETKAZDFikBDARBCVx0QXEBdYjq6uLoK6gIjsGiOCKOkQ4q5rEjkq8hBhE0ARH1cWMbghaiIgOK6QQHgSNgFMBydAyBBCiPDbP6oHOkP3TPdMVdd3pl+vc+pMTVX1re909+333Fu369YYIwAwb4fNewAASAQJgCYECYAWBAmAFgQJgBYECYAWBGmNHvOYx4wk+31ZXFw8oI/f6It5zWte885o3lUJ0hpdd9118x4B4JAmSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0sGXeA8CBqqp5j7BfxhjzHgFasYUEQAu2kDikLCwszHuEfToYZoR5sIUEQAuCBEALggRAC4IEQAuCBEALggRAC4IEQAuCBEALggRAC4IEQAuCBEALggRAC4IEQAuCBEALggRAC4IEQAuCBEALggRAC4IEQAuCBEALggRAC4IEQAuCBEALW+Y9AP1UVZJkjDHnSTiU+T5jT7aQAGhBkABoQZAAaEGQAGhBkABoQZAAaEGQAGhBkABoQZAAaEGQAGhBkABoQZAAaEGQAGhBkABoQZAAaEGQAGhBkABoQZAAaEGQAGhBkABoQZAAaGHuQaqqp1bV3y77+6iq46a07Kuq6tSl6wtV9bppLBeA6Zt7kPalqh5cVW+uquur6tNV9a6qetq85wJguloHqaoemuSSJH+T5Lgkd07yU0keO8+59sc555wz7xFYp127duX3f//3s2vXrnmPQhMLCwvzHqGFWf0827AgVdXzquojVbWrqq6sqiet4cN+LcmFY4xfHWNcNyb+YYzxg8uW+/iqevfSFtTbq+rENcxy+6p6XVV9aunj/r6q7nog/759Oe+882a5eKbspptuymWXXZbFxcVcdtlluemmm+Y9Eg2ceeaZ8x6hhVn9PNvILaSPJHlYkqOSnJnkdVV1zGoPrqo7Jnlokj/ay2MelOTVSZ6RydbTK5K8saput49Zfmxpjnsufdwzk/iJs4eqOigu03bJJZfk3HPPzS233JK3vvWtueWWW3LuuefmkksumerzzPvztp7L9u3b23/dOPhtWJDGGH84xrhmjPHlMcYbknw4yYP38iFHZzLf4l4e8/QkrxhjvHOM8aUxxoVJbk7yHfsY55ZMQnTc0sf9wxjjhj0fVFVPr6odVbVj586d+1jk11pYWLjVyrf7us3+3o4//vhs3bo1Z599dk466aScffbZ2bp1a44//vh5j8YcWI8nNuLzUGOMqS1sr09U9ZQkpyU5dummrZls2XwpyU+OMb576XEjyTcnuSbJriSnjjH+apVlvjnJI5N8cdnNhyf5iTHG66vqqqVlX1xVC5kE6Eer6rZJfjHJf0iyLcnrkrxgjHHLavOffPLJY8eOHfvxL//KrNmoz/WB2v0Nd7DNm0xvH//FF1+cE088Mfe///1zxRVX5PLLL8+pp546lWXvnvFg+fwmyeLiYo45ZtUdGvtllt9ns5g3md16PKt5Z+UAPw+rbh5v2d8lruvZq+6V5PwkpyR5xxjjS1X17r0NNsb4fFW9I8mTk6wYpCQfT3L2GOPs9cyzFJ4zk5xZVccmeXOSDyZ51XqWw6HrhhtuyPnnn5/73Oc++dCHPpT73ve+8x4JDnkbEqQkRyQZSXYmSU0O2z5hDR/33CR/WVVXJ3n1GONTVfWAJM8fY/xQJpH706q6OMm7ktwxky2mS8cYqx4aVVWPSnJdkiuT3JDJLrwv7+e/bU1OO+20WS6eKTvllFPy6Ec/Otu2bcv111/vNQ+SJGeccca8R2hhVj/PNuQ1pDHGlUnOTfKOJJ9Mcv8kb1vDx709yaOXLh+tqk8neWUmWzQZY+xI8p+T/FaSzyT5pyRPXcNId8vkYIkbkrw/k8PKX7uef9N6nX766bNcPFN21FFHZdu2bUmSbdu25aijjprzRHSw2V43Ws2sfp5t1BZSxhgvSPKCVe6+YNnjbvVf0THGu7KX3zsaY1yU5KJV7jt22fWFZddfn+T1+54agI3S+hdjAdg8BAmAFgQJgBYECYAWBAmAFgQJgBYECYAWBAmAFgQJgBYECYAWBAmAFgQJgBYECYAWBAmAFgQJgBYECYAWBAmAFgQJgBYECYAWBAmAFgQJgBa2zHsA+hljZHFxcd5jcIgbY8x7BJqxhQRAC4IEQAuCBEALggRAC4IEQAuCBEALggRAC4IEQAuCBEALggRAC4IEQAuCBEALggRAC4IEQAuCBEALggRAC4IEQAuCBEALggRAC4IEQAuCBEALggRAC1vmPQBM08LCwrxHAPaTLSQAWrCFxCFljDHvEdZkcXFx3iNAO7aQOOiNMXLNNdccNDECViZIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0sGXeA8BaVdW8R5iqMca8R4BWbCEB0IItJA4611x6679vf/jq93W0fF7gq2whAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdDClvV+QFV9Q5Kty28bY3x0ahMBsCmtOUhV9Zgkr0pytyS17K6R5DZTnos5qpp8eccYc56E9fB142C3nl12L01yVpKtY4zDll3ECIADtp5ddkcnecXw3y8AZmA9W0ivSvK0WQ0CwOa2ni2k70jynKp6XpJrl98xxnj4VKcCYNNZT5B+Z+kCAFO35iCNMS6c5SAAbG7r+sXYqnpaVV1SVR9c+tNrSgBMxXp+D+kFSZ6S5NwkVye5V5LnVtX2McbZM5oPgE1iPa8h/WSSR44xrt59Q1W9JcmlSQQJgAOynl12RyTZucdtn0pyh+mNA8BmtZ4gXZTkd6vqW6rqDlV1fJILk7xlNqMBsJmsJ0jPTrIryeVJPpfk3UluTPIzM5gLgE1mPYd935DkKVX11CRfn+S6McaXZzUYAJvLXoNUVceOMa5aun7vPe7euuzdhZ1+AoADsq8tpCuSHLl0/Z8yOdVE7fEYp58A4IDtNUhjjCOXXXd2WQBmZs2RqarfWOX2l0xvHAA2q/Vs9Tx1ldv/0xTmAGCT2+dRdlX147sfu+z6bvdOct3UpwJg01nLYd+7t4AOz623hkaSTyb5sWkPBcDms88gjTEelSRV9eIxxi/NfiQANqP1/GLsV2JUk19AqmX3+QVZAA7Ieo6y215Vf1pVn0ryr0luWXY56FTVd1fV26vqs1X16ap6W1V9+7znAtis1nOU3SuSfDHJKZm8l923JXljkmfOYK6Zqqo7JXlTkt9M8nVJ7p7kzCQ3z+o5zznnnFktmjX45HXJjzz39vmkQ3CgrfUE6TuT/PgY491JxhjjPUl+IsnPzWSy2bpPkowxXj/G+NIY46Yxxl+OMS6f1ROed955s1o0+/DZXcl5F942//iBw/Prr7ltPrtr3hMBK1lPkL6Uya66JLm+qu6Sybt9333qU83eh5J8qaourKrHVtXR8x6oo6pqddkfZ738djnxibfJB//5m3PxxZfkA5/45pz4xNvkrJffbsqfrfXr8PmBTtYTpHcmedzS9bckeUOSP0myY9pDzdrSO5d/dyaHrp+fZGdVvbGq7rr8cVX19KraUVU7du7c89yE+7awsHCrHxa7ry8sLBzwv4G1ecKjbs7RRx2ehRf9Wk466aQsvOjXcvRRh+cJj5rZ3llgP9UYY20PrNqW5LAxxqer6g5JTk+yNclLxhiLM5xx5pZONvi6JB8eY/zwSo85+eSTx44d+9/eqspaP9fztuxd3Oc8ya3tnuuaS299+/aHf/X6nvclyVkvu03q6Kfn/vc/MVdccXnGZ16RX/6p+R0YunveaX9+Z/l1W1xczDHHHDP15c6KeWfrAOdddXN+PYd9X7/s+k1JztrfaboZY3ygqi5I8ox5z8L0XbV4+7z1j1+ZRzz48PzNu76YU7/zDpnsbQY62df5kF60loWMMV44nXE2xtIW0fclecMY4xNVdc8kP5zk72b1nKeddtqsFs0+nPXsG3Pms5J73O2mfOLa5LDDxAg62tcW0j03ZIqNtyvJQ5KctrQr8vpMDgP/+Vk94emnnz6rRbMP27/hq9fvcbf5zQHs3b7Oh/S0jRpkI40x/jnJD857DgC+as2vIa1wCvOvcApzAA7UmoOUlU9hvvtwHqcwB+CArOcou1v9zlJV3S3JGUkum/ZQAGw+6/nF2FsZY1yb5L8m+W/TGweAzWq/g7TkW5LccRqDALC5reeghsvy1deMkkmI7pdkTb+rBAB7s56DGn5nj7/fmOQ9Y4wPT3EeADap9RzUcOEsBwFgc1vPGWMPr6oXVdWHq+rGpT/Pqqrbz3JAADaH9eyye1kmBzE8J8nVSe6V5BczOR/Sj09/NAA2k/UE6YlJvmnZu35fWVXvzOQXZgUJgAOynsO+r83XHuJ9hyQH9bmQAOhhPVtIr01yUVX9ZpJPZPJO4D+d5DVV9ejdDxpjXDLdEQHYDNYTpN0nr/vFPW5/5tIlmfye0qpvwgoAq1nPYd//ZpaDALC5rWcLKVW1Jcl3ZnJk3SeSvGOM8a+zGAyAzWU9bx10fJL/ncmBDB/P5DWkL1TV948x3j+j+QDYJNZzlN1vJ3llknuOMR46xrhHkpcv3Q4AB2Q9QXpgkvPGGMvfYPUlS7cDwAFZT5CuSfKIPW572NLtAHBA1nNQw/OTvLGq3pSvvnXQ9yX50VkMBsDmss8gVdUdk/xSkhOS/FmS9yfZnuS9SV44xvjQTCdkw40xsrjoDTgONrfemw4Hn7VsIb00yclJ/iLJ45J8ZozxrJlOBcCms5bXkB6T5HvHGM9N8thMdtMBwFStJUhHjDEWk2SM8fEkR812JAA2o7XssttSVY9KUqv83RuqAnDA1hKkf0ny6mV//9Qef/eGqgAcsH0GaYxx7AbMAcAmt55fjAWAmREkAFoQJABaECQAWhAkAFoQJABaECQAWhAkAFoQJABaECQAWhAkAFoQJABaECQAWhAkAFoQJABaECQAWhAkAFpYyynMoZXtD9+/+4DebCEB0IItJA4aY4wVb6+qfT6mm8XFxXmPAO3YQuKgN8bINddcc9DECFiZIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0MKWeQ8AB6qq5j3CzI0x5j0CzJwtJABasIXEIeOn3/vbU13eS0941syWvT8zwKHOFhIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALWyZ9wD0U1VJkjHGnCfhUOb7jD3ZQgKgBUECoAVBAqAFQQKgBUECoAVBAqAFQQKgBUECoAVBAqAFQQKgBUECoAVBAqAFQQKgBUECoAVBAqAFQQKgBUECoAVBAqAFQQKgBUECoAVBAqAFQQKgBUECoAVBSlJVV1XVqfOeg0PPjTs/m7945sty487PznsUpmBhYWHeI7RwzjnnzGS5ggQzcvMNn8//fdlF+dR7P5Z/fPlFufmGz897JA7QmWeeOe8RWjjvvPNmslxBYlVVdVBcOnrnuX+WCx7xvBz2/3blrRe/NfXRXbngEc/LO8/9s/1a3rw/x9u3b98UXzfmS5D2oqqeXlU7qmrHzp075z0OB5F7P/ZBOWLbkTn7zBfnpJNOytlnvjhHbDsy937sg+Y9Guu0sLBwq4juvr7Zdt9txOdhy9SWdAgaY7wyySuT5OSTTx5zHmfDjXFw/JM7/m/7Lt/6jTnu8d+eP3/Tm/Kxq6/OFVdckeMef3Lu8q3fuF/Lm/fXYnFxMcccc8xUl9nx67aShYWFr/zQraq5fy3mZSM+D4IEM3Ljxz+d8y89P8d+1/1y1dvel+Me8YB5jwStCRLMyEOe96R8+3OfkDttv3NuuOZTOewwe8gPdmeccca8R2jhtNNOm8lyBQlmZOvdjv7K9Tttv/McJ2FaNtvrRqs5/fTTZ7Jc/2UDoAVbSEnGGMfOewaAzc4WEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtCBIALWyZ9wD0M8bI4uLivMfgEDfGmPcINGMLCYAWBAmAFgQJgBYECYAWBAmAFgQJgBYECYAWBAmAFgQJgBYECYAWBAmAFgQJgBYECYAWBAmAFgQJgBYECYAWBAmAFgQJgBYECYAWBAmAFgQJgBYECYAWBAmAFrbMewCYlpee8KyDctnAhC0kAFqwhcRBb4yRxcXFHHPMMVNdblXd6jmmaRbzwsFOkGAV044QsHd22QHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQgiAB0IIgAdCCIAHQQo0x5j3DQaGqLhpjPGbecwAcqgQJgBbssgOgBUECoAVBAqAFQZqiqnp2Ve2oqpur6oJVHvPCqhpVdeoGj7fSLKvOW1V3rKrfrqrrquqzVXXpnMZcPtPe5v3Bqnp/Ve2qqiur6olzGnP5TLerqldV1dVLc727qh677P5TquoDVfX5qvqrqrpX13mr6juq6v9U1aeramdV/WFVHdN13j0e12KdW8P3Q6t1bg3zTn2dE6TpuibJi5O8eqU7q+qbkvxAksWNHGov9jbvK5N8XZL7Lv35sxs412pWnLeq7p7kdUlOS3KnJD+f5Peq6hs2fMJb25Lk40kekeSoJL+U5A+q6tiq+vokf5LklzP5/O5I8oZ5Dbpk1XmTHJ3J98SxSe6VZFeS/zmPIZfZ27xJ2q1z+5q32zq3t+/f2axzYwyXKV8y+aF5wQq3X5TkcUmuSnLqvOdcbd4kxye5Icmd5j3bGud9SJJ/2eMxO5M8dN6zrjD75UmenOTpSd6+7PYjktyU5Ph5z7jSvCvc/m1Jds17vn3N23WdW+H7ofU6t8K8M1nnbCFtkKr6gSQ3jzHePO9Z1uDBSa5OcubS7oMrqurJ8x5qL3YkeX9V/buqus3SroObM1l52qiquya5T5L3Jblfkvfsvm+McWOSjyzd3sIe8+7p4avcPjd7ztt9ndtj3vbr3B7zzmSd23LgY7IvVXVkkl9J8j3znmWN7pHkhCR/nGR7kocm+fOqunKM8f65TraCMcaXquo1SX4vye2TfDHJDyz9kG+hqm6b5HeTXDjG+EBVbc3kf5TLfTbJkRs+3Ar2nHeP+05M8sIkT5jHbCtZ4fPbep1bYd5/n8br3ErfD7NY52whbYyFJK8dY1w15znW6qYktyR58Rjji2OMv0nyV0m+d75jrWzpxer/keSRSQ7PZJ/371TVA+c5125VdViS12ay0j576ebPZbLvfbk7ZfLazFytMu/u+45L8hdJ/ssY47I5jPc1Vpl3IU3XuVXmbbvOrTTvrNY5QdoYpyR5TlVdW1XXJrlnJi8O/sKc51rNSpvdnd/S44FJLh1j7BhjfHmM8fdJ3pmkw5GMleRVSe6ayWsbtyzd9b4kD1j2uCOSfFPmvBtsL/Nm6SjAi5OcNcZ47ZxGvJW9zNtyndvLvC3Xub3MO5N1TpCmqKq2VNXtk9wmyW2q6vZVtSWTleOETL6ID8zkaLFnJHnp3IbNXue9NMnHkjx/6THfleRRSd4yx3H3Nu/fJ3nY7v+dVdWDkjwsPV5DelkmR019/xjjpmW3/2mSE6rqyUv/phcmuXzP3WNzsOK8S0dVXZLkt8YYL5/XcCtY7fPbcp3L6vO2XOey+ryzWefmfdTGoXTJZDfB2OOysMLjrkqDI372Nm8mL66/I8mNSa5M8qTm8z47yT9lssvro0l+rsG891qa8QuZ7KLbffmRpftPTfKBTHbX/HWSY7vOm+SMpfuW3/65rvOu8Ni5r3Nr+H5otc6tYd6pr3PeXBWAFuyyA6AFQQKgBUECoAVBAqAFQQKgBUECoAVBgkNcVd2lJuddusMUlvUzVfWr05gL9iRIMGNVdVVV3VRVn1t6K5sLlt5cdS0f+9Sq+tsDHOF5mZyu46alZf51Vf3kHs/zyKr6xBqWdX6SH2lwrikOQYIEG+P7xxhbM3kbmwclef5GPGlV3S7Jj2VyMrUDNsb4QiZvrvqUaSwPlhMk2EBjjGszeX+yr7wrclU9r6o+suxU0E9auv2+SV6e5KFLW1fXL91+u6o6p6o+VlWfrKqX72V33EOSXD/GWMvWz+55dj/f7ssXquqqZQ/56yTft55/N6yFIMEGqqp7JHlsJu8BtttHMnljyqOSnJnkdVV1zJicB+eZSd4xxtg6xti29Pj/nsmJ0h6Y5Lgkd8/kzVlXcv8kH1zPjGOM3c+3NZNTl78zyeuXPeT9WfZO5TAtggQb439V1a4kH0/yL5m8WWmSZIzxh2OMa8bkbfzfkOTDmZxB9GssnQ7g6Ul+dozx6THGrkxORPdDqzzvtqx8jqXfqKrrd1+SvGmVj/+NpY9/wbLbdmUST5gqQYKN8cQxxpGZnNDs+CRfv/uOqnpKVb17WRxOWH7/Hu6S5I5J/mHZ4y9aun0ln8nKZ6F9zhhj2+5Lksfv+YCqesbSvP9xjPHlZXcdmcnZbWGqBAk20JicCfSCJOckXznp3fmZvJX/nZfi8N4ktftD9ljEdZmcruJ+y4Jy1NLutZVcnsnuvXWpqoclOSvJE8YYN+xx932TvGe9y4R9ESTYeC9J8j1V9YAkR2QSnZ1JUlVPy2QLabdPJrlHVR2eJEtbKucn+fXdh15X1d2r6t+u8lzvSrJt6QR7a1JV90zyB0meMsb40AoPeUQmR9rBVAkSbLAxxs4kr0nywjHGlUnOzeTEbJ/M5CCEty17+CWZnNb82qq6bum2X8jkoIi/q6obMjmt+Les8lxfzGSL7EfXMeIpmZyy+o+WHWn3viRZOrvt45JcuI7lwZo4QR8c4qrqLkkuS/KgcevTUO/Psn4myT3HGM+dynCwjCAB0IJddgC0IEgAtCBIALQgSAC0IEgAtCBIALQgSAC0IEgAtPD/AUXlkbNXxf3jAAAAAElFTkSuQmCC","text/plain":["<Figure size 432x576 with 1 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","text/plain":["<Figure size 432x576 with 1 Axes>"]},"metadata":{"needs_background":"light","tags":[]},"output_type":"display_data"}],"source":["sim.analysis.plotSpikeStats();"]},{"cell_type":"markdown","metadata":{"id":"P8deeT59anO8"},"source":["\n","1) Start from netpyne tut_osc_start.py (in editor or Jupyter notebook)\n","\n","2) Rename the 'M' pop to 'I' pop (inhibitory population) \n","\n","3) Add an inhibitory synapse called 'inh' with the same properties as 'exc', except equlibrium potential = -70\n","\n","4) Increase background input ('bkg') rate to 50 Hz and target only the 'S' population: `'conds': {'pop': 'S'}`\n","\n","5) Add spike histogram: `simConfig.analysis['plotSpikeHist'] = {'include': ['S', 'I']}`\n","\n","6) Run the model\n","\n","Why are some cells not spiking?\n","\n","7) Change the connection S->M to S->I (you need to change both the label and the conditions!)\n","\n","8) Reduce the S->I divergence to 1.\n","\n","9) Add a connection I->S with probability: 0.7, weight: 0.02, delay: 5, synMech: 'inh'\n","\n","10) Compare synchrony: add `'syncLines':True` to plotRaster\n","\n","11) Modify parameters (weight, probability, delay, tau2,…) to get different levels of synchrony and oscillation frequencies\n","\n"]}],"metadata":{"colab":{"collapsed_sections":[],"name":"tut_netpyne_osc_start.ipynb","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}
diff --git a/netpyne/tutorials/netpyne-course-2021/tut_netpyne_stim.ipynb b/netpyne/tutorials/netpyne-course-2021/tut_netpyne_stim.ipynb
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-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"tut_netpyne_stim.ipynb","provenance":[{"file_id":"19y6MLKhDAdBxLUZm2sHOuQx-5bqSODs-","timestamp":1621524871397}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"V0cRyhp8YWl2","executionInfo":{"status":"ok","timestamp":1621524900309,"user_tz":240,"elapsed":9440,"user":{"displayName":"Salvador Dura-Bernal","photoUrl":"","userId":"10473966374056868820"}},"outputId":"338493d7-c5b3-497d-e0df-a0c3338fb234"},"source":["!pip install neuron\n","!pip install netpyne\n","import matplotlib"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Collecting neuron\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/14/f4/ea50608c7633c286859d6cce0aad621da22a8da7ff9787efc8bb71fe0597/NEURON-8.0.0-cp37-cp37m-manylinux1_x86_64.whl (12.6MB)\n","\u001b[K     |████████████████████████████████| 12.6MB 297kB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.9.3 in /usr/local/lib/python3.7/dist-packages (from neuron) (1.19.5)\n","Installing collected packages: neuron\n","Successfully installed neuron-8.0.0\n","Collecting netpyne\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9e/24/0f9d685a3fbcbca0d86d9ca6521465c43725d9e23760c91524fe77191f12/netpyne-1.0.0.2-py2.py3-none-any.whl (312kB)\n","\u001b[K     |████████████████████████████████| 317kB 8.8MB/s \n","\u001b[?25hRequirement already satisfied: bokeh in /usr/local/lib/python3.7/dist-packages (from netpyne) (2.3.2)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from netpyne) (1.4.1)\n","Collecting matplotlib-scalebar\n","  Downloading https://files.pythonhosted.org/packages/51/a4/cd254234c35f3591361988e89ab132ee14789f2ebe1ede621d63f5241f00/matplotlib_scalebar-0.7.2-py2.py3-none-any.whl\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from netpyne) (1.19.5)\n","Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from netpyne) (1.1.5)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from netpyne) (3.2.2)\n","Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from netpyne) (0.16.0)\n","Requirement already satisfied: packaging>=16.8 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (20.9)\n","Requirement already satisfied: pillow>=7.1.0 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (7.1.2)\n","Requirement already satisfied: tornado>=5.1 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (5.1.1)\n","Requirement already satisfied: typing-extensions>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (3.7.4.3)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (2.8.1)\n","Requirement already satisfied: Jinja2>=2.9 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (2.11.3)\n","Requirement already satisfied: PyYAML>=3.10 in /usr/local/lib/python3.7/dist-packages (from bokeh->netpyne) (3.13)\n","Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->netpyne) (2018.9)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->netpyne) (0.10.0)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->netpyne) (1.3.1)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->netpyne) (2.4.7)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->bokeh->netpyne) (1.15.0)\n","Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from Jinja2>=2.9->bokeh->netpyne) (2.0.0)\n","Installing collected packages: matplotlib-scalebar, netpyne\n","Successfully installed matplotlib-scalebar-0.7.2 netpyne-1.0.0.2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"f0P--qg5YUT6","executionInfo":{"status":"ok","timestamp":1621526222498,"user_tz":240,"elapsed":1989,"user":{"displayName":"Salvador Dura-Bernal","photoUrl":"","userId":"10473966374056868820"}},"outputId":"5a676705-6b6a-4925-941c-8471215bfb7d"},"source":["from netpyne import specs, sim\n","%matplotlib inline\n","\n","# Network parameters\n","netParams = specs.NetParams()  # object of class NetParams to store the network parameters\n","\n","## Cell params\n","secs = {}   # sections dict\n","secs['soma'] = {'geom': {}, 'mechs': {}}                                                    # soma params dict\n","secs['soma']['geom'] = {'diam': 18.8, 'L': 18.8}                                            # soma geometry\n","secs['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70}      # soma hh mechanism\n","netParams.cellParams['PYR'] = {'secs': secs}                                                # add dict to list of cell params\n","\n","## Population parameters\n","netParams.popParams['S'] = {'cellType': 'PYR', 'numCells': 20}\n","netParams.popParams['M'] = {'cellType': 'PYR', 'numCells': 20}\n","\n","## Synaptic mechanism parameters\n","netParams.synMechParams['exc'] = {'mod': 'Exp2Syn', 'tau1': 0.1, 'tau2': 5.0, 'e': 0}  # excitatory synaptic mechanism\n","\n","\n","## Stimulation parameters\n","netParams.stimSourceParams['Input_1'] = {\n","    'type': 'IClamp', \n","    'del': 300, \n","    'dur': 100, \n","    'amp': 'uniform(0.4,0.5)'}\n","\n","netParams.stimSourceParams['Input_2'] = {\n","    'type': 'VClamp', \n","    'dur': [0,50,200], \n","    'amp': [-60,-30,40], \n","    'gain': 1e5, \n","    'rstim': 1, \n","    'tau1': 0.1, \n","    'tau2': 0}\n","\n","netParams.stimSourceParams['Input_3'] = {\n","    'type': 'AlphaSynapse', \n","    'onset': 'uniform(300,600)', \n","    'tau': 5, \n","    'gmax': '4*post_ynorm', \n","    'e': 0}\n","\n","netParams.stimSourceParams['Input_4'] = {\n","    'type': 'NetStim', \n","    'interval': 'uniform(20,100)', \n","    'start': 600, \n","    'noise': 0.1}\n","\n","\n","netParams.stimTargetParams['Input_1->S'] = {\n","    'source': 'Input_1', \n","    'sec':'soma', \n","    'loc': 0.8, \n","    'conds': {'pop':'S', 'cellList': list(range(15))}}\n","\n","netParams.stimTargetParams['Input_2->S'] = {\n","    'source': 'Input_2', \n","    'sec':'soma', \n","    'loc': 0.5, \n","    'conds': {'pop':'S', 'ynorm': [0,0.5]}}\n","\n","netParams.stimTargetParams['Input_3->M'] = {\n","    'source': 'Input_3', \n","    'sec':'soma', \n","    'loc': 0.2, \n","    'conds': {'pop':'M'}}\n","\n","netParams.stimTargetParams['Input_4->PYR'] = {\n","    'source': 'Input_4', \n","    'sec':'soma', \n","    'loc': 0.5, \n","    'synMech': 'exc',\n","    'weight': '0.1+normal(0.2,0.05)',\n","    'delay': 1,\n","    'conds': {'cellType':'PYR', 'ynorm': [0.6,1.0]}}\n","\n","\n","# Simulation options\n","simConfig = specs.SimConfig()       # object of class SimConfig to store simulation configuration\n","\n","simConfig.duration = 1*1e3          # Duration of the simulation, in ms\n","simConfig.dt = 0.025                # Internal integration timestep to use\n","simConfig.verbose = False           # Show detailed messages\n","simConfig.recordTraces = {'V_soma':{'sec':'soma','loc':0.5,'var':'v'}}  # Dict with traces to record\n","simConfig.recordStep = 0.1          # Step size in ms to save data (eg. V traces, LFP, etc)\n","simConfig.filename = 'tut6'         # Set file output name\n","simConfig.savePickle = False        # Save params, network and sim output to pickle file\n","\n","simConfig.analysis['plotRaster'] = {'saveFig': True, 'orderBy': 'y', 'orderInverse': True}                                 # Plot a raster\n","simConfig.analysis['plotTraces'] = {'include': [('S',0), ('M',0)], 'saveFig': True}  # Plot recorded traces for this list of cells\n","\n","\n","# Create network and run simulation\n","sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig)\n","\n","\n"],"execution_count":15,"outputs":[{"output_type":"stream","text":["\n","Start time:  2021-05-20 15:57:00.677663\n","\n","Creating network of 2 cell populations on 1 hosts...\n","  Number of cells on node 0: 40 \n","  Done; cell creation time = 0.00 s.\n","Making connections...\n","  Number of connections on node 0: 0 \n","  Done; cell connection time = 0.00 s.\n","Adding stims...\n","  Number of stims on node 0: 61 \n","  Done; cell stims creation time = 0.01 s.\n","Recording 2 traces of 1 types on node 0\n","\n","Running simulation for 1000.0 ms...\n","  Done; run time = 0.42 s; real-time ratio: 2.36.\n","\n","Gathering data...\n","  Done; gather time = 0.01 s.\n","\n","Analyzing...\n","  Cells: 40\n","  Connections: 16 (0.40 per cell)\n","  Spikes: 148 (3.70 Hz)\n","  Simulated time: 1.0 s; 1 workers\n","  Run time: 0.42 s\n","  Done; saving time = 0.02 s.\n","Plotting raster...\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["Plotting recorded cell traces ... cell\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 720x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["  Done; plotting time = 1.15 s\n","\n","Total time = 1.62 s\n","\n","End time:  2021-05-20 15:57:02.300830\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"G1Z-Kd9jZ6lp","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621537068406,"user_tz":240,"elapsed":273,"user":{"displayName":"Salvador Dura-Bernal","photoUrl":"","userId":"10473966374056868820"}},"outputId":"1d6881f6-f27c-49b7-adce-32a519e199b7"},"source":["sim.create()\n","\n","sim.net.cells[0].secs['soma']['hObj'](0.5).hh.gna = 10\n","sim.net.cells[0].secs['soma']['mechs']['hh']['gna']\n","\n","sim.net.modifyCells({'secs': {'soma': {'mechs': {'hh':{'gna':10}}}}, 'conds': {'pop': 'S'})\n","sim.net.modifyConns({'secs': {'soma': {'mechs': {'hh':{'gna':10}}}}, 'conds': {'pop': 'S'})\n","\n","sim.simulate()\n","sim.analyze()"],"execution_count":59,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[]"]},"metadata":{"tags":[]},"execution_count":59}]},{"cell_type":"code","metadata":{"id":"UjmJcb2cHyC6"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/netpyne/tutorials/netpyne_tut0.py b/netpyne/tutorials/netpyne_tut0.py
deleted file mode 100644
index 44260abce..000000000
--- a/netpyne/tutorials/netpyne_tut0.py
+++ /dev/null
@@ -1,9 +0,0 @@
-"""
-Install NetPyNE tutorials
-"""
-
-import os
-
-os.system(
-    "mkdir netpyne_tuts && cd netpyne_tuts && export PATH=/bin:/usr/bin && python3 -m venv env && source env/bin/activate && python3 -m pip install --upgrade pip && python3 -m pip install --upgrade ipython && python3 -m pip install --upgrade ipykernel && python3 -m pip install --upgrade jupyter && ipython kernel install --user --name=env && python3 -m pip install --upgrade neuron && git clone https://github.com/Neurosim-lab/netpyne.git && python3 -m pip install -e netpyne && cp -r netpyne/netpyne/tutorials . && cd tutorials && jupyter notebook"
-)
diff --git a/netpyne/tutorials/netpyne_tut1/netpyne_tut1_ide.ipynb b/netpyne/tutorials/netpyne_tut1/netpyne_tut1_ide.ipynb
deleted file mode 100644
index 946173992..000000000
--- a/netpyne/tutorials/netpyne_tut1/netpyne_tut1_ide.ipynb
+++ /dev/null
@@ -1,1192 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Running NetPyNE in a Jupyter Notebook\r\n",
-    "\r\n",
-    "## Preliminaries\r\n",
-    "\r\n",
-    "Hopefully you already completed these preliminaries by following the instructions at https://github.com/Neurosim-lab/netpyne/blob/development/netpyne/tutorials/README.md.  We will now walk you through how we installed the NetPyNE tutorials.\r\n",
-    "\r\n",
-    "We don't want to affect your system in any way, so we will operate from a virtual environment. These preliminary steps must be completed before going through this tutorial. You can't complete the preliminary steps from within Jupyter because you can't enter a virtual environment in Jupyter, you have to switch to a kernel made from your virtual environment.\r\n",
-    "\r\n",
-    "First we will empty your path of all but essentials. Then we will create and activate a virtual environment. Then we will update pip and install some necessary packages in the virtual environment, and finally we will create a kernel from the virtual environment that can be used by Jupyter.  \r\n",
-    "\r\n",
-    "### Create and activate a virtual environment\r\n",
-    "\r\n",
-    "First, open a Terminal and switch to the directory where you downloaded this notebook:\r\n",
-    "\r\n",
-    "        cd netpyne_tuts\r\n",
-    "\r\n",
-    "Next, clear your PATH of all but the essentials. Don't worry, your normal PATH will return the next time you open a Terminal.\r\n",
-    "\r\n",
-    "        export PATH=/bin:/usr/bin\r\n",
-    "    \r\n",
-    "Next, create a virtual environment named \"env\":\r\n",
-    "\r\n",
-    "        python3 -m venv env\r\n",
-    "    \r\n",
-    "Check to see where you are currently running Python from:\r\n",
-    "\r\n",
-    "        which python3\r\n",
-    "    \r\n",
-    "Enter your new virtual environment:\r\n",
-    "\r\n",
-    "        source env/bin/activate\r\n",
-    "    \r\n",
-    "You should see in your prompt that you are in **env**.  \r\n",
-    "\r\n",
-    "Now see where you are running Python from:\r\n",
-    "\r\n",
-    "        which python3\r\n",
-    "    \r\n",
-    "It should come from inside your new virtual environment.  Any changes we make here will only exist in the **env** directory that was created here.  \r\n",
-    "\r\n",
-    "To exit your virtual environment, enter:\r\n",
-    "\r\n",
-    "        deactivate\r\n",
-    "    \r\n",
-    "Your prompt should reflect the change.  To get back in, enter:\r\n",
-    "\r\n",
-    "        source env/bin/activate\r\n",
-    "    \r\n",
-    "### Update pip and install packages\r\n",
-    "\r\n",
-    "We will now update pip and install some necessary packages in the virtual environment. From inside your virtual environment, enter:\r\n",
-    "\r\n",
-    "        python3 -m pip install --upgrade pip\r\n",
-    "        python3 -m pip install --upgrade ipython\r\n",
-    "        python3 -m pip install --upgrade ipykernel\r\n",
-    "        python3 -m pip install --upgrade jupyter\r\n",
-    "    \r\n",
-    "### Make a Jupyter kernel out of this virtual environment\r\n",
-    "\r\n",
-    "Now we will create a kernel that can be used by Jupyter Notebooks.  Enter:\r\n",
-    "\r\n",
-    "        ipython kernel install --user --name=env\r\n",
-    "\r\n",
-    "### Install NEURON and NetPyNE\r\n",
-    "\r\n",
-    "        python3 -m pip install --upgrade neuron\r\n",
-    "        python3 -m pip install --upgrade netpyne\r\n",
-    "    \r\n",
-    "### Launch this notebook in Jupyter Notebook\r\n",
-    "\r\n",
-    "Now we will launch Jupyter from within the virtual environment.  Enter:\r\n",
-    "\r\n",
-    "        jupyter notebook netpyne_tut1.ipynb\r\n",
-    "    \r\n",
-    "This should open a web browser with Jupyter running this notebook.  From the menu bar, click on **Kernel**, hover over **Change kernel** and select **env**.  We are now operating in the virtual environment (see **env** in the upper right instead of **Python 3**) and can begin the tutorial.\r\n",
-    "\r\n",
-    "## Single line command\r\n",
-    "\r\n",
-    "Entering the following single line command should perform all the previous steps and launch this tutorial in a Jupyter notebook in your web browser:\r\n",
-    "\r\n",
-    "        git clone https://github.com/Neurosim-lab/netpyne.git && cd netpyne/netpyne/tutorials/netpyne_tut1 && export PATH=/bin:/usr/bin && python3 -m venv env && source env/bin/activate && python3 -m pip install --upgrade pip && python3 -m pip install --upgrade ipython && python3 -m pip install --upgrade ipykernel && python3 -m pip install --upgrade jupyter && ipython kernel install --user --name=env && jupyter notebook netpyne_tut1.ipynb\r\n",
-    "\r\n",
-    "\r\n",
-    "## To run this again in the future\r\n",
-    "\r\n",
-    "Be sure to enter your virtual environment before running Jupyter!\r\n",
-    "\r\n",
-    "        cd netpyne_tuts\r\n",
-    "        source env/bin/activate\r\n",
-    "        jupyter notebook netpyne_tut1.ipynb\r\n",
-    "    \r\n",
-    "And then make sure you are in the **env** kernel in Jupyter.\r\n",
-    "\r\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tutorial 1 -- a simple network with one population\n",
-    "\n",
-    "Now we are ready to start NetPyNE Tutorial 1, which will create a simple network model we can simulate.  We will create a fairly simple network model of 40 pyramidal-like, two-compartment neurons with standard Hodgkin-Huxley dynamics in the somas and passive dynamics in the dendrites.  We will then connect the neurons randomly with a 10% probability of connection using a standard double-exponential synapse model.  Finally, we will add a current clamp stimulus to one cell to activate the network.  Then we will explore the model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instantiate network parameters and simulation configuration\n",
-    "\n",
-    "You need two things to define a model/simulation in NetPyNE: 1) the parameters of the network and all its components (**netParams**) and 2) the configuration of the simulation (**simConfig**).  These requirements exist as objects in NetPyNE.  Let's instantiate them now."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "from netpyne import specs, sim\n",
-    "netParams = specs.NetParams()\n",
-    "simConfig = specs.SimConfig()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These NetPyNE objects come with a lot of defaults set which you can explore with tab completion, but we'll focus on that more later.\n",
-    "\n",
-    "We are going to plunge ahead and build our model: a simple network of 40 pyramidal-like two-compartment neurons with standard Hodgkin-Huxley dynamics in the soma and passive dynamics in the dendrite.  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create a cell model\n",
-    "\n",
-    "First we will add a cell type to our model by adding a dictionary named **pyr** to the *Cell Parameters* dictionary (**cellParams**) in the *Network Parameters* dictionary (**netParams**).  We will then add an empty dictionary named **secs** to hold our compartments."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "netParams.cellParams['pyr'] = {}\n",
-    "netParams.cellParams['pyr']['secs'] = {}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Specify the soma compartment properties\n",
-    "\n",
-    "Now we will define our **soma**, by adding a **geom** dictionary defining the geometry of the soma and a **mechs** dictionary defining the biophysical mechanics being added to the soma."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "netParams.cellParams['pyr']['secs']['soma'] = {}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "netParams.cellParams['pyr']['secs']['soma']['geom'] = {\n",
-    "    \"diam\": 12,\n",
-    "    \"L\": 12,\n",
-    "    \"Ra\": 100.0,\n",
-    "    \"cm\": 1\n",
-    "    }"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "netParams.cellParams['pyr']['secs']['soma']['mechs'] = {\"hh\": {\n",
-    "    \"gnabar\": 0.12,\n",
-    "    \"gkbar\": 0.036,\n",
-    "    \"gl\": 0.0003,\n",
-    "    \"el\": -54.3\n",
-    "    }}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The **hh** mechanism is builtin to NEURON, but you can see its *.mod* file here:\n",
-    "https://github.com/neuronsimulator/nrn/blob/master/src/nrnoc/hh.mod\n",
-    "\n",
-    "It is the original Hodgkin-Huxley treatment for the set of sodium, potassium, and leakage channels found in the squid giant axon membrane."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Specify the dendrite compartment properties\n",
-    "\n",
-    "Next will do the same thing for the dendrite compartment, but we will do it slightly differently.  We will first build up a **dend** dictionary and then add it to the cell model dictionary **pyr** when we are done."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dend = {}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dend['geom'] = {\"diam\": 1.0,\n",
-    "                \"L\": 200.0,\n",
-    "                \"Ra\": 100.0,\n",
-    "                \"cm\": 1,\n",
-    "               }"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dend['mechs'] = {\"pas\": \n",
-    "                    {\"g\": 0.001,\n",
-    "                     \"e\": -70}\n",
-    "                }"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The **pas** mechanim is a simple leakage channel and is builtin to NEURON.  Its *.mod* file is available here:\n",
-    "https://github.com/neuronsimulator/nrn/blob/master/src/nrnoc/passive.mod\n",
-    "\n",
-    "In order to connect the dendrite compartment to the soma compartment, we must add a **topol** dictionary to our **dend** dictionary."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dend['topol'] = {\"parentSec\": \"soma\",\n",
-    "                 \"parentX\": 1.0,\n",
-    "                 \"childX\": 0,\n",
-    "                }"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With our **dend** section dictionary complete, we must now add it to the **pyr** cell dictionary."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "netParams.cellParams['pyr']['secs']['dend'] = dend"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Our two-compartment cell model is now completely specified.  Our next step is to create a population of these cells."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create a population of cells\n",
-    "\n",
-    "NetPyNE uses *populations* of cells to specify connectivity.  In this tutorial, we will create just one population which we will call **E** (for excitatory).  It will be made of the **pyr** cells we just specified, and we want 40 of them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "netParams.popParams['E'] = {\n",
-    "    \"cellType\": \"pyr\",\n",
-    "    \"numCells\": 40,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create a synaptic model\n",
-    "\n",
-    "We need a synaptic mechanism to connect our cells with.  We will create one called **exc** by adding a dictionary to the *synaptic mechanism parameters* dictionary (**synMechParams**).  The synapse *mod* used (**Exp2Syn**) is a simple double-exponential which is builtin to NEURON.  It's *.mod* file is available here:\n",
-    "https://github.com/neuronsimulator/nrn/blob/master/src/nrnoc/exp2syn.mod"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "netParams.synMechParams['exc'] = {\n",
-    "    \"mod\": \"Exp2Syn\",\n",
-    "    \"tau1\": 0.1,\n",
-    "    \"tau2\": 1.0,\n",
-    "    \"e\": 0\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Connect the cells\n",
-    "\n",
-    "Now we will specify the connectivity in our model by adding an entry to the **connParams** dictionary.  We will call our connectivity rule **E->E** as it will define connectivity from our **E** population to our **E** population.\n",
-    "\n",
-    "We will use the *synMech* **exc**, which we defined above.  For this synaptic mechanism, a *weight* of about **0.005** is appropriate.  These cells will have a 10% probability of getting connected, and will be activated five milliseconds after an action potential occurs in the presynaptic cell.  Synapses will occur on the **dend** *section* at its very end (*location* **1.0**)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "netParams.connParams['E->E'] = {\n",
-    "    \"preConds\": {\"pop\": \"E\"},\n",
-    "    \"postConds\": {\"pop\": \"E\"},\n",
-    "    \"weight\": 0.005,\n",
-    "    \"probability\": 0.1,\n",
-    "    \"delay\": 5.0,\n",
-    "    \"synMech\": \"exc\",\n",
-    "    \"sec\": \"dend\",\n",
-    "    \"loc\": 1.0,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Set up the simulation configuration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "simConfig.filename = \"netpyne_tut1\"\n",
-    "simConfig.duration = 200.0\n",
-    "simConfig.dt = 0.1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will record from from the first cell (**0**) and we will record the voltage in the middle of the soma and the end of the dendrite."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "simConfig.recordCells = [0]\n",
-    "simConfig.recordTraces = {\n",
-    "    \"V_soma\": {\n",
-    "        \"sec\": \"soma\",\n",
-    "        \"loc\": 0.5,\n",
-    "        \"var\": \"v\",\n",
-    "    },\n",
-    "    \"V_dend\": {\n",
-    "        \"sec\": \"dend\",\n",
-    "        \"loc\": 1.0,\n",
-    "        \"var\": \"v\",\n",
-    "    }\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally we will set up some plots to be automatically generated and saved."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "simConfig.analysis = {\n",
-    "    \"plotTraces\": {\n",
-    "        \"include\": [0],\n",
-    "        \"saveFig\": True,\n",
-    "    },\n",
-    "    \"plotRaster\": {\n",
-    "        \"saveFig\": True,\n",
-    "    }\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To see plots in the notebook, we first have to enter the following command."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create, simulate, and analyze the model\n",
-    "\n",
-    "Use one simple command to create, simulate, and analyze the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that there was no spiking in the network, and thus the spike raster was not plotted.  But there should be one new file in your directory: **netpyne_tut1_traces.png**.  Take a look.  Not too interesting, the cell just settles into its resting membrane potential.\n",
-    "\n",
-    "Let's overlay the traces."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, figData = sim.analysis.plotTraces(overlay=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plot the 2D connectivity of the network"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can take a look at the physical layout of our network model.  You can see all the options available for **plot2Dnet** here:\n",
-    "http://netpyne.org/netpyne.analysis.network.html#netpyne.analysis.network.plot2Dnet"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, figData = sim.analysis.plot2Dnet()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plot the connectivity matrix\n",
-    "\n",
-    "You can see all the options available for **plotConn** here:\n",
-    "http://netpyne.org/netpyne.analysis.network.html#netpyne.analysis.network.plotConn"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, figData = sim.analysis.plotConn()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Not very interesting with just one population, but we can also look at the cellular level connectivity."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, figData = sim.analysis.plotConn(feature='weight', groupBy='cell')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Add a stimulation\n",
-    "\n",
-    "We'll need to kickstart this network to see some activity -- let's inject current into one of the cells.  First we need to add an entry to the *Stimulation Source Parameters* dictionary (**stimSourceParams**).  We'll call our stimulation **IClamp1**, and we'll use the standard NEURON *type*: **IClamp**.  The current injection will last for a *duration* of 20 ms, it will start at a *delay* of 5 ms, and it will have an *amplitude* of 0.1 nanoAmps. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "netParams.stimSourceParams['IClamp1'] = {\n",
-    "    \"type\": \"IClamp\",\n",
-    "    \"dur\": 5,\n",
-    "    \"del\": 20,\n",
-    "    \"amp\": 0.1,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we need to add a target for our stimulation.  We do that by adding a dictionary to the *Stimulation Target Parameters* dictionary (**stimTargetParams**).  We'll call this connectivity rule **IClamp1->cell0**, because it will go from the source we just created (**IClamp1**) and the first cell in our population.  The stimulation (current injection in this case) will occur in our **dend** *section* at the very tip (*location* of **1.0**)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "netParams.stimTargetParams['IClamp1->cell0'] = {\n",
-    "    \"source\": \"IClamp1\",\n",
-    "    \"conds\": {\"cellList\": [0]},\n",
-    "    \"sec\": \"dend\",\n",
-    "    \"loc\": 1.0,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create, simulate, and analyze the model\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we see spiking in the network, and the raster plot appears.  Let's improve the plots a little bit."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, figData = sim.analysis.plotTraces(overlay=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, figData = sim.analysis.plotRaster(marker='o', markerSize=50)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can see all of the options available in **plotTraces** here:\n",
-    "http://netpyne.org/netpyne.analysis.traces.html#netpyne.analysis.traces.plotTraces\n",
-    "\n",
-    "You can see all of the options available in **plotRaster** here:\n",
-    "http://netpyne.org/netpyne.analysis.spikes.html#netpyne.analysis.spikes.plotRaster"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plot the connectivity matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, figData = sim.analysis.plotConn()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Record and plot a variety of traces\n",
-    "\n",
-    "Now let's explore the model by recording and plotting a variety of traces.  First let's clear our **recordTraces** dictionary and turn off the automatic raster plot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "simConfig.recordTraces = {}\n",
-    "simConfig.analysis['plotRaster'] = False"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Record and plot the somatic conductances\n",
-    "\n",
-    "Let's record and plot the somatic conductances.  We need to take a look at the **hh** mod file to see what the variables are called.  The file is available here: https://github.com/neuronsimulator/nrn/blob/master/src/nrnoc/hh.mod\n",
-    "\n",
-    "We can see that the conductances are called *gna*, *gk*, and *gl*.  Let's set up recording for these conductances in the middle of the soma."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "simConfig.recordTraces['gNa'] = {'sec': 'soma', 'loc': 0.5, 'mech': 'hh', 'var': 'gna'}\n",
-    "simConfig.recordTraces['gK'] = {'sec': 'soma', 'loc': 0.5, 'mech': 'hh', 'var': 'gk'}\n",
-    "simConfig.recordTraces['gL'] = {'sec': 'soma', 'loc': 0.5, 'mech': 'hh', 'var': 'gl'}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then we can re-run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's zoom in on one spike and overylay the traces."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, figData = sim.analysis.plotTraces(timeRange=[90, 110], overlay=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Record from synapses"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Our synapses are set up to use **Exp2Syn**, which is builtin to NEURON.  Its mod file is available here: https://github.com/neuronsimulator/nrn/blob/master/src/nrnoc/exp2syn.mod\n",
-    "\n",
-    "Looking in the file, we can see that its current variable is called **i**.  Let's record that and the voltage in the dendrite."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "simConfig.recordTraces = {}\n",
-    "simConfig.recordTraces['iSyn0'] = {'sec': 'dend', 'loc': 1.0, 'synMech': 'exc', 'var': 'i'}\n",
-    "simConfig.recordTraces['V_dend'] = {'sec': 'dend', 'loc': 1.0, 'var': 'v'}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "That's the first synapse created in that location, but there are likely multiple synapses.  Let's plot all the synaptic currents entering cell 0.  First we need to see what they are.  The network is defined in **sim.net**.  Type in *sim.net.* and then push *Tab* to see what's available.\n",
-    "\n",
-    "The data for cell 0 is in **sim.net.allCells[0]**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sim.net.allCells[0].keys()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The connections coming onto the cell are in **conns**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sim.net.allCells[0]['conns']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So we want to record six synaptic currents.  Lets do that in a *for loop* at the same time creating a dictionary to hold the synaptic trace names as keys (and later the trace arrays as values)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "simConfig.recordTraces = {}\n",
-    "simConfig.recordTraces['V_soma'] = {'sec': 'soma', 'loc': 0.5, 'var': 'v'}\n",
-    "simConfig.recordTraces['V_dend'] = {'sec': 'dend', 'loc': 1.0, 'var': 'v'}\n",
-    "\n",
-    "syn_plots = {}\n",
-    "for index, presyn in enumerate(sim.net.allCells[0]['conns']):    \n",
-    "    trace_name = 'i_syn_' + str(presyn['preGid'])\n",
-    "    syn_plots[trace_name] = None \n",
-    "    simConfig.recordTraces[trace_name] = {'sec': 'dend', 'loc': 1.0, 'synMech': 'exc', 'var': 'i', 'index': index}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(simConfig.recordTraces)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Extracting recorded data\n",
-    "\n",
-    "Let's make our synaptic currents plot nicer.  We'll make a figure with two plots, the top one will be the somatic and dendritic voltage and the bottom plot will be all of the synaptic currents overlaid.\n",
-    "\n",
-    "First we'll have to extract the data.  Simulation data gets stored in the dictionary **sim.allSimData**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sim.allSimData.keys()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**spkt** is an array of the times of all spikes in the network.  **spkid** is an array of the universal index (GID) of the cell spiking.   **t** is an array of the time for traces.  Our traces appear as we named them, and each is a dictionary with its key being **cell_GID** and its value being the array of the trace."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sim.allSimData.V_soma.keys()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So let's extract our data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "time = sim.allSimData['t']\n",
-    "v_soma = sim.allSimData['V_soma']['cell_0']\n",
-    "v_dend = sim.allSimData['V_dend']['cell_0']\n",
-    "\n",
-    "for syn_plot in syn_plots:\n",
-    "    syn_plots[syn_plot] = sim.allSimData[syn_plot]['cell_0']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And now we can make our custom plot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "fig = plt.figure()\n",
-    "\n",
-    "plt.subplot(211)\n",
-    "plt.plot(time, v_soma, label='v_soma')\n",
-    "plt.plot(time, v_dend, label='v_dend')\n",
-    "plt.legend()\n",
-    "plt.xlabel('Time (ms)')\n",
-    "plt.ylabel('Membrane potential (mV)')\n",
-    "\n",
-    "plt.subplot(212)\n",
-    "for syn_plot in syn_plots:\n",
-    "    plt.plot(time, syn_plots[syn_plot], label=syn_plot)\n",
-    "plt.legend()\n",
-    "plt.xlabel('Time (ms)')\n",
-    "plt.ylabel('Synaptic current (nA)')\n",
-    "\n",
-    "plt.savefig('syn_currents.jpg', dpi=600)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Cleaning up our figure (reducing font size, etc.) will be left as an exercise.  See the **matplotlib** users guide here:\n",
-    "https://matplotlib.org/users/index.html\n",
-    "\n",
-    "Now we will put all of this together into a single file.  But first, let's clear our workspace with the following command."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%reset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## This tutorial in a single Python file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from netpyne import specs, sim\n",
-    "netParams = specs.NetParams()\n",
-    "simConfig = specs.SimConfig()\n",
-    "\n",
-    "# Create a cell type\n",
-    "# ------------------\n",
-    "\n",
-    "netParams.cellParams['pyr'] = {}\n",
-    "netParams.cellParams['pyr']['secs'] = {}\n",
-    "\n",
-    "# Add a soma section\n",
-    "netParams.cellParams['pyr']['secs']['soma'] = {}\n",
-    "netParams.cellParams['pyr']['secs']['soma']['geom'] = {\n",
-    "    \"diam\": 12,\n",
-    "    \"L\": 12,\n",
-    "    \"Ra\": 100.0,\n",
-    "    \"cm\": 1\n",
-    "    }\n",
-    "\n",
-    "# Add hh mechanism to soma\n",
-    "netParams.cellParams['pyr']['secs']['soma']['mechs'] = {\"hh\": {\n",
-    "    \"gnabar\": 0.12,\n",
-    "    \"gkbar\": 0.036,\n",
-    "    \"gl\": 0.0003,\n",
-    "    \"el\": -54.3\n",
-    "    }}\n",
-    "\n",
-    "# Add a dendrite section\n",
-    "dend = {}\n",
-    "dend['geom'] = {\"diam\": 1.0,\n",
-    "                \"L\": 200.0,\n",
-    "                \"Ra\": 100.0,\n",
-    "                \"cm\": 1,\n",
-    "               }\n",
-    "\n",
-    "# Add pas mechanism to dendrite\n",
-    "dend['mechs'] = {\"pas\": \n",
-    "                    {\"g\": 0.001,\n",
-    "                     \"e\": -70}\n",
-    "                }\n",
-    "\n",
-    "# Connect the dendrite to the soma\n",
-    "dend['topol'] = {\"parentSec\": \"soma\",\n",
-    "                 \"parentX\": 1.0,\n",
-    "                 \"childX\": 0,\n",
-    "                }\n",
-    "\n",
-    "# Add the dend dictionary to the cell parameters dictionary\n",
-    "netParams.cellParams['pyr']['secs']['dend'] = dend\n",
-    "\n",
-    "# Create a population of these cells\n",
-    "# ----------------------------------\n",
-    "netParams.popParams['E'] = {\n",
-    "    \"cellType\": \"pyr\",\n",
-    "    \"numCells\": 40,\n",
-    "}\n",
-    "\n",
-    "# Add Exp2Syn synaptic mechanism\n",
-    "# ------------------------------\n",
-    "netParams.synMechParams['exc'] = {\n",
-    "    \"mod\": \"Exp2Syn\",\n",
-    "    \"tau1\": 0.1,\n",
-    "    \"tau2\": 1.0,\n",
-    "    \"e\": 0\n",
-    "}\n",
-    "\n",
-    "# Define the connectivity\n",
-    "# -----------------------\n",
-    "netParams.connParams['E->E'] = {\n",
-    "    \"preConds\": {\"pop\": \"E\"},\n",
-    "    \"postConds\": {\"pop\": \"E\"},\n",
-    "    \"weight\": 0.005,\n",
-    "    \"probability\": 0.1,\n",
-    "    \"delay\": 5.0,\n",
-    "    \"synMech\": \"exc\",\n",
-    "    \"sec\": \"dend\",\n",
-    "    \"loc\": 1.0,\n",
-    "}\n",
-    "\n",
-    "# Add a stimulation\n",
-    "# -----------------\n",
-    "netParams.stimSourceParams['IClamp1'] = {\n",
-    "    \"type\": \"IClamp\",\n",
-    "    \"dur\": 5,\n",
-    "    \"del\": 20,\n",
-    "    \"amp\": 0.1,\n",
-    "}\n",
-    "\n",
-    "# Connect the stimulation\n",
-    "# -----------------------\n",
-    "netParams.stimTargetParams['IClamp1->cell0'] = {\n",
-    "    \"source\": \"IClamp1\",\n",
-    "    \"conds\": {\"cellList\": [0]},\n",
-    "    \"sec\": \"dend\",\n",
-    "    \"loc\": 1.0,\n",
-    "}\n",
-    "\n",
-    "# Set up the simulation configuration\n",
-    "# -----------------------------------\n",
-    "\n",
-    "simConfig.filename = \"netpyne_tut1\"\n",
-    "simConfig.duration = 200.0\n",
-    "simConfig.dt = 0.1\n",
-    "\n",
-    "# Record from cell 0\n",
-    "simConfig.recordCells = [0]\n",
-    "\n",
-    "# Record the voltage at the soma and the dendrite\n",
-    "simConfig.recordTraces = {\n",
-    "    \"V_soma\": {\n",
-    "        \"sec\": \"soma\",\n",
-    "        \"loc\": 0.5,\n",
-    "        \"var\": \"v\",\n",
-    "    },\n",
-    "    \"V_dend\": {\n",
-    "        \"sec\": \"dend\",\n",
-    "        \"loc\": 1.0,\n",
-    "        \"var\": \"v\",\n",
-    "    }\n",
-    "}\n",
-    "\n",
-    "# Record somatic conductances\n",
-    "#simConfig.recordTraces['gNa'] = {'sec': 'soma', 'loc': 0.5, 'mech': 'hh', 'var': 'gna'}\n",
-    "#simConfig.recordTraces['gK'] = {'sec': 'soma', 'loc': 0.5, 'mech': 'hh', 'var': 'gk'}\n",
-    "#simConfig.recordTraces['gL'] = {'sec': 'soma', 'loc': 0.5, 'mech': 'hh', 'var': 'gl'}\n",
-    "\n",
-    "# Automatically generate some figures\n",
-    "simConfig.analysis = {\n",
-    "    \"plotTraces\": {\n",
-    "        \"include\": [0],\n",
-    "        \"saveFig\": True,\n",
-    "        \"overlay\": True,\n",
-    "    },\n",
-    "    \"plotRaster\": {\n",
-    "        \"saveFig\": True,\n",
-    "        \"marker\": \"o\",\n",
-    "        \"markerSize\": 50,\n",
-    "    },\n",
-    "    \"plotConn\": {\n",
-    "        \"saveFig\": True,\n",
-    "        \"feature\": \"weight\",\n",
-    "        \"groupby\": \"cell\",\n",
-    "        \"markerSize\": 50,\n",
-    "    },\n",
-    "    \"plot2Dnet\": {\n",
-    "        \"saveFig\": True,\n",
-    "    },\n",
-    "}\n",
-    "\n",
-    "\n",
-    "# Create, simulate, and analyze the model\n",
-    "# ---------------------------------------\n",
-    "sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig)\n",
-    "\n",
-    "\n",
-    "# Set up the recording for the synaptic current plots\n",
-    "syn_plots = {}\n",
-    "for index, presyn in enumerate(sim.net.allCells[0]['conns']):    \n",
-    "    trace_name = 'i_syn_' + str(presyn['preGid'])\n",
-    "    syn_plots[trace_name] = None \n",
-    "    simConfig.recordTraces[trace_name] = {'sec': 'dend', 'loc': 1.0, 'synMech': 'exc', 'var': 'i', 'index': index}\n",
-    "\n",
-    "    \n",
-    "# Create, simulate, and analyze the model\n",
-    "# ---------------------------------------\n",
-    "sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig)\n",
-    "    \n",
-    "\n",
-    "# Extract the data\n",
-    "# ----------------\n",
-    "time = sim.allSimData['t']\n",
-    "v_soma = sim.allSimData['V_soma']['cell_0']\n",
-    "v_dend = sim.allSimData['V_dend']['cell_0']\n",
-    "\n",
-    "for syn_plot in syn_plots:\n",
-    "    syn_plots[syn_plot] = sim.allSimData[syn_plot]['cell_0']\n",
-    "\n",
-    "    \n",
-    "# Plot our custom figure\n",
-    "# ----------------------\n",
-    "import matplotlib.pyplot as plt\n",
-    "fig = plt.figure()\n",
-    "\n",
-    "plt.subplot(211)\n",
-    "plt.plot(time, v_soma, label='v_soma')\n",
-    "plt.plot(time, v_dend, label='v_dend')\n",
-    "plt.legend()\n",
-    "plt.xlabel('Time (ms)')\n",
-    "plt.ylabel('Membrane potential (mV)')\n",
-    "\n",
-    "plt.subplot(212)\n",
-    "for syn_plot in syn_plots:\n",
-    "    plt.plot(time, syn_plots[syn_plot], label=syn_plot)\n",
-    "plt.legend()\n",
-    "plt.xlabel('Time (ms)')\n",
-    "plt.ylabel('Synaptic current (nA)')\n",
-    "\n",
-    "plt.savefig('syn_currents.jpg', dpi=600)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.8.5 64-bit",
-   "name": "python385jvsc74a57bd08bd251fc099d12831c2d42b0b064725cbd8ed83490264d7a2d571a2f47e12950"
-  },
-  "language_info": {
-   "name": "python",
-   "version": ""
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
\ No newline at end of file
diff --git a/netpyne/tutorials/netpyne_tut1/netpyne_tut1_web.ipynb b/netpyne/tutorials/netpyne_tut1/netpyne_tut1_web.ipynb
deleted file mode 100644
index 239b11fef..000000000
--- a/netpyne/tutorials/netpyne_tut1/netpyne_tut1_web.ipynb
+++ /dev/null
@@ -1,1201 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Running NetPyNE in a Jupyter Notebook\n",
-    "\n",
-    "## Preliminaries\n",
-    "\n",
-    "Hopefully you already completed these preliminaries by following the instructions at https://github.com/Neurosim-lab/netpyne/blob/development/netpyne/tutorials/README.md.  We will now walk you through how we installed the NetPyNE tutorials.\n",
-    "\n",
-    "We don't want to affect your system in any way, so we will operate from a virtual environment. These preliminary steps must be completed before going through this tutorial. You can't complete the preliminary steps from within Jupyter because you can't enter a virtual environment in Jupyter, you have to switch to a kernel made from your virtual environment.\n",
-    "\n",
-    "First we will empty your path of all but essentials. Then we will create and activate a virtual environment. Then we will update pip and install some necessary packages in the virtual environment, and finally we will create a kernel from the virtual environment that can be used by Jupyter.  \n",
-    "\n",
-    "### Create and activate a virtual environment\n",
-    "\n",
-    "First, open a Terminal and switch to the directory where you downloaded this notebook:\n",
-    "\n",
-    "    cd netpyne_tuts\n",
-    "\n",
-    "Next, clear your PATH of all but the essentials. Don't worry, your normal PATH will return the next time you open a Terminal.\n",
-    "\n",
-    "    export PATH=/bin:/usr/bin\n",
-    "    \n",
-    "Next, create a virtual environment named \"env\":\n",
-    "\n",
-    "    python3 -m venv env\n",
-    "    \n",
-    "Check to see where you are currently running Python from:\n",
-    "\n",
-    "    which python3\n",
-    "    \n",
-    "Enter your new virtual environment:\n",
-    "\n",
-    "    source env/bin/activate\n",
-    "    \n",
-    "You should see in your prompt that you are in **env**.  \n",
-    "\n",
-    "Now see where you are running Python from:\n",
-    "\n",
-    "    which python3\n",
-    "    \n",
-    "It should come from inside your new virtual environment.  Any changes we make here will only exist in the **env** directory that was created here.  \n",
-    "\n",
-    "To exit your virtual environment, enter:\n",
-    "\n",
-    "    deactivate\n",
-    "    \n",
-    "Your prompt should reflect the change.  To get back in, enter:\n",
-    "\n",
-    "    source env/bin/activate\n",
-    "    \n",
-    "### Update pip and install packages\n",
-    "\n",
-    "We will now update pip and install some necessary packages in the virtual environment. From inside your virtual environment, enter:\n",
-    "\n",
-    "    python3 -m pip install --upgrade pip\n",
-    "    python3 -m pip install --upgrade ipython\n",
-    "    python3 -m pip install --upgrade ipykernel\n",
-    "    python3 -m pip install --upgrade jupyter\n",
-    "    \n",
-    "### Make a Jupyter kernel out of this virtual environment\n",
-    "\n",
-    "Now we will create a kernel that can be used by Jupyter Notebooks.  Enter:\n",
-    "\n",
-    "    ipython kernel install --user --name=env\n",
-    "\n",
-    "### Install NEURON and NetPyNE\n",
-    "\n",
-    "    python3 -m pip install --upgrade neuron\n",
-    "    python3 -m pip install --upgrade netpyne\n",
-    "    \n",
-    "### Launch this notebook in Jupyter Notebook\n",
-    "\n",
-    "Now we will launch Jupyter from within the virtual environment.  Enter:\n",
-    "\n",
-    "    jupyter notebook netpyne_tut1.ipynb\n",
-    "    \n",
-    "This should open a web browser with Jupyter running this notebook.  From the menu bar, click on **Kernel**, hover over **Change kernel** and select **env**.  We are now operating in the virtual environment (see **env** in the upper right instead of **Python 3**) and can begin the tutorial.\n",
-    "\n",
-    "## Single line command\n",
-    "\n",
-    "Entering the following single line command should perform all the previous steps and launch this tutorial in a Jupyter notebook in your web browser:\n",
-    "\n",
-    "    git clone https://github.com/Neurosim-lab/netpyne.git && cd netpyne/netpyne/tutorials/netpyne_tut1 && export PATH=/bin:/usr/bin && python3 -m venv env && source env/bin/activate && python3 -m pip install --upgrade pip && python3 -m pip install --upgrade ipython && python3 -m pip install --upgrade ipykernel && python3 -m pip install --upgrade jupyter && ipython kernel install --user --name=env && jupyter notebook netpyne_tut1.ipynb\n",
-    "\n",
-    "\n",
-    "## To run this again in the future\n",
-    "\n",
-    "Be sure to enter your virtual environment before running Jupyter!\n",
-    "\n",
-    "    cd netpyne_tuts\n",
-    "    source env/bin/activate\n",
-    "    jupyter notebook netpyne_tut1.ipynb\n",
-    "    \n",
-    "And then make sure you are in the **env** kernel in Jupyter.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tutorial 1 -- a simple network with one population\n",
-    "\n",
-    "Now we are ready to start NetPyNE Tutorial 1, which will create a simple network model we can simulate.  We will create a fairly simple network model of 40 pyramidal-like, two-compartment neurons with standard Hodgkin-Huxley dynamics in the somas and passive dynamics in the dendrites.  We will then connect the neurons randomly with a 10% probability of connection using a standard double-exponential synapse model.  Finally, we will add a current clamp stimulus to one cell to activate the network.  Then we will explore the model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Instantiate network parameters and simulation configuration\n",
-    "\n",
-    "You need two things to define a model/simulation in NetPyNE: 1) the parameters of the network and all its components (**netParams**) and 2) the configuration of the simulation (**simConfig**).  These requirements exist as objects in NetPyNE.  Let's instantiate them now."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "from netpyne import specs, sim\n",
-    "netParams = specs.NetParams()\n",
-    "simConfig = specs.SimConfig()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These NetPyNE objects come with a lot of defaults set which you can explore with tab completion, but we'll focus on that more later.\n",
-    "\n",
-    "We are going to plunge ahead and build our model: a simple network of 40 pyramidal-like two-compartment neurons with standard Hodgkin-Huxley dynamics in the soma and passive dynamics in the dendrite.  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create a cell model\n",
-    "\n",
-    "First we will add a cell type to our model by adding a dictionary named **pyr** to the *Cell Parameters* dictionary (**cellParams**) in the *Network Parameters* dictionary (**netParams**).  We will then add an empty dictionary named **secs** to hold our compartments."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "netParams.cellParams['pyr'] = {}\n",
-    "netParams.cellParams['pyr']['secs'] = {}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Specify the soma compartment properties\n",
-    "\n",
-    "Now we will define our **soma**, by adding a **geom** dictionary defining the geometry of the soma and a **mechs** dictionary defining the biophysical mechanics being added to the soma."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "netParams.cellParams['pyr']['secs']['soma'] = {}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "netParams.cellParams['pyr']['secs']['soma']['geom'] = {\n",
-    "    \"diam\": 12,\n",
-    "    \"L\": 12,\n",
-    "    \"Ra\": 100.0,\n",
-    "    \"cm\": 1\n",
-    "    }"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "netParams.cellParams['pyr']['secs']['soma']['mechs'] = {\"hh\": {\n",
-    "    \"gnabar\": 0.12,\n",
-    "    \"gkbar\": 0.036,\n",
-    "    \"gl\": 0.0003,\n",
-    "    \"el\": -54.3\n",
-    "    }}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The **hh** mechanism is builtin to NEURON, but you can see its *.mod* file here:\n",
-    "https://github.com/neuronsimulator/nrn/blob/master/src/nrnoc/hh.mod\n",
-    "\n",
-    "It is the original Hodgkin-Huxley treatment for the set of sodium, potassium, and leakage channels found in the squid giant axon membrane."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Specify the dendrite compartment properties\n",
-    "\n",
-    "Next will do the same thing for the dendrite compartment, but we will do it slightly differently.  We will first build up a **dend** dictionary and then add it to the cell model dictionary **pyr** when we are done."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dend = {}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dend['geom'] = {\"diam\": 1.0,\n",
-    "                \"L\": 200.0,\n",
-    "                \"Ra\": 100.0,\n",
-    "                \"cm\": 1,\n",
-    "               }"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dend['mechs'] = {\"pas\": \n",
-    "                    {\"g\": 0.001,\n",
-    "                     \"e\": -70}\n",
-    "                }"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The **pas** mechanim is a simple leakage channel and is builtin to NEURON.  Its *.mod* file is available here:\n",
-    "https://github.com/neuronsimulator/nrn/blob/master/src/nrnoc/passive.mod\n",
-    "\n",
-    "In order to connect the dendrite compartment to the soma compartment, we must add a **topol** dictionary to our **dend** dictionary."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dend['topol'] = {\"parentSec\": \"soma\",\n",
-    "                 \"parentX\": 1.0,\n",
-    "                 \"childX\": 0,\n",
-    "                }"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With our **dend** section dictionary complete, we must now add it to the **pyr** cell dictionary."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "netParams.cellParams['pyr']['secs']['dend'] = dend"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Our two-compartment cell model is now completely specified.  Our next step is to create a population of these cells."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create a population of cells\n",
-    "\n",
-    "NetPyNE uses *populations* of cells to specify connectivity.  In this tutorial, we will create just one population which we will call **E** (for excitatory).  It will be made of the **pyr** cells we just specified, and we want 40 of them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "netParams.popParams['E'] = {\n",
-    "    \"cellType\": \"pyr\",\n",
-    "    \"numCells\": 40,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create a synaptic model\n",
-    "\n",
-    "We need a synaptic mechanism to connect our cells with.  We will create one called **exc** by adding a dictionary to the *synaptic mechanism parameters* dictionary (**synMechParams**).  The synapse *mod* used (**Exp2Syn**) is a simple double-exponential which is builtin to NEURON.  It's *.mod* file is available here:\n",
-    "https://github.com/neuronsimulator/nrn/blob/master/src/nrnoc/exp2syn.mod"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "netParams.synMechParams['exc'] = {\n",
-    "    \"mod\": \"Exp2Syn\",\n",
-    "    \"tau1\": 0.1,\n",
-    "    \"tau2\": 1.0,\n",
-    "    \"e\": 0\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Connect the cells\n",
-    "\n",
-    "Now we will specify the connectivity in our model by adding an entry to the **connParams** dictionary.  We will call our connectivity rule **E->E** as it will define connectivity from our **E** population to our **E** population.\n",
-    "\n",
-    "We will use the *synMech* **exc**, which we defined above.  For this synaptic mechanism, a *weight* of about **0.005** is appropriate.  These cells will have a 10% probability of getting connected, and will be activated five milliseconds after an action potential occurs in the presynaptic cell.  Synapses will occur on the **dend** *section* at its very end (*location* **1.0**)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "netParams.connParams['E->E'] = {\n",
-    "    \"preConds\": {\"pop\": \"E\"},\n",
-    "    \"postConds\": {\"pop\": \"E\"},\n",
-    "    \"weight\": 0.005,\n",
-    "    \"probability\": 0.1,\n",
-    "    \"delay\": 5.0,\n",
-    "    \"synMech\": \"exc\",\n",
-    "    \"sec\": \"dend\",\n",
-    "    \"loc\": 1.0,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Set up the simulation configuration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "simConfig.filename = \"netpyne_tut1\"\n",
-    "simConfig.duration = 200.0\n",
-    "simConfig.dt = 0.1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will record from from the first cell (**0**) and we will record the voltage in the middle of the soma and the end of the dendrite."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "simConfig.recordCells = [0]\n",
-    "simConfig.recordTraces = {\n",
-    "    \"V_soma\": {\n",
-    "        \"sec\": \"soma\",\n",
-    "        \"loc\": 0.5,\n",
-    "        \"var\": \"v\",\n",
-    "    },\n",
-    "    \"V_dend\": {\n",
-    "        \"sec\": \"dend\",\n",
-    "        \"loc\": 1.0,\n",
-    "        \"var\": \"v\",\n",
-    "    }\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally we will set up some plots to be automatically generated and saved."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "simConfig.analysis = {\n",
-    "    \"plotTraces\": {\n",
-    "        \"include\": [0],\n",
-    "        \"saveFig\": True,\n",
-    "    },\n",
-    "    \"plotRaster\": {\n",
-    "        \"saveFig\": True,\n",
-    "    }\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To see plots in the notebook, we first have to enter the following command."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create, simulate, and analyze the model\n",
-    "\n",
-    "Use one simple command to create, simulate, and analyze the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that there was no spiking in the network, and thus the spike raster was not plotted.  But there should be one new file in your directory: **netpyne_tut1_traces.png**.  Take a look.  Not too interesting, the cell just settles into its resting membrane potential.\n",
-    "\n",
-    "Let's overlay the traces."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, figData = sim.analysis.plotTraces(overlay=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plot the 2D connectivity of the network"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can take a look at the physical layout of our network model.  You can see all the options available for **plot2Dnet** here:\n",
-    "http://netpyne.org/netpyne.analysis.network.html#netpyne.analysis.network.plot2Dnet"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, figData = sim.analysis.plot2Dnet()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plot the connectivity matrix\n",
-    "\n",
-    "You can see all the options available for **plotConn** here:\n",
-    "http://netpyne.org/netpyne.analysis.network.html#netpyne.analysis.network.plotConn"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, figData = sim.analysis.plotConn()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Not very interesting with just one population, but we can also look at the cellular level connectivity."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, figData = sim.analysis.plotConn(feature='weight', groupBy='cell')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Add a stimulation\n",
-    "\n",
-    "We'll need to kickstart this network to see some activity -- let's inject current into one of the cells.  First we need to add an entry to the *Stimulation Source Parameters* dictionary (**stimSourceParams**).  We'll call our stimulation **IClamp1**, and we'll use the standard NEURON *type*: **IClamp**.  The current injection will last for a *duration* of 20 ms, it will start at a *delay* of 5 ms, and it will have an *amplitude* of 0.1 nanoAmps. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "netParams.stimSourceParams['IClamp1'] = {\n",
-    "    \"type\": \"IClamp\",\n",
-    "    \"dur\": 5,\n",
-    "    \"del\": 20,\n",
-    "    \"amp\": 0.1,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we need to add a target for our stimulation.  We do that by adding a dictionary to the *Stimulation Target Parameters* dictionary (**stimTargetParams**).  We'll call this connectivity rule **IClamp1->cell0**, because it will go from the source we just created (**IClamp1**) and the first cell in our population.  The stimulation (current injection in this case) will occur in our **dend** *section* at the very tip (*location* of **1.0**)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "netParams.stimTargetParams['IClamp1->cell0'] = {\n",
-    "    \"source\": \"IClamp1\",\n",
-    "    \"conds\": {\"cellList\": [0]},\n",
-    "    \"sec\": \"dend\",\n",
-    "    \"loc\": 1.0,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create, simulate, and analyze the model\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we see spiking in the network, and the raster plot appears.  Let's improve the plots a little bit."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, figData = sim.analysis.plotTraces(overlay=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, figData = sim.analysis.plotRaster(marker='o', markerSize=50)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can see all of the options available in **plotTraces** here:\n",
-    "http://netpyne.org/netpyne.analysis.traces.html#netpyne.analysis.traces.plotTraces\n",
-    "\n",
-    "You can see all of the options available in **plotRaster** here:\n",
-    "http://netpyne.org/netpyne.analysis.spikes.html#netpyne.analysis.spikes.plotRaster"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plot the connectivity matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, figData = sim.analysis.plotConn()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Record and plot a variety of traces\n",
-    "\n",
-    "Now let's explore the model by recording and plotting a variety of traces.  First let's clear our **recordTraces** dictionary and turn off the automatic raster plot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "simConfig.recordTraces = {}\n",
-    "simConfig.analysis['plotRaster'] = False"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Record and plot the somatic conductances\n",
-    "\n",
-    "Let's record and plot the somatic conductances.  We need to take a look at the **hh** mod file to see what the variables are called.  The file is available here: https://github.com/neuronsimulator/nrn/blob/master/src/nrnoc/hh.mod\n",
-    "\n",
-    "We can see that the conductances are called *gna*, *gk*, and *gl*.  Let's set up recording for these conductances in the middle of the soma."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "simConfig.recordTraces['gNa'] = {'sec': 'soma', 'loc': 0.5, 'mech': 'hh', 'var': 'gna'}\n",
-    "simConfig.recordTraces['gK'] = {'sec': 'soma', 'loc': 0.5, 'mech': 'hh', 'var': 'gk'}\n",
-    "simConfig.recordTraces['gL'] = {'sec': 'soma', 'loc': 0.5, 'mech': 'hh', 'var': 'gl'}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then we can re-run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's zoom in on one spike and overylay the traces."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, figData = sim.analysis.plotTraces(timeRange=[90, 110], overlay=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Record from synapses"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Our synapses are set up to use **Exp2Syn**, which is builtin to NEURON.  Its mod file is available here: https://github.com/neuronsimulator/nrn/blob/master/src/nrnoc/exp2syn.mod\n",
-    "\n",
-    "Looking in the file, we can see that its current variable is called **i**.  Let's record that and the voltage in the dendrite."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "simConfig.recordTraces = {}\n",
-    "simConfig.recordTraces['iSyn0'] = {'sec': 'dend', 'loc': 1.0, 'synMech': 'exc', 'var': 'i'}\n",
-    "simConfig.recordTraces['V_dend'] = {'sec': 'dend', 'loc': 1.0, 'var': 'v'}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "That's the first synapse created in that location, but there are likely multiple synapses.  Let's plot all the synaptic currents entering cell 0.  First we need to see what they are.  The network is defined in **sim.net**.  Type in *sim.net.* and then push *Tab* to see what's available.\n",
-    "\n",
-    "The data for cell 0 is in **sim.net.allCells[0]**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sim.net.allCells[0].keys()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The connections coming onto the cell are in **conns**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sim.net.allCells[0]['conns']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So we want to record six synaptic currents.  Lets do that in a *for loop* at the same time creating a dictionary to hold the synaptic trace names as keys (and later the trace arrays as values)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "simConfig.recordTraces = {}\n",
-    "simConfig.recordTraces['V_soma'] = {'sec': 'soma', 'loc': 0.5, 'var': 'v'}\n",
-    "simConfig.recordTraces['V_dend'] = {'sec': 'dend', 'loc': 1.0, 'var': 'v'}\n",
-    "\n",
-    "syn_plots = {}\n",
-    "for index, presyn in enumerate(sim.net.allCells[0]['conns']):    \n",
-    "    trace_name = 'i_syn_' + str(presyn['preGid'])\n",
-    "    syn_plots[trace_name] = None \n",
-    "    simConfig.recordTraces[trace_name] = {'sec': 'dend', 'loc': 1.0, 'synMech': 'exc', 'var': 'i', 'index': index}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(simConfig.recordTraces)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Extracting recorded data\n",
-    "\n",
-    "Let's make our synaptic currents plot nicer.  We'll make a figure with two plots, the top one will be the somatic and dendritic voltage and the bottom plot will be all of the synaptic currents overlaid.\n",
-    "\n",
-    "First we'll have to extract the data.  Simulation data gets stored in the dictionary **sim.allSimData**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sim.allSimData.keys()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**spkt** is an array of the times of all spikes in the network.  **spkid** is an array of the universal index (GID) of the cell spiking.   **t** is an array of the time for traces.  Our traces appear as we named them, and each is a dictionary with its key being **cell_GID** and its value being the array of the trace."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sim.allSimData.V_soma.keys()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So let's extract our data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "time = sim.allSimData['t']\n",
-    "v_soma = sim.allSimData['V_soma']['cell_0']\n",
-    "v_dend = sim.allSimData['V_dend']['cell_0']\n",
-    "\n",
-    "for syn_plot in syn_plots:\n",
-    "    syn_plots[syn_plot] = sim.allSimData[syn_plot]['cell_0']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And now we can make our custom plot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "fig = plt.figure()\n",
-    "\n",
-    "plt.subplot(211)\n",
-    "plt.plot(time, v_soma, label='v_soma')\n",
-    "plt.plot(time, v_dend, label='v_dend')\n",
-    "plt.legend()\n",
-    "plt.xlabel('Time (ms)')\n",
-    "plt.ylabel('Membrane potential (mV)')\n",
-    "\n",
-    "plt.subplot(212)\n",
-    "for syn_plot in syn_plots:\n",
-    "    plt.plot(time, syn_plots[syn_plot], label=syn_plot)\n",
-    "plt.legend()\n",
-    "plt.xlabel('Time (ms)')\n",
-    "plt.ylabel('Synaptic current (nA)')\n",
-    "\n",
-    "plt.savefig('syn_currents.jpg', dpi=600)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Cleaning up our figure (reducing font size, etc.) will be left as an exercise.  See the **matplotlib** users guide here:\n",
-    "https://matplotlib.org/users/index.html\n",
-    "\n",
-    "Now we will put all of this together into a single file.  But first, let's clear our workspace with the following command."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%reset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## This tutorial in a single Python file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from netpyne import specs, sim\n",
-    "netParams = specs.NetParams()\n",
-    "simConfig = specs.SimConfig()\n",
-    "\n",
-    "# Create a cell type\n",
-    "# ------------------\n",
-    "\n",
-    "netParams.cellParams['pyr'] = {}\n",
-    "netParams.cellParams['pyr']['secs'] = {}\n",
-    "\n",
-    "# Add a soma section\n",
-    "netParams.cellParams['pyr']['secs']['soma'] = {}\n",
-    "netParams.cellParams['pyr']['secs']['soma']['geom'] = {\n",
-    "    \"diam\": 12,\n",
-    "    \"L\": 12,\n",
-    "    \"Ra\": 100.0,\n",
-    "    \"cm\": 1\n",
-    "    }\n",
-    "\n",
-    "# Add hh mechanism to soma\n",
-    "netParams.cellParams['pyr']['secs']['soma']['mechs'] = {\"hh\": {\n",
-    "    \"gnabar\": 0.12,\n",
-    "    \"gkbar\": 0.036,\n",
-    "    \"gl\": 0.0003,\n",
-    "    \"el\": -54.3\n",
-    "    }}\n",
-    "\n",
-    "# Add a dendrite section\n",
-    "dend = {}\n",
-    "dend['geom'] = {\"diam\": 1.0,\n",
-    "                \"L\": 200.0,\n",
-    "                \"Ra\": 100.0,\n",
-    "                \"cm\": 1,\n",
-    "               }\n",
-    "\n",
-    "# Add pas mechanism to dendrite\n",
-    "dend['mechs'] = {\"pas\": \n",
-    "                    {\"g\": 0.001,\n",
-    "                     \"e\": -70}\n",
-    "                }\n",
-    "\n",
-    "# Connect the dendrite to the soma\n",
-    "dend['topol'] = {\"parentSec\": \"soma\",\n",
-    "                 \"parentX\": 1.0,\n",
-    "                 \"childX\": 0,\n",
-    "                }\n",
-    "\n",
-    "# Add the dend dictionary to the cell parameters dictionary\n",
-    "netParams.cellParams['pyr']['secs']['dend'] = dend\n",
-    "\n",
-    "# Create a population of these cells\n",
-    "# ----------------------------------\n",
-    "netParams.popParams['E'] = {\n",
-    "    \"cellType\": \"pyr\",\n",
-    "    \"numCells\": 40,\n",
-    "}\n",
-    "\n",
-    "# Add Exp2Syn synaptic mechanism\n",
-    "# ------------------------------\n",
-    "netParams.synMechParams['exc'] = {\n",
-    "    \"mod\": \"Exp2Syn\",\n",
-    "    \"tau1\": 0.1,\n",
-    "    \"tau2\": 1.0,\n",
-    "    \"e\": 0\n",
-    "}\n",
-    "\n",
-    "# Define the connectivity\n",
-    "# -----------------------\n",
-    "netParams.connParams['E->E'] = {\n",
-    "    \"preConds\": {\"pop\": \"E\"},\n",
-    "    \"postConds\": {\"pop\": \"E\"},\n",
-    "    \"weight\": 0.005,\n",
-    "    \"probability\": 0.1,\n",
-    "    \"delay\": 5.0,\n",
-    "    \"synMech\": \"exc\",\n",
-    "    \"sec\": \"dend\",\n",
-    "    \"loc\": 1.0,\n",
-    "}\n",
-    "\n",
-    "# Add a stimulation\n",
-    "# -----------------\n",
-    "netParams.stimSourceParams['IClamp1'] = {\n",
-    "    \"type\": \"IClamp\",\n",
-    "    \"dur\": 5,\n",
-    "    \"del\": 20,\n",
-    "    \"amp\": 0.1,\n",
-    "}\n",
-    "\n",
-    "# Connect the stimulation\n",
-    "# -----------------------\n",
-    "netParams.stimTargetParams['IClamp1->cell0'] = {\n",
-    "    \"source\": \"IClamp1\",\n",
-    "    \"conds\": {\"cellList\": [0]},\n",
-    "    \"sec\": \"dend\",\n",
-    "    \"loc\": 1.0,\n",
-    "}\n",
-    "\n",
-    "# Set up the simulation configuration\n",
-    "# -----------------------------------\n",
-    "\n",
-    "simConfig.filename = \"netpyne_tut1\"\n",
-    "simConfig.duration = 200.0\n",
-    "simConfig.dt = 0.1\n",
-    "\n",
-    "# Record from cell 0\n",
-    "simConfig.recordCells = [0]\n",
-    "\n",
-    "# Record the voltage at the soma and the dendrite\n",
-    "simConfig.recordTraces = {\n",
-    "    \"V_soma\": {\n",
-    "        \"sec\": \"soma\",\n",
-    "        \"loc\": 0.5,\n",
-    "        \"var\": \"v\",\n",
-    "    },\n",
-    "    \"V_dend\": {\n",
-    "        \"sec\": \"dend\",\n",
-    "        \"loc\": 1.0,\n",
-    "        \"var\": \"v\",\n",
-    "    }\n",
-    "}\n",
-    "\n",
-    "# Record somatic conductances\n",
-    "#simConfig.recordTraces['gNa'] = {'sec': 'soma', 'loc': 0.5, 'mech': 'hh', 'var': 'gna'}\n",
-    "#simConfig.recordTraces['gK'] = {'sec': 'soma', 'loc': 0.5, 'mech': 'hh', 'var': 'gk'}\n",
-    "#simConfig.recordTraces['gL'] = {'sec': 'soma', 'loc': 0.5, 'mech': 'hh', 'var': 'gl'}\n",
-    "\n",
-    "# Automatically generate some figures\n",
-    "simConfig.analysis = {\n",
-    "    \"plotTraces\": {\n",
-    "        \"include\": [0],\n",
-    "        \"saveFig\": True,\n",
-    "        \"overlay\": True,\n",
-    "    },\n",
-    "    \"plotRaster\": {\n",
-    "        \"saveFig\": True,\n",
-    "        \"marker\": \"o\",\n",
-    "        \"markerSize\": 50,\n",
-    "    },\n",
-    "    \"plotConn\": {\n",
-    "        \"saveFig\": True,\n",
-    "        \"feature\": \"weight\",\n",
-    "        \"groupby\": \"cell\",\n",
-    "        \"markerSize\": 50,\n",
-    "    },\n",
-    "    \"plot2Dnet\": {\n",
-    "        \"saveFig\": True,\n",
-    "    },\n",
-    "}\n",
-    "\n",
-    "\n",
-    "# Create, simulate, and analyze the model\n",
-    "# ---------------------------------------\n",
-    "sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig)\n",
-    "\n",
-    "\n",
-    "# Set up the recording for the synaptic current plots\n",
-    "syn_plots = {}\n",
-    "for index, presyn in enumerate(sim.net.allCells[0]['conns']):    \n",
-    "    trace_name = 'i_syn_' + str(presyn['preGid'])\n",
-    "    syn_plots[trace_name] = None \n",
-    "    simConfig.recordTraces[trace_name] = {'sec': 'dend', 'loc': 1.0, 'synMech': 'exc', 'var': 'i', 'index': index}\n",
-    "\n",
-    "    \n",
-    "# Create, simulate, and analyze the model\n",
-    "# ---------------------------------------\n",
-    "sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig)\n",
-    "    \n",
-    "\n",
-    "# Extract the data\n",
-    "# ----------------\n",
-    "time = sim.allSimData['t']\n",
-    "v_soma = sim.allSimData['V_soma']['cell_0']\n",
-    "v_dend = sim.allSimData['V_dend']['cell_0']\n",
-    "\n",
-    "for syn_plot in syn_plots:\n",
-    "    syn_plots[syn_plot] = sim.allSimData[syn_plot]['cell_0']\n",
-    "\n",
-    "    \n",
-    "# Plot our custom figure\n",
-    "# ----------------------\n",
-    "import matplotlib.pyplot as plt\n",
-    "fig = plt.figure()\n",
-    "\n",
-    "plt.subplot(211)\n",
-    "plt.plot(time, v_soma, label='v_soma')\n",
-    "plt.plot(time, v_dend, label='v_dend')\n",
-    "plt.legend()\n",
-    "plt.xlabel('Time (ms)')\n",
-    "plt.ylabel('Membrane potential (mV)')\n",
-    "\n",
-    "plt.subplot(212)\n",
-    "for syn_plot in syn_plots:\n",
-    "    plt.plot(time, syn_plots[syn_plot], label=syn_plot)\n",
-    "plt.legend()\n",
-    "plt.xlabel('Time (ms)')\n",
-    "plt.ylabel('Synaptic current (nA)')\n",
-    "\n",
-    "plt.savefig('syn_currents.jpg', dpi=600)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "env",
-   "language": "python",
-   "name": "env"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/netpyne/tutorials/netpyne_tut1/netpyne_tut1.ipynb b/netpyne/tutorials/tut01_simple_network.ipynb
similarity index 72%
rename from netpyne/tutorials/netpyne_tut1/netpyne_tut1.ipynb
rename to netpyne/tutorials/tut01_simple_network.ipynb
index 239b11fef..cf40a817d 100644
--- a/netpyne/tutorials/netpyne_tut1/netpyne_tut1.ipynb
+++ b/netpyne/tutorials/tut01_simple_network.ipynb
@@ -1,119 +1,56 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "HZFPWWNxEozc"
+   },
    "source": [
-    "# Running NetPyNE in a Jupyter Notebook\n",
+    "# NetPyNE Tutorial 1: Simulating a simple network\n",
     "\n",
+    "In this tutorial, we will create a simple network model we can simulate. The model will consist of 40 pyramidal-like, two-compartment neurons with standard Hodgkin-Huxley dynamics in the somas and passive dynamics in the dendrites. We will connect the neurons randomly with a 10% probability of connection using a standard double-exponential synapse model. Finally, we will add a current clamp stimulus to one cell to activate the network. Then we will explore the model."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LIhuiXkEEOaJ"
+   },
+   "source": [
     "## Preliminaries\n",
     "\n",
-    "Hopefully you already completed these preliminaries by following the instructions at https://github.com/Neurosim-lab/netpyne/blob/development/netpyne/tutorials/README.md.  We will now walk you through how we installed the NetPyNE tutorials.\n",
-    "\n",
-    "We don't want to affect your system in any way, so we will operate from a virtual environment. These preliminary steps must be completed before going through this tutorial. You can't complete the preliminary steps from within Jupyter because you can't enter a virtual environment in Jupyter, you have to switch to a kernel made from your virtual environment.\n",
-    "\n",
-    "First we will empty your path of all but essentials. Then we will create and activate a virtual environment. Then we will update pip and install some necessary packages in the virtual environment, and finally we will create a kernel from the virtual environment that can be used by Jupyter.  \n",
-    "\n",
-    "### Create and activate a virtual environment\n",
-    "\n",
-    "First, open a Terminal and switch to the directory where you downloaded this notebook:\n",
+    "If you are going to run this notebook locally using Jupyter Notebook, start from following instructions https://github.com/suny-downstate-medical-center/netpyne/blob/development/netpyne/tutorials/README.md.\n",
     "\n",
-    "    cd netpyne_tuts\n",
+    "If you are using Open Source Brain or EBRAINS, everything is already set up.\n",
     "\n",
-    "Next, clear your PATH of all but the essentials. Don't worry, your normal PATH will return the next time you open a Terminal.\n",
+    "On any other online platform (e.g. on Google Collab) you might need to run the following commmands to install NEURON and NetPyNE using **pip**:\n",
+    "```\n",
+    "!pip install neuron\n",
+    "!pip install netpyne\n",
+    "```\n",
     "\n",
-    "    export PATH=/bin:/usr/bin\n",
-    "    \n",
-    "Next, create a virtual environment named \"env\":\n",
-    "\n",
-    "    python3 -m venv env\n",
-    "    \n",
-    "Check to see where you are currently running Python from:\n",
-    "\n",
-    "    which python3\n",
-    "    \n",
-    "Enter your new virtual environment:\n",
-    "\n",
-    "    source env/bin/activate\n",
-    "    \n",
-    "You should see in your prompt that you are in **env**.  \n",
-    "\n",
-    "Now see where you are running Python from:\n",
-    "\n",
-    "    which python3\n",
-    "    \n",
-    "It should come from inside your new virtual environment.  Any changes we make here will only exist in the **env** directory that was created here.  \n",
-    "\n",
-    "To exit your virtual environment, enter:\n",
-    "\n",
-    "    deactivate\n",
-    "    \n",
-    "Your prompt should reflect the change.  To get back in, enter:\n",
-    "\n",
-    "    source env/bin/activate\n",
-    "    \n",
-    "### Update pip and install packages\n",
-    "\n",
-    "We will now update pip and install some necessary packages in the virtual environment. From inside your virtual environment, enter:\n",
-    "\n",
-    "    python3 -m pip install --upgrade pip\n",
-    "    python3 -m pip install --upgrade ipython\n",
-    "    python3 -m pip install --upgrade ipykernel\n",
-    "    python3 -m pip install --upgrade jupyter\n",
-    "    \n",
-    "### Make a Jupyter kernel out of this virtual environment\n",
-    "\n",
-    "Now we will create a kernel that can be used by Jupyter Notebooks.  Enter:\n",
-    "\n",
-    "    ipython kernel install --user --name=env\n",
-    "\n",
-    "### Install NEURON and NetPyNE\n",
-    "\n",
-    "    python3 -m pip install --upgrade neuron\n",
-    "    python3 -m pip install --upgrade netpyne\n",
-    "    \n",
-    "### Launch this notebook in Jupyter Notebook\n",
-    "\n",
-    "Now we will launch Jupyter from within the virtual environment.  Enter:\n",
-    "\n",
-    "    jupyter notebook netpyne_tut1.ipynb\n",
-    "    \n",
-    "This should open a web browser with Jupyter running this notebook.  From the menu bar, click on **Kernel**, hover over **Change kernel** and select **env**.  We are now operating in the virtual environment (see **env** in the upper right instead of **Python 3**) and can begin the tutorial.\n",
-    "\n",
-    "## Single line command\n",
-    "\n",
-    "Entering the following single line command should perform all the previous steps and launch this tutorial in a Jupyter notebook in your web browser:\n",
-    "\n",
-    "    git clone https://github.com/Neurosim-lab/netpyne.git && cd netpyne/netpyne/tutorials/netpyne_tut1 && export PATH=/bin:/usr/bin && python3 -m venv env && source env/bin/activate && python3 -m pip install --upgrade pip && python3 -m pip install --upgrade ipython && python3 -m pip install --upgrade ipykernel && python3 -m pip install --upgrade jupyter && ipython kernel install --user --name=env && jupyter notebook netpyne_tut1.ipynb\n",
-    "\n",
-    "\n",
-    "## To run this again in the future\n",
-    "\n",
-    "Be sure to enter your virtual environment before running Jupyter!\n",
-    "\n",
-    "    cd netpyne_tuts\n",
-    "    source env/bin/activate\n",
-    "    jupyter notebook netpyne_tut1.ipynb\n",
-    "    \n",
-    "And then make sure you are in the **env** kernel in Jupyter.\n",
-    "\n"
+    "Now we are ready to start the tutorial."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "jl2nrc-uEK4J"
+   },
    "source": [
-    "# Tutorial 1 -- a simple network with one population\n",
-    "\n",
-    "Now we are ready to start NetPyNE Tutorial 1, which will create a simple network model we can simulate.  We will create a fairly simple network model of 40 pyramidal-like, two-compartment neurons with standard Hodgkin-Huxley dynamics in the somas and passive dynamics in the dendrites.  We will then connect the neurons randomly with a 10% probability of connection using a standard double-exponential synapse model.  Finally, we will add a current clamp stimulus to one cell to activate the network.  Then we will explore the model."
+    "## Instantiate network parameters and simulation configuration"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "ALX9AcIxELkk"
+   },
    "source": [
-    "## Instantiate network parameters and simulation configuration\n",
-    "\n",
     "You need two things to define a model/simulation in NetPyNE: 1) the parameters of the network and all its components (**netParams**) and 2) the configuration of the simulation (**simConfig**).  These requirements exist as objects in NetPyNE.  Let's instantiate them now."
    ]
   },
@@ -121,7 +58,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "scrolled": true
+    "id": "b3u6onuhNMCy"
    },
    "outputs": [],
    "source": [
@@ -131,27 +68,41 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "m4W4av8nNXNR"
+   },
    "source": [
-    "These NetPyNE objects come with a lot of defaults set which you can explore with tab completion, but we'll focus on that more later.\n",
-    "\n",
-    "We are going to plunge ahead and build our model: a simple network of 40 pyramidal-like two-compartment neurons with standard Hodgkin-Huxley dynamics in the soma and passive dynamics in the dendrite.  "
+    "Now we are going to specify our model: a simple network of 40 pyramidal-like two-compartment neurons with standard Hodgkin-Huxley dynamics in the soma and passive dynamics in the dendrite."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "j8KLTVVxNkXv"
+   },
+   "source": [
+    "## Specify a cell model"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "bOPkiH7dNonu"
+   },
    "source": [
-    "## Create a cell model\n",
-    "\n",
     "First we will add a cell type to our model by adding a dictionary named **pyr** to the *Cell Parameters* dictionary (**cellParams**) in the *Network Parameters* dictionary (**netParams**).  We will then add an empty dictionary named **secs** to hold our compartments."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "_JoTgRmhNRm6"
+   },
    "outputs": [],
    "source": [
     "netParams.cellParams['pyr'] = {}\n",
@@ -159,18 +110,31 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "rlex7LioN1fu"
+   },
+   "source": [
+    "### Specify the soma compartment properties\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "wpV-7o4NN2YN"
+   },
    "source": [
-    "### Specify the soma compartment properties\n",
-    "\n",
     "Now we will define our **soma**, by adding a **geom** dictionary defining the geometry of the soma and a **mechs** dictionary defining the biophysical mechanics being added to the soma."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "qrEjGWwwNxPA"
+   },
    "outputs": [],
    "source": [
     "netParams.cellParams['pyr']['secs']['soma'] = {}"
@@ -179,7 +143,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "o1NQacsIN9Uc"
+   },
    "outputs": [],
    "source": [
     "netParams.cellParams['pyr']['secs']['soma']['geom'] = {\n",
@@ -193,7 +159,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "ZEc3mV_UOASS"
+   },
    "outputs": [],
    "source": [
     "netParams.cellParams['pyr']['secs']['soma']['mechs'] = {\"hh\": {\n",
@@ -205,8 +173,11 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "djBCFHzrOJ9i"
+   },
    "source": [
     "The **hh** mechanism is builtin to NEURON, but you can see its *.mod* file here:\n",
     "https://github.com/neuronsimulator/nrn/blob/master/src/nrnoc/hh.mod\n",
@@ -215,18 +186,31 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "8QJuWmfaOOJP"
+   },
+   "source": [
+    "### Specify the dendrite compartment properties\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "mI2lIZtjOPGY"
+   },
    "source": [
-    "### Specify the dendrite compartment properties\n",
-    "\n",
     "Next will do the same thing for the dendrite compartment, but we will do it slightly differently.  We will first build up a **dend** dictionary and then add it to the cell model dictionary **pyr** when we are done."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "QnUvVve2OE-L"
+   },
    "outputs": [],
    "source": [
     "dend = {}"
@@ -235,7 +219,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "uLC6urqEOUKm"
+   },
    "outputs": [],
    "source": [
     "dend['geom'] = {\"diam\": 1.0,\n",
@@ -248,7 +234,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "VuNCZNFNOW4m"
+   },
    "outputs": [],
    "source": [
     "dend['mechs'] = {\"pas\": \n",
@@ -258,8 +246,11 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "SyhGANVhOgR5"
+   },
    "source": [
     "The **pas** mechanim is a simple leakage channel and is builtin to NEURON.  Its *.mod* file is available here:\n",
     "https://github.com/neuronsimulator/nrn/blob/master/src/nrnoc/passive.mod\n",
@@ -270,7 +261,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "Gm5p0r8kOZsy"
+   },
    "outputs": [],
    "source": [
     "dend['topol'] = {\"parentSec\": \"soma\",\n",
@@ -280,8 +273,11 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "9PaXF7DAOnip"
+   },
    "source": [
     "With our **dend** section dictionary complete, we must now add it to the **pyr** cell dictionary."
    ]
@@ -289,32 +285,50 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "1veMTubgOj6g"
+   },
    "outputs": [],
    "source": [
     "netParams.cellParams['pyr']['secs']['dend'] = dend"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "67JEURREOr6B"
+   },
    "source": [
     "Our two-compartment cell model is now completely specified.  Our next step is to create a population of these cells."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "KCWwp0wWOuSh"
+   },
+   "source": [
+    "## Specify a population of cells\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "4ywQnIPoOvOT"
+   },
    "source": [
-    "## Create a population of cells\n",
-    "\n",
     "NetPyNE uses *populations* of cells to specify connectivity.  In this tutorial, we will create just one population which we will call **E** (for excitatory).  It will be made of the **pyr** cells we just specified, and we want 40 of them."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "-wggC6nyOp1I"
+   },
    "outputs": [],
    "source": [
     "netParams.popParams['E'] = {\n",
@@ -324,11 +338,22 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "2AH2dJPPO2ag"
+   },
+   "source": [
+    "## Specify a synaptic model\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "YDynbArYO3hJ"
+   },
    "source": [
-    "## Create a synaptic model\n",
-    "\n",
     "We need a synaptic mechanism to connect our cells with.  We will create one called **exc** by adding a dictionary to the *synaptic mechanism parameters* dictionary (**synMechParams**).  The synapse *mod* used (**Exp2Syn**) is a simple double-exponential which is builtin to NEURON.  It's *.mod* file is available here:\n",
     "https://github.com/neuronsimulator/nrn/blob/master/src/nrnoc/exp2syn.mod"
    ]
@@ -336,7 +361,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "eyx8AZk5O0JW"
+   },
    "outputs": [],
    "source": [
     "netParams.synMechParams['exc'] = {\n",
@@ -348,11 +375,22 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "ms7OJIcTPCgQ"
+   },
+   "source": [
+    "## Specify the connectivity\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0tRNXK9lPDNz"
+   },
    "source": [
-    "## Connect the cells\n",
-    "\n",
     "Now we will specify the connectivity in our model by adding an entry to the **connParams** dictionary.  We will call our connectivity rule **E->E** as it will define connectivity from our **E** population to our **E** population.\n",
     "\n",
     "We will use the *synMech* **exc**, which we defined above.  For this synaptic mechanism, a *weight* of about **0.005** is appropriate.  These cells will have a 10% probability of getting connected, and will be activated five milliseconds after an action potential occurs in the presynaptic cell.  Synapses will occur on the **dend** *section* at its very end (*location* **1.0**)"
@@ -361,7 +399,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "s3YGPtw1PAHy"
+   },
    "outputs": [],
    "source": [
     "netParams.connParams['E->E'] = {\n",
@@ -377,8 +417,11 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "ZMkGcFHDPLzU"
+   },
    "source": [
     "## Set up the simulation configuration"
    ]
@@ -386,7 +429,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "vUit_vcvPI9Q"
+   },
    "outputs": [],
    "source": [
     "simConfig.filename = \"netpyne_tut1\"\n",
@@ -395,8 +440,11 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "k99wj1rfPSBR"
+   },
    "source": [
     "We will record from from the first cell (**0**) and we will record the voltage in the middle of the soma and the end of the dendrite."
    ]
@@ -404,7 +452,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "hnySg4EnPQPm"
+   },
    "outputs": [],
    "source": [
     "simConfig.recordCells = [0]\n",
@@ -423,8 +473,11 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "90JxAxiOPXiP"
+   },
    "source": [
     "Finally we will set up some plots to be automatically generated and saved."
    ]
@@ -432,7 +485,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "ZlGE85YUPVWT"
+   },
    "outputs": [],
    "source": [
     "simConfig.analysis = {\n",
@@ -447,67 +502,106 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "bov32kaoP2Vz"
+   },
+   "source": [
+    "## Create, simulate, and analyze the model\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "U8dG1xMi5SNy"
+   },
    "source": [
-    "To see plots in the notebook, we first have to enter the following command."
+    "The command `%matplotlib inline` allows figures to be shown in this notebook."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "6TOOw3g55aF_"
+   },
    "outputs": [],
    "source": [
     "%matplotlib inline"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "j81xP_F_P3mK"
+   },
    "source": [
-    "## Create, simulate, and analyze the model\n",
-    "\n",
     "Use one simple command to create, simulate, and analyze the model."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "lNrM2UmtPbiT"
+   },
    "outputs": [],
    "source": [
     "sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "t3DDYDY1QxGz"
+   },
    "source": [
     "We can see that there was no spiking in the network, and thus the spike raster was not plotted.  But there should be one new file in your directory: **netpyne_tut1_traces.png**.  Take a look.  Not too interesting, the cell just settles into its resting membrane potential.\n",
     "\n",
-    "Let's overlay the traces."
+    "Let's overlay the traces.  You can see all the options available for **plotTraces** here: http://netpyne.org/netpyne.analysis.traces.html#netpyne.analysis.traces.plotTraces\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "E98_BnQ8P7Xu"
+   },
    "outputs": [],
    "source": [
-    "fig, figData = sim.analysis.plotTraces(overlay=True)"
+    "sim.analysis.plotTraces(overlay=True);"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "FFCgpfXnQ_0A"
+   },
+   "source": [
+    "## Explore the model"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lb2CL6KLRDeK"
+   },
    "source": [
     "### Plot the 2D connectivity of the network"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "Uk3USecxRGjB"
+   },
    "source": [
     "Now we can take a look at the physical layout of our network model.  You can see all the options available for **plot2Dnet** here:\n",
     "http://netpyne.org/netpyne.analysis.network.html#netpyne.analysis.network.plot2Dnet"
@@ -516,18 +610,31 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "9XTkQE1wQ38e"
+   },
    "outputs": [],
    "source": [
-    "fig, figData = sim.analysis.plot2Dnet()"
+    "sim.analysis.plot2Dnet();"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "uro0E4o1RScq"
+   },
+   "source": [
+    "### Plot the connectivity matrix\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "4hgKkReTRTR8"
+   },
    "source": [
-    "### Plot the connectivity matrix\n",
-    "\n",
     "You can see all the options available for **plotConn** here:\n",
     "http://netpyne.org/netpyne.analysis.network.html#netpyne.analysis.network.plotConn"
    ]
@@ -535,15 +642,20 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "8LGEpe5gRNCP"
+   },
    "outputs": [],
    "source": [
-    "fig, figData = sim.analysis.plotConn()"
+    "sim.analysis.plotConn();"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "yik3rBcmRbKu"
+   },
    "source": [
     "Not very interesting with just one population, but we can also look at the cellular level connectivity."
    ]
@@ -551,25 +663,40 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "DzgzP7weRYlC"
+   },
    "outputs": [],
    "source": [
-    "fig, figData = sim.analysis.plotConn(feature='weight', groupBy='cell')"
+    "sim.analysis.plotConn(feature='weight', groupBy='cell');"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "ZuDnPRgnRnrv"
+   },
+   "source": [
+    "## Add a stimulation\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "VkP-GCicRofc"
+   },
    "source": [
-    "## Add a stimulation\n",
-    "\n",
     "We'll need to kickstart this network to see some activity -- let's inject current into one of the cells.  First we need to add an entry to the *Stimulation Source Parameters* dictionary (**stimSourceParams**).  We'll call our stimulation **IClamp1**, and we'll use the standard NEURON *type*: **IClamp**.  The current injection will last for a *duration* of 20 ms, it will start at a *delay* of 5 ms, and it will have an *amplitude* of 0.1 nanoAmps. "
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "VYud_OWPRd4_"
+   },
    "outputs": [],
    "source": [
     "netParams.stimSourceParams['IClamp1'] = {\n",
@@ -581,8 +708,11 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "nhWVRby-RyPR"
+   },
    "source": [
     "Now we need to add a target for our stimulation.  We do that by adding a dictionary to the *Stimulation Target Parameters* dictionary (**stimTargetParams**).  We'll call this connectivity rule **IClamp1->cell0**, because it will go from the source we just created (**IClamp1**) and the first cell in our population.  The stimulation (current injection in this case) will occur in our **dend** *section* at the very tip (*location* of **1.0**)."
    ]
@@ -590,7 +720,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "nCQDb148Ruck"
+   },
    "outputs": [],
    "source": [
     "netParams.stimTargetParams['IClamp1->cell0'] = {\n",
@@ -602,24 +734,32 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "_PbhoEk2SErC"
+   },
    "source": [
-    "### Create, simulate, and analyze the model\n"
+    "Now we can re-run the simulation."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "MM-WlZP-R0XA"
+   },
    "outputs": [],
    "source": [
     "sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "TNN1Tb7RSR0s"
+   },
    "source": [
     "Now we see spiking in the network, and the raster plot appears.  Let's improve the plots a little bit."
    ]
@@ -627,73 +767,74 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "GciqodwiSG1G"
+   },
    "outputs": [],
    "source": [
-    "fig, figData = sim.analysis.plotTraces(overlay=True)"
+    "sim.analysis.plotTraces(overlay=True);"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "vabK1mCWSVUq"
+   },
    "outputs": [],
    "source": [
-    "fig, figData = sim.analysis.plotRaster(marker='o', markerSize=50)"
+    "sim.analysis.plotRaster(marker='o', markerSize=50);"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "N8vACernS6vB"
+   },
    "source": [
-    "You can see all of the options available in **plotTraces** here:\n",
-    "http://netpyne.org/netpyne.analysis.traces.html#netpyne.analysis.traces.plotTraces\n",
-    "\n",
-    "You can see all of the options available in **plotRaster** here:\n",
-    "http://netpyne.org/netpyne.analysis.spikes.html#netpyne.analysis.spikes.plotRaster"
+    "## Record and plot a variety of traces\n"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "12NOAp6uTDfm"
+   },
    "source": [
-    "### Plot the connectivity matrix"
+    "Now let's explore the model by recording and plotting a variety of traces.  First let's clear our **recordTraces** dictionary and turn off the automatic raster plot."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "W03sHekMShTD"
+   },
    "outputs": [],
    "source": [
-    "fig, figData = sim.analysis.plotConn()"
+    "simConfig.recordTraces = {}\n",
+    "simConfig.analysis['plotRaster'] = False"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Record and plot a variety of traces\n",
-    "\n",
-    "Now let's explore the model by recording and plotting a variety of traces.  First let's clear our **recordTraces** dictionary and turn off the automatic raster plot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "id": "RiACDbsSTK-E"
+   },
    "source": [
-    "simConfig.recordTraces = {}\n",
-    "simConfig.analysis['plotRaster'] = False"
+    "### Record and plot the somatic conductances\n"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "okha2e6qTM2H"
+   },
    "source": [
-    "### Record and plot the somatic conductances\n",
-    "\n",
     "Let's record and plot the somatic conductances.  We need to take a look at the **hh** mod file to see what the variables are called.  The file is available here: https://github.com/neuronsimulator/nrn/blob/master/src/nrnoc/hh.mod\n",
     "\n",
     "We can see that the conductances are called *gna*, *gk*, and *gl*.  Let's set up recording for these conductances in the middle of the soma."
@@ -702,7 +843,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "h6ujyVeiTIR1"
+   },
    "outputs": [],
    "source": [
     "simConfig.recordTraces['gNa'] = {'sec': 'soma', 'loc': 0.5, 'mech': 'hh', 'var': 'gna'}\n",
@@ -711,8 +854,11 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "n2asw_OGTTvJ"
+   },
    "source": [
     "Then we can re-run the simulation."
    ]
@@ -720,15 +866,20 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "VNBf-o3kTTEy"
+   },
    "outputs": [],
    "source": [
     "sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "Fmy-PVTNTe0l"
+   },
    "source": [
     "Let's zoom in on one spike and overylay the traces."
    ]
@@ -736,51 +887,67 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "WBUXgh2cTXNV"
+   },
    "outputs": [],
    "source": [
-    "fig, figData = sim.analysis.plotTraces(timeRange=[90, 110], overlay=True)"
+    "sim.analysis.plotTraces(timeRange=[90, 110], overlay=True);"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "ED_WB0-VTpFz"
+   },
    "source": [
     "### Record from synapses"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "7mWPDfhxTroO"
+   },
    "source": [
     "Our synapses are set up to use **Exp2Syn**, which is builtin to NEURON.  Its mod file is available here: https://github.com/neuronsimulator/nrn/blob/master/src/nrnoc/exp2syn.mod\n",
     "\n",
-    "Looking in the file, we can see that its current variable is called **i**.  Let's record that and the voltage in the dendrite."
+    "Looking in the file, we can see that its current variable is called **i**.  Let's record that and the voltages in the dendrite and soma."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "9pccQhfAThpz"
+   },
    "outputs": [],
    "source": [
     "simConfig.recordTraces = {}\n",
     "simConfig.recordTraces['iSyn0'] = {'sec': 'dend', 'loc': 1.0, 'synMech': 'exc', 'var': 'i'}\n",
-    "simConfig.recordTraces['V_dend'] = {'sec': 'dend', 'loc': 1.0, 'var': 'v'}"
+    "simConfig.recordTraces['V_dend'] = {'sec': 'dend', 'loc': 1.0, 'var': 'v'}\n",
+    "simConfig.recordTraces['V_soma'] = {'sec': 'soma', 'loc': 0.5, 'var': 'v'}"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "e2dXHf_VTx7e"
+   },
    "outputs": [],
    "source": [
     "sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "dzPOoU_qDyAe"
+   },
    "source": [
     "That's the first synapse created in that location, but there are likely multiple synapses.  Let's plot all the synaptic currents entering cell 0.  First we need to see what they are.  The network is defined in **sim.net**.  Type in *sim.net.* and then push *Tab* to see what's available.\n",
     "\n",
@@ -790,31 +957,41 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "yuaxU0Z8T2fc"
+   },
    "outputs": [],
    "source": [
     "sim.net.allCells[0].keys()"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "3ZRJp8v8D-Qy"
+   },
    "source": [
-    "The connections coming onto the cell are in **conns**."
+    "The connections coming onto the cell are in the **conns** dictionary."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "go8gYRqHD4Yt"
+   },
    "outputs": [],
    "source": [
     "sim.net.allCells[0]['conns']"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "pemVfeg7EMUl"
+   },
    "source": [
     "So we want to record six synaptic currents.  Lets do that in a *for loop* at the same time creating a dictionary to hold the synaptic trace names as keys (and later the trace arrays as values)."
    ]
@@ -822,7 +999,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "fz-lgK7BECcv"
+   },
    "outputs": [],
    "source": [
     "simConfig.recordTraces = {}\n",
@@ -836,27 +1015,54 @@
     "    simConfig.recordTraces[trace_name] = {'sec': 'dend', 'loc': 1.0, 'synMech': 'exc', 'var': 'i', 'index': index}"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Ed7ol9_0EhHb"
+   },
+   "source": [
+    "Let's take a look at our **recordTraces** dictionary now."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "WtKSCwjwERl1"
+   },
    "outputs": [],
    "source": [
-    "print(simConfig.recordTraces)"
+    "simConfig.recordTraces"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "5xCF2e8GEto2"
+   },
+   "source": [
+    "Now we'll run the simulation again so that these traces get recorded."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "qyVTLamiEWmy"
+   },
    "outputs": [],
    "source": [
     "sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "heaQJJrhE8Y6"
+   },
    "source": [
     "## Extracting recorded data\n",
     "\n",
@@ -868,15 +1074,20 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "P87eEEtTEy4_"
+   },
    "outputs": [],
    "source": [
     "sim.allSimData.keys()"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "ybgQdEtQFHvL"
+   },
    "source": [
     "**spkt** is an array of the times of all spikes in the network.  **spkid** is an array of the universal index (GID) of the cell spiking.   **t** is an array of the time for traces.  Our traces appear as we named them, and each is a dictionary with its key being **cell_GID** and its value being the array of the trace."
    ]
@@ -884,15 +1095,20 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "IqSWZYRzFBN4"
+   },
    "outputs": [],
    "source": [
-    "sim.allSimData.V_soma.keys()\n"
+    "sim.allSimData.V_soma.keys()"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "NgRZqz5gFTuS"
+   },
    "source": [
     "So let's extract our data."
    ]
@@ -900,7 +1116,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "FiXRLSS6FQT-"
+   },
    "outputs": [],
    "source": [
     "time = sim.allSimData['t']\n",
@@ -912,8 +1130,11 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "eeR0rZhhFb3E"
+   },
    "source": [
     "And now we can make our custom plot."
    ]
@@ -921,11 +1142,13 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "H0RBievCFWh8"
+   },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
-    "fig = plt.figure()\n",
+    "fig = plt.figure(figsize=[10, 8])\n",
     "\n",
     "plt.subplot(211)\n",
     "plt.plot(time, v_soma, label='v_soma')\n",
@@ -945,25 +1168,19 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "6B2Idoo9GNL8"
+   },
    "source": [
-    "Cleaning up our figure (reducing font size, etc.) will be left as an exercise.  See the **matplotlib** users guide here:\n",
-    "https://matplotlib.org/users/index.html\n",
+    "Congratulations!  You have simulated a network model by specifying parameters for cells, populations, synapses, connectivity, stimulations, and recording.  You explored some basic analyses and extracted data to make a custom plot.\n",
     "\n",
-    "Now we will put all of this together into a single file.  But first, let's clear our workspace with the following command."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%reset"
+    "Now we will put all of this together into a single file."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -1178,10 +1395,16 @@
   }
  ],
  "metadata": {
+  "colab": {
+   "collapsed_sections": [],
+   "name": "NetPyNE_EBRAINS_tut1.ipynb",
+   "provenance": [],
+   "toc_visible": true
+  },
   "kernelspec": {
-   "display_name": "env",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "env"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1193,9 +1416,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.2"
+   "version": "3.9.16"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 1
 }
diff --git a/netpyne/tutorials/tut02_position_based_conn.ipynb b/netpyne/tutorials/tut02_position_based_conn.ipynb
new file mode 100644
index 000000000..f4584fbf7
--- /dev/null
+++ b/netpyne/tutorials/tut02_position_based_conn.ipynb
@@ -0,0 +1,241 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## NetPyNE Tutorial 2: Position- and distance-based connectivity"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following example demonstrates how to spatially separate populations, add inhibitory populations, and implement weights, probabilities of connection, and delays that depend on cell positions or distances.\n",
+    "(more details http://netpyne.org/tutorial.html#tutorial-5-position-and-distance-based-connectivity)\n",
+    "\n",
+    "We will build a cortical-like network with six populations (three excitatory and three inhibitory) distributed in three layers: 2/3, 4 and 5. Since we want to distribute the cells spatially, the first thing we need to do is define the volume dimensions where cells will be placed."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preliminaries\n",
+    "\n",
+    "If you are going to run this notebook locally using Jupyter Notebook, start from following instructions https://github.com/Neurosim-lab/netpyne/blob/development/netpyne/tutorials/README.md.\n",
+    "\n",
+    "If you are using Open Source Brain or EBRAINS, everything is already set up.\n",
+    "\n",
+    "On any other online platform (e.g. on Google Collab) you might need to run the following commmands to install NEURON and NetPyNE using **pip**:\n",
+    "```\n",
+    "!pip install neuron\n",
+    "!pip install netpyne\n",
+    "```\n",
+    "\n",
+    "Now we are ready to start NetPyNE Tutorial."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from netpyne import specs, sim\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Network parameters\n",
+    "netParams = specs.NetParams()  # object of class NetParams to store the network parameters\n",
+    "\n",
+    "netParams.sizeX = 100              # x-dimension (horizontal length) size in um\n",
+    "netParams.sizeY = 1000             # y-dimension (vertical height or cortical depth) size in um\n",
+    "netParams.sizeZ = 100              # z-dimension (horizontal length) size in um\n",
+    "netParams.propVelocity = 100.0     # propagation velocity (um/ms)\n",
+    "netParams.probLengthConst = 150.0  # length constant for conn probability (um)\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that we also added two parameters (propVelocity and probLengthConst) which we’ll use later for the connectivity rules.\n",
+    "\n",
+    "\n",
+    "Next we can define cell properties of each type of cell (E and I), and create our populations labeled according to the cell type and layer e.g. ‘E2’ for excitatory cells in layer 2. We can define the cortical depth range of each population by using the `yRange` parameter. This range can also be specified using normalized values using `ynormRange`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "## Cell types\n",
+    "secs = {} # sections dict\n",
+    "secs['soma'] = {'geom': {}, 'mechs': {}}                                                # soma params dict\n",
+    "secs['soma']['geom'] = {'diam': 15, 'L': 14, 'Ra': 120.0}                               # soma geometry\n",
+    "secs['soma']['mechs']['hh'] = {'gnabar': 0.13, 'gkbar': 0.036, 'gl': 0.003, 'el': -70}  # soma hh mechanism\n",
+    "netParams.cellParams['E'] = {'secs': secs}                                              # add dict to list of cell params\n",
+    "\n",
+    "secs = {} # sections dict\n",
+    "secs['soma'] = {'geom': {}, 'mechs': {}}                                                # soma params dict\n",
+    "secs['soma']['geom'] = {'diam': 10.0, 'L': 9.0, 'Ra': 110.0}                            # soma geometry\n",
+    "secs['soma']['mechs']['hh'] = {'gnabar': 0.11, 'gkbar': 0.036, 'gl': 0.003, 'el': -70}  # soma hh mechanism\n",
+    "netParams.cellParams['I'] = {'secs': secs}                                              # add dict to list of cell params\n",
+    "\n",
+    "## Population parameters\n",
+    "netParams.popParams['E2'] = {'cellType': 'E', 'numCells': 50, 'yRange': [100,300]}\n",
+    "netParams.popParams['I2'] = {'cellType': 'I', 'numCells': 50, 'yRange': [100,300]}\n",
+    "netParams.popParams['E4'] = {'cellType': 'E', 'numCells': 50, 'yRange': [300,600]}\n",
+    "netParams.popParams['I4'] = {'cellType': 'I', 'numCells': 50, 'yRange': [300,600]}\n",
+    "netParams.popParams['E5'] = {'cellType': 'E', 'numCells': 50, 'ynormRange': [0.6,1.0]}\n",
+    "netParams.popParams['I5'] = {'cellType': 'I', 'numCells': 50, 'ynormRange': [0.6,1.0]}\n",
+    "\n",
+    "\n",
+    "## Synaptic mechanism parameters\n",
+    "netParams.synMechParams['exc'] = {'mod': 'Exp2Syn', 'tau1': 0.8, 'tau2': 5.3, 'e': 0}  # NMDA synaptic mechanism\n",
+    "netParams.synMechParams['inh'] = {'mod': 'Exp2Syn', 'tau1': 0.6, 'tau2': 8.5, 'e': -75}  # GABA synaptic mechanism\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In terms of stimulation, we’ll add background inputs to all cell in the network. The weight will be fixed to 0.01, but we’ll make the delay come from a gaussian distribution with mean 5 ms and standard deviation 2, and have a minimum value of 1 ms. We can do this using string-based functions: `max(1, normal(5,2)`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "# Stimulation parameters\n",
+    "netParams.stimSourceParams['bkg'] = {'type': 'NetStim', 'rate': 20, 'noise': 0.3}\n",
+    "netParams.stimTargetParams['bkg->all'] = {'source': 'bkg', 'conds': {'cellType': ['E','I']}, 'weight': 0.01, 'delay': 'max(1, normal(5,2))', 'synMech': 'exc'}\n",
+    "\n",
+    "# Simulation options\n",
+    "simConfig = specs.SimConfig()        # object of class SimConfig to store simulation configuration\n",
+    "simConfig.duration = 1*1e3           # Duration of the simulation, in ms\n",
+    "simConfig.dt = 0.025                 # Internal integration timestep to use\n",
+    "simConfig.verbose = False            # Show detailed messages\n",
+    "simConfig.recordTraces = {'V_soma':{'sec':'soma','loc':0.5,'var':'v'}}  # Dict with traces to record\n",
+    "simConfig.recordStep = 1             # Step size in ms to save data (eg. V traces, LFP, etc)\n",
+    "simConfig.filename = 'tut5'          # Set file output name\n",
+    "simConfig.savePickle = False         # Save params, network and sim output to pickle file\n",
+    "simConfig.saveMat = False            # Save params, network and sim output to pickle file\n",
+    "\n",
+    "\n",
+    "simConfig.analysis['plotRaster'] = {'orderBy': 'y', 'orderInverse': True, 'saveFig': True}         # Plot a raster\n",
+    "simConfig.analysis['plotTraces'] = {'include': [('E2',0), ('E4', 0), ('E5', 5)], 'saveFig': True, 'oneFigPer': 'trace'}  # Plot recorded traces for this list of cells\n",
+    "simConfig.analysis['plot2Dnet'] = {'saveFig': True}                                                # plot 2D cell positions and connections\n",
+    "simConfig.analysis['plotConn'] = {'saveFig': True}                                                 # plot connectivity matrix\n",
+    "# simConfig.analysis['plot2Dfiring'] = {'saveFig': True, 'showFig': True}\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we run the model at this point we will see the cells are distributed into three layers as specified, and they all spike randomly with an average rate of 20 Hz driven by background input:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Create network and run simulation\n",
+    "sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let’s now add excitatory connections with some spatially-dependent properties to illustrate NetPyNE’s capabilities.\n",
+    "\n",
+    "Running the model now shows excitatory connections in red, and how cells in the deeper layers (higher y values) exhibit lower rates and higher synchronization, due to increased weights leading to depolarization blockade. This difference is also visible in the voltage traces of layer 2 vs layer 5 cells:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Cell connectivity rules\n",
+    "netParams.connParams['E->all'] = {\n",
+    "    'preConds': {'cellType': 'E'}, 'postConds': {'y': [100,1000]},  #  E -> all (100-1000 um)\n",
+    "    'probability': 0.1 ,                  # probability of connection\n",
+    "    'weight': '0.005*post_ynorm',         # synaptic weight\n",
+    "    'delay': 'dist_3D/propVelocity',      # transmission delay (ms)\n",
+    "    'synMech': 'exc'}                     # synaptic mechanism\n",
+    "\n",
+    "# Create network and run simulation\n",
+    "sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we add inhibitory connections which will project only onto excitatory cells, specified here using the pop attribute.\n",
+    "\n",
+    "Notice that the 2D network diagram now shows inhibitory connections in blue, and these are mostly local/lateral within layers, due to the distance-related probability restriction. These local inhibitory connections reduce the overall synchrony, introducing some richness into the temporal firing patterns of the network."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "netParams.connParams['I->E'] = {\n",
+    "    'preConds': {'cellType': 'I'}, 'postConds': {'pop': ['E2','E4','E5']},       #  I -> E\n",
+    "    'probability': '0.4*exp(-dist_3D/probLengthConst)',   # probability of connection\n",
+    "    'weight': 0.001,                                      # synaptic weight\n",
+    "    'delay': 'dist_3D/propVelocity',                      # transmission delay (ms)\n",
+    "    'synMech': 'inh'}                                     # synaptic mechanism\n",
+    "\n",
+    "# Create network and run simulation\n",
+    "sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/netpyne/tutorials/tut03_stimulation.ipynb b/netpyne/tutorials/tut03_stimulation.ipynb
new file mode 100644
index 000000000..425cf2bf2
--- /dev/null
+++ b/netpyne/tutorials/tut03_stimulation.ipynb
@@ -0,0 +1 @@
+{"cells":[{"cell_type":"markdown","metadata":{},"source":["## NetPyNE Tutorial 3: Adding Stimulation to the Network"]},{"cell_type":"markdown","metadata":{},"source":["## Preliminaries\n","\n","If you are going to run this notebook locally using Jupyter Notebook, start from following instructions https://github.com/suny-downstate-medical-center/netpyne/blob/development/netpyne/tutorials/README.md.\n","\n","If you are using Open Source Brain or EBRAINS, everything is already set up.\n","\n","On any other online platform (e.g. on Google Collab) you might need to run the following commmands to install NEURON and NetPyNE using **pip**:\n","```\n","!pip install neuron\n","!pip install netpyne\n","```\n","\n","Now we are ready to start the tutorial."]},{"cell_type":"markdown","metadata":{},"source":["Two dictionary structures are used to specify cell stimulation parameters: `stimSourceParams` to define the parameters of the sources of stimulation; and `stimTargetParams` to specify what cells will be applied what source of stimulation (mapping of sources to cells). See [Stimulation parameters](http://netpyne.org/user_documentation.html#stimulation) for details.\n","\n","In this example, we will take as a starting point the simple network similar to that from the first tutorial, but with no connection parameters, and add external stimulation instead."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":1989,"status":"ok","timestamp":1621526222498,"user":{"displayName":"Salvador Dura-Bernal","photoUrl":"","userId":"10473966374056868820"},"user_tz":240},"id":"f0P--qg5YUT6","outputId":"5a676705-6b6a-4925-941c-8471215bfb7d"},"outputs":[],"source":["from netpyne import specs, sim\n","\n","# Network parameters\n","netParams = specs.NetParams()  # object of class NetParams to store the network parameters\n","\n","## Cell params\n","secs = {}   # sections dict\n","secs['soma'] = {'geom': {}, 'mechs': {}}                                                    # soma params dict\n","secs['soma']['geom'] = {'diam': 18.8, 'L': 18.8}                                            # soma geometry\n","secs['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70}      # soma hh mechanism\n","netParams.cellParams['PYR'] = {'secs': secs}                                                # add dict to list of cell params\n","\n","## Population parameters\n","netParams.popParams['S'] = {'cellType': 'PYR', 'numCells': 20}\n","netParams.popParams['M'] = {'cellType': 'PYR', 'numCells': 20}\n","\n","## Synaptic mechanism parameters\n","netParams.synMechParams['exc'] = {'mod': 'Exp2Syn', 'tau1': 0.1, 'tau2': 5.0, 'e': 0}  # excitatory synaptic mechanism\n"]},{"cell_type":"markdown","metadata":{},"source":["Below we add four typical NEURON sources of stimulation, each of a different type: `IClamp`, `VClamp`, `AlphaSynapse`, `NetStim`. Note that parameter values can also include string-based functions ([Functions as strings](http://netpyne.org/user_documentation.html#function-string)), for example to set a uniform distribution of onset values (`'onset': 'uniform(300,600)'`), or maximum conductance dependent on the target cell normalized depth (`'gmax': '4*post_ynorm'`):"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["## Stimulation parameters\n","netParams.stimSourceParams['Input_1'] = {\n","    'type': 'IClamp', \n","    'del': 300, \n","    'dur': 100, \n","    'amp': 'uniform(0.4,0.5)'}\n","\n","netParams.stimSourceParams['Input_2'] = {\n","    'type': 'VClamp', \n","    'dur': [0,50,200], \n","    'amp': [-60,-30,40], \n","    'gain': 1e5, \n","    'rstim': 1, \n","    'tau1': 0.1, \n","    'tau2': 0}\n","\n","netParams.stimSourceParams['Input_3'] = {\n","    'type': 'AlphaSynapse', \n","    'onset': 'uniform(300,600)', \n","    'tau': 5, \n","    'gmax': '4*post_ynorm', \n","    'e': 0}\n","\n","netParams.stimSourceParams['Input_4'] = {\n","    'type': 'NetStim', \n","    'interval': 'uniform(20,100)', \n","    'start': 600, \n","    'noise': 0.1}"]},{"cell_type":"markdown","metadata":{},"source":["Now we can map or apply any of the above stimulation sources to any subset of cells in the network by adding items to the `stimTargetParams` dict. Note that we can use any of the cell tags (e.g. `‘pop’`, `‘cellType’` or `‘ynorm’`) to select which cells will be stimulated. Additionally, using the `‘cellList’` option, we can target a specific list of cells (using relative cell ids) within the subset of cells selected (e.g. first 15 cells of the ‘S’ population):"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["\n","netParams.stimTargetParams['Input_1->S'] = {\n","    'source': 'Input_1', \n","    'sec':'soma', \n","    'loc': 0.8, \n","    'conds': {'pop':'S', 'cellList': list(range(15))}}\n","\n","netParams.stimTargetParams['Input_2->S'] = {\n","    'source': 'Input_2', \n","    'sec':'soma', \n","    'loc': 0.5, \n","    'conds': {'pop':'S', 'ynorm': [0,0.5]}}\n","\n","netParams.stimTargetParams['Input_3->M'] = {\n","    'source': 'Input_3', \n","    'sec':'soma', \n","    'loc': 0.2, \n","    'conds': {'pop':'M'}}\n","\n","netParams.stimTargetParams['Input_4->PYR'] = {\n","    'source': 'Input_4', \n","    'sec':'soma', \n","    'loc': 0.5, \n","    'synMech': 'exc',\n","    'weight': '0.1+normal(0.2,0.05)',\n","    'delay': 1,\n","    'conds': {'cellType':'PYR', 'ynorm': [0.6,1.0]}}"]},{"cell_type":"markdown","metadata":{},"source":["Set up configuration in a standard way, and run the simulation:"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["\n","# Simulation options\n","simConfig = specs.SimConfig()       # object of class SimConfig to store simulation configuration\n","\n","simConfig.duration = 1*1e3          # Duration of the simulation, in ms\n","simConfig.dt = 0.025                # Internal integration timestep to use\n","simConfig.verbose = False           # Show detailed messages\n","simConfig.recordTraces = {'V_soma':{'sec':'soma','loc':0.5,'var':'v'}}  # Dict with traces to record\n","simConfig.recordStep = 0.1          # Step size in ms to save data (eg. V traces, LFP, etc)\n","simConfig.filename = 'tut6'         # Set file output name\n","simConfig.savePickle = False        # Save params, network and sim output to pickle file\n","\n","simConfig.analysis['plotRaster'] = {'saveFig': True, 'orderBy': 'y', 'orderInverse': True}                                 # Plot a raster\n","simConfig.analysis['plotTraces'] = {'include': [('S',0), ('M',0)], 'saveFig': True}  # Plot recorded traces for this list of cells\n","\n","%matplotlib inline\n","# Create network and run simulation\n","sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig)"]}],"metadata":{"colab":{"collapsed_sections":[],"name":"tut_netpyne_stim.ipynb","provenance":[{"file_id":"19y6MLKhDAdBxLUZm2sHOuQx-5bqSODs-","timestamp":1621524871397}]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.16"}},"nbformat":4,"nbformat_minor":0}
diff --git a/netpyne/tutorials/tut04_importing_cell_models.ipynb b/netpyne/tutorials/tut04_importing_cell_models.ipynb
new file mode 100644
index 000000000..c97184ec7
--- /dev/null
+++ b/netpyne/tutorials/tut04_importing_cell_models.ipynb
@@ -0,0 +1,575 @@
+{
+  "cells": [
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HZFPWWNxEozc"
+      },
+      "source": [
+        "# NetPyNE Tutorial 4: Importing cell models\n",
+        "\n",
+        "NetPyNE allows the importation of cell models previously defined in external files using the `importCellParams()` method. This method will convert all the cell information into the required NetPyNE format. This is also a way to make use of cell models which have been implemented separately.\n",
+        "\n",
+        "The `cellRule = netParams.importCellParams(label, conds, fileName, cellName, cellArgs={}, importSynMechs=False)` method takes as arguments the (arbitrary)label of the new cell rule, the name of the file where the cell is defined (`.py`, `.hoc`, or `.swc` files), and the name of the cell template (hoc) or cell class (Python).  If you wish to import the synaptic mechanisms parameters, you can set the `importSynMechs=True`.  The method returns the new cell rule so that it can be further modified.\n",
+        "\n",
+        "There are many existing cell models available for use, for example, at [ModelDB](hhttps://senselab.med.yale.edu/ModelDB/)).  In this tutorial, we will import a variety of cell models from different file types."
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Preliminaries\n",
+        "\n",
+        "If you are going to run this notebook locally using Jupyter Notebook, start from following instructions https://github.com/Neurosim-lab/netpyne/blob/development/netpyne/tutorials/README.md.\n",
+        "\n",
+        "If you are using Open Source Brain or EBRAINS, everything is already set up.\n",
+        "\n",
+        "On any other online platform (e.g. on Google Collab) you might need to run the following commmands to install NEURON and NetPyNE using **pip**:\n",
+        "```\n",
+        "!pip install neuron\n",
+        "!pip install netpyne\n",
+        "```\n",
+        "\n",
+        "Now we are ready to start NetPyNE Tutorial."
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BjLSlJvvJfmp"
+      },
+      "source": [
+        "## Compile mechanisms\n",
+        "\n",
+        "The cell models require membrane mechanisms (e.g. channel models) that are not built-in to NEURON.  So, now we will compile the necessary mechanisms (in the `mod` directory) using `nrnivmodl`.  You can learn more about mechanisms and `.mod` files in the [NEURON documentation](https://www.neuron.yale.edu/neuron/static/py_doc/modelspec/programmatic/mechanisms/nmodl.html). "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HzRiVxt4Nk8M"
+      },
+      "outputs": [],
+      "source": [
+        "!nrnivmodl mod"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IITT46g4NyFi"
+      },
+      "source": [
+        "This should have created a new directory: `x86_64` which contains the compiled mechanisms NEURON can use."
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jl2nrc-uEK4J"
+      },
+      "source": [
+        "## Instantiate network parameters and simulation configuration\n",
+        "\n"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ALX9AcIxELkk"
+      },
+      "source": [
+        "You need two things to define a model/simulation in NetPyNE: 1) the parameters of the network and all its components (**netParams**) and 2) the configuration of the simulation (**simConfig**).  These requirements exist as objects in NetPyNE.  Let's instantiate them now."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "b3u6onuhNMCy"
+      },
+      "outputs": [],
+      "source": [
+        "from netpyne import specs, sim\n",
+        "netParams = specs.NetParams()\n",
+        "simConfig = specs.SimConfig()"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UslyhWdazxhG"
+      },
+      "source": [
+        "The following line allows figures to be shown in this notebook:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UlmteR8dz2ii"
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "j8KLTVVxNkXv"
+      },
+      "source": [
+        "## Import cell models\n"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bOPkiH7dNonu"
+      },
+      "source": [
+        "### Import a NEURON cell model from a Python file\n",
+        "\n",
+        "Please open the file `HHCellFile.py` in the `cells` directory to see how this cell model is defined in NEURON/Python.  In particular, note that the cell model is defined in a class named `HHCellClass`.  This must be included as the `cellName` so that NetPyNE knows how to instantiate the model.\n",
+        "\n",
+        "Now we will import it into NetPyNE:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_JoTgRmhNRm6"
+      },
+      "outputs": [],
+      "source": [
+        "cellRule = netParams.importCellParams(\n",
+        "    label='python_model', \n",
+        "    fileName='cells/HHCellFile.py', \n",
+        "    cellName='HHCellClass', \n",
+        "    importSynMechs=True,\n",
+        "    )"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ftmrl7MlRogA"
+      },
+      "source": [
+        "### Import a cell morphology from a HOC file"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "t1Tih4_G1h6S"
+      },
+      "source": [
+        "Please open the file `geom.hoc` in the `cells` directory in a text-editor to see how this morphology is defined in a template named `E21`.  This must be included as the `cellName` so that NetPyNE knows how to instantiate the model.\n",
+        "\n",
+        "Note that this file just defines the morphology of a cell, not any membrane mechanisms."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QamR3wIcRt8o"
+      },
+      "outputs": [],
+      "source": [
+        "cellRule = netParams.importCellParams(\n",
+        "    label='hoc_morph', \n",
+        "    fileName='cells/geom.hoc', \n",
+        "    cellName='E21', \n",
+        "    importSynMechs=False,\n",
+        "    )"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yNvWTucZ2W_z"
+      },
+      "source": [
+        "Because the file we are importing contains no mechanisms, just the cell's morphology, we will add the default NEURON Hodgkin-Huxley mechanism into the soma section and a passive leak current in all sections. \n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "W7SvEPDP2D3U"
+      },
+      "outputs": [],
+      "source": [
+        "cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70}\n",
+        "for secName in cellRule['secs']:\n",
+        "    cellRule['secs'][secName]['mechs']['pas'] = {'g': 0.0000357, 'e': -70}\n",
+        "    cellRule['secs'][secName]['geom']['cm'] = 1"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jjgdY2I0Pq13"
+      },
+      "source": [
+        "### Import a cell morphology from an SWC file"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ghbsl13VSZxJ"
+      },
+      "source": [
+        "Please open the file `BS0284.swc` in the `cells` directory in a text-editor to see how this morphology is defined.  SWC is a common file format for neuron morphology, but SWC files do not specify a complete cell model, they only define the morphology of a cell.  There are many experimentally reconstructed neuron morphologies available for download at [NeuroMorpho](https://neuromorpho.org)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "AhdKorY_RGDe"
+      },
+      "outputs": [],
+      "source": [
+        "cellRule = netParams.importCellParams(\n",
+        "        label='swc_morph',\n",
+        "        fileName='cells/BS0284.swc',\n",
+        "        cellName='',\n",
+        "        importSynMechs=False,\n",
+        "        )"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vhGKlSkz4gN8"
+      },
+      "source": [
+        "Because the file we are importing contains no mechanisms, just the cell's morphology, we will add the default NEURON Hodgkin-Huxley mechanism into the soma section and a passive leak current in all sections."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "VsusUj2o4q_E"
+      },
+      "outputs": [],
+      "source": [
+        "for secName in cellRule['secs']:\n",
+        "    cellRule['secs'][secName]['mechs']['pas'] = {'g': 0.0000357, 'e': -70}\n",
+        "    cellRule['secs'][secName]['geom']['cm'] = 1\n",
+        "    if secName.startswith('soma'):\n",
+        "        cellRule['secs'][secName]['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70}"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NINXSIof4xmh"
+      },
+      "source": [
+        "If a morphology has multiple sections defining the soma, it is convenient for recording and analysis to rename the first soma section to just `soma`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6M7Igi1D5ZbZ"
+      },
+      "outputs": [],
+      "source": [
+        "netParams.renameCellParamsSec('swc_morph', 'soma_0', 'soma')"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hNFLEyIR6N0B"
+      },
+      "source": [
+        "### Import a complete cell model from a HOC file"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pbcscddf6mVa"
+      },
+      "source": [
+        "Please open the file `pyr3_traub.hoc` in the `cells` directory in a text-editor to see how this model is defined in a template named `pyr3`.  This must be included as the `cellName` so that NetPyNE knows how to instantiate the model. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "liBgMVCc7ME9"
+      },
+      "outputs": [],
+      "source": [
+        "cellRule = netParams.importCellParams(\n",
+        "    label='hoc_model', \n",
+        "    fileName='cells/pyr3_traub.hoc', \n",
+        "    cellName='pyr3',\n",
+        "    )"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AtG1MwBP4bh_"
+      },
+      "source": [
+        "Looking in the file, we can see that the soma compartment is `comp[1]`, which becomes `comp_1` in NetPyNE.  We will rename this section for convenience."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "80fRbGDk4vgS"
+      },
+      "outputs": [],
+      "source": [
+        "netParams.renameCellParamsSec('hoc_model', 'comp_1', 'soma')"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_DBqHb-h7glx"
+      },
+      "source": [
+        "## Make populations from the imported cell models"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "z7nT8Z9b0QGq"
+      },
+      "source": [
+        "We'll make a population for each cell type, consisting of just one cell."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "OL9UAoKr7vxv"
+      },
+      "outputs": [],
+      "source": [
+        "netParams.popParams['python_pop'] = {'cellType': 'python_model', 'numCells': 1}\n",
+        "netParams.popParams['hoc_morph_pop'] = {'cellType': 'hoc_morph', 'numCells': 1}\n",
+        "netParams.popParams['swc_morph_pop'] = {'cellType': 'swc_morph', 'numCells': 1}\n",
+        "netParams.popParams['hoc_model_pop'] = {'cellType': 'hoc_model', 'numCells': 1}"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZMkGcFHDPLzU"
+      },
+      "source": [
+        "## Set up the simulation configuration"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "vUit_vcvPI9Q"
+      },
+      "outputs": [],
+      "source": [
+        "simConfig.filename = \"netpyne_tut2\"\n",
+        "simConfig.duration = 200.0\n",
+        "simConfig.dt = 0.1"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "k99wj1rfPSBR"
+      },
+      "source": [
+        "We will record the voltage in the middle of the soma compartment for all cells."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hnySg4EnPQPm"
+      },
+      "outputs": [],
+      "source": [
+        "simConfig.recordCells = ['all']\n",
+        "simConfig.recordTraces = {\n",
+        "    \"V_soma\": {\n",
+        "        \"sec\": \"soma\",\n",
+        "        \"loc\": 0.5,\n",
+        "        \"var\": \"v\",\n",
+        "    },\n",
+        "}"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "90JxAxiOPXiP"
+      },
+      "source": [
+        "Finally we will set up the traces plots to be automatically generated and saved."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ZlGE85YUPVWT"
+      },
+      "outputs": [],
+      "source": [
+        "simConfig.analysis = {\n",
+        "    \"plotTraces\": {\n",
+        "        \"include\": ['all'],\n",
+        "        \"saveFig\": True,\n",
+        "    },\n",
+        "}"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bov32kaoP2Vz"
+      },
+      "source": [
+        "## Create, simulate, and analyze the model\n"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "j81xP_F_P3mK"
+      },
+      "source": [
+        "Use one simple command to create, simulate, and analyze the model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lNrM2UmtPbiT"
+      },
+      "outputs": [],
+      "source": [
+        "sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig)"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IX70EJYA-huW"
+      },
+      "source": [
+        "## Visualize the cell models\n",
+        "\n",
+        "We can use the `plotShape` function to visualize the cells.  You can see all the options available for **plotShape** here:\n",
+        "http://netpyne.org/reference.html#netpyne.analysis.plotShape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "T8KenOky5Ebh"
+      },
+      "outputs": [],
+      "source": [
+        "sim.analysis.plotShape(includePre=['python_pop'], includePost=['python_pop'], showFig=True)\n",
+        "sim.analysis.plotShape(includePre=['hoc_morph_pop'], includePost=['hoc_morph_pop'], showFig=True)\n",
+        "sim.analysis.plotShape(includePre=['swc_morph_pop'], includePost=['swc_morph_pop'], showFig=True)\n",
+        "sim.analysis.plotShape(includePre=['hoc_model_pop'], includePost=['hoc_model_pop'], showFig=True)"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6B2Idoo9GNL8"
+      },
+      "source": [
+        "Congratulations!  You have imported existing cell models of a variety of types for use in simulations.\n",
+        "\n",
+        "There are several other NetPyNE tutorials available."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "name": "NetPyNE_EBRAINS_tut2.ipynb",
+      "provenance": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.16"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/netpyne/tutorials/tut05_analysis_plotting.ipynb b/netpyne/tutorials/tut05_analysis_plotting.ipynb
new file mode 100644
index 000000000..752171545
--- /dev/null
+++ b/netpyne/tutorials/tut05_analysis_plotting.ipynb
@@ -0,0 +1,779 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RRlxTCVG35B0"
+   },
+   "source": [
+    "## NetPyNE Tutorial 5: Analysis and Plotting"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preliminaries\n",
+    "\n",
+    "If you are going to run this notebook locally using Jupyter Notebook, start from following instructions https://github.com/Neurosim-lab/netpyne/blob/development/netpyne/tutorials/README.md.\n",
+    "\n",
+    "If you are using Open Source Brain or EBRAINS, everything is already set up.\n",
+    "\n",
+    "On any other online platform (e.g. on Google Collab) you might need to run the following commmands to install NEURON and NetPyNE using **pip**:\n",
+    "```\n",
+    "!pip install neuron\n",
+    "!pip install netpyne\n",
+    "```\n",
+    "\n",
+    "Now we are ready to start the tutorial."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Compile mechanisms\n",
+    "\n",
+    "The cell models require membrane mechanisms (e.g. channel models) that are not built-in to NEURON.  So, now we will compile the necessary mechanisms (in the `mod` directory) using `nrnivmodl`.  You can learn more about mechanisms and `.mod` files in the [NEURON documentation](https://www.neuron.yale.edu/neuron/static/py_doc/modelspec/programmatic/mechanisms/nmodl.html). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!nrnivmodl mod"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "VxNcuIfJ65LH"
+   },
+   "source": [
+    "## Load a tutorial\n",
+    "\n",
+    "We will be modifying `gui_tut3` and running that to explore analyses and plotting.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ot3uw7v94Sw1"
+   },
+   "outputs": [],
+   "source": [
+    "from netpyne import specs\n",
+    "\n",
+    "\n",
+    "#------------------------------------------------------------------------------\n",
+    "#\n",
+    "# NETWORK PARAMETERS\n",
+    "#\n",
+    "#------------------------------------------------------------------------------\n",
+    "\n",
+    "netParams = specs.NetParams()  # object of class NetParams to store the network parameters\n",
+    "\n",
+    "netParams.sizeX = 100 # x-dimension (horizontal length) size in um\n",
+    "netParams.sizeY = 500 # y-dimension (vertical height or cortical depth) size in um\n",
+    "netParams.sizeZ = 100 # z-dimension (horizontal length) size in um\n",
+    "netParams.propVelocity = 100.0 # propagation velocity (um/ms)\n",
+    "netParams.probLengthConst = 150.0 # length constant for conn probability (um)\n",
+    "\n",
+    "#------------------------------------------------------------------------------\n",
+    "## Cell parameters\n",
+    "netParams.loadCellParams(label='E', fileName='cells/CSTR_cellParams.json')\n",
+    "netParams.importCellParams(label='I', fileName='cells/FScell.hoc', cellName='FScell')\n",
+    "\n",
+    "\n",
+    "#------------------------------------------------------------------------------\n",
+    "## Population parameters\n",
+    "netParams.popParams['E2'] = {'cellType': 'E', 'numCells': 10, 'yRange': [50,150]}\n",
+    "netParams.popParams['I2'] = {'cellType': 'I', 'numCells': 10, 'yRange': [50,150]}\n",
+    "netParams.popParams['E4'] = {'cellType': 'E', 'numCells': 10, 'yRange': [150,300]}\n",
+    "netParams.popParams['I4'] = {'cellType': 'I', 'numCells': 10, 'yRange': [150,300]}\n",
+    "netParams.popParams['E5'] = {'cellType': 'E', 'numCells': 10, 'ynormRange': [0.6,1.0]}\n",
+    "netParams.popParams['I5'] = {'cellType': 'I', 'numCells': 10, 'ynormRange': [0.6,1.0]}\n",
+    "\n",
+    "#------------------------------------------------------------------------------\n",
+    "## Synaptic mechanism parameters\n",
+    "netParams.synMechParams['exc'] = {'mod': 'Exp2Syn', 'tau1': 0.8, 'tau2': 5.3, 'e': 0}  # NMDA synaptic mechanism\n",
+    "netParams.synMechParams['inh'] = {'mod': 'Exp2Syn', 'tau1': 0.6, 'tau2': 8.5, 'e': -75}  # GABA synaptic mechanism\n",
+    "\n",
+    "#------------------------------------------------------------------------------\n",
+    "# Stimulation parameters\n",
+    "netParams.stimSourceParams['bkg'] = {'type': 'NetStim', 'rate': 20, 'noise': 0.3}\n",
+    "netParams.stimTargetParams['bkg->E'] = {'source': 'bkg', 'conds': {'cellType': ['E']}, 'weight': 0.02, 'sec': 'soma', 'delay': 'max(1, normal(5,2))', 'synMech': 'exc'}\n",
+    "netParams.stimTargetParams['bkg->I'] = {'source': 'bkg', 'conds': {'cellType': ['I']}, 'weight': 0.004, 'sec': 'soma', 'delay': 'max(1, normal(5,2))', 'synMech': 'exc'}\n",
+    "\n",
+    "#------------------------------------------------------------------------------\n",
+    "# Cell connectivity rules\n",
+    "netParams.connParams['E->all'] = {\n",
+    "  'preConds': {'cellType': 'E'}, 'postConds': {'y': [50,500]},  #  E -> all (100-1000 um)\n",
+    "  'probability': 0.1,                  # probability of connection\n",
+    "  'weight': '0.04*post_ynorm',         # synaptic weight \n",
+    "  'delay': 'dist_3D/propVelocity',      # transmission delay (ms) \n",
+    "  'synMech': 'exc'}                     # synaptic mechanism \n",
+    "\n",
+    "netParams.connParams['I->E'] = {\n",
+    "  'preConds': {'cellType': 'I'}, 'postConds': {'pop': ['E2','E4','E5']},       #  I -> E\n",
+    "  'probability': '0.3*exp(-dist_3D/probLengthConst)',   # probability of connection\n",
+    "  'weight': 0.01,                                      # synaptic weight \n",
+    "  'delay': 'dist_3D/propVelocity',                      # transmission delay (ms) \n",
+    "  'sec': ['soma','Bdend'], \n",
+    "  'synMech': 'inh'}                                     # synaptic mechanism \n",
+    "\n",
+    "\n",
+    "#------------------------------------------------------------------------------\n",
+    "## RxD params\n",
+    "\n",
+    "### constants\n",
+    "\n",
+    "## Change ip3_init from 0 to 0.1 to observe multiscale effect:  \n",
+    "## netParams.rxdParams['constants']['ip3_init'] = 0.1\n",
+    "## high ip3 -> ER Ca released to Cyt -> kBK channels open -> less firing \n",
+    "\n",
+    "constants = {'ip3_init': 0.0,  # initial ip3 concentration \n",
+    "            'caDiff': 0.08,  # calcium diffusion coefficient\n",
+    "            'ip3Diff': 1.41,  # ip3 diffusion coefficient\n",
+    "            'caci_init': 1e-5,  # intracellular calcium initial concentration\n",
+    "            'caco_init': 2.0,   # extracellular calcium initial concentration\n",
+    "            'gip3r': 12040 * 100,  # ip3 receptors density\n",
+    "            'gserca': 0.3913,  # SERCA conductance\n",
+    "            'gleak': 6.020,   # ER leak channel conductance\n",
+    "            'kserca': 0.1,  # SERCA reaction constant\n",
+    "            'kip3': 0.15,  # ip3 reaction constant\n",
+    "            'kact': 0.4,  #\n",
+    "            'ip3rtau': 2000,  # ip3 receptors time constant\n",
+    "            'fc': 0.8,  # fraction of cytosol\n",
+    "            'fe': 0.2,  # fraction of ER\n",
+    "            'margin': 20}  # extracellular volume additional margin \n",
+    "\n",
+    "netParams.rxdParams['constants'] = constants\n",
+    "\n",
+    "### regions\n",
+    "regions = {}\n",
+    "regions['cyt'] = {'cells': 'all', 'secs': 'all', 'nrn_region': 'i', 'geometry': {'class': 'FractionalVolume', 'args': {'volume_fraction': constants['fc'], 'surface_fraction': 1}}}\n",
+    "regions['er'] = {'cells': 'all', 'secs': 'all', 'geometry': {'class': 'FractionalVolume', 'args': {'volume_fraction': constants['fe']}}}\n",
+    "regions['cyt_er_membrane'] = {'cells': 'all', 'secs': 'all', 'geometry': {'class': 'ScalableBorder', 'args': {'scale': 1, 'on_cell_surface': False}}}\n",
+    "\n",
+    "margin = 20  # extracellular volume additional margin \n",
+    "x, y, z = [0-margin, 100+margin], [-500-margin, 0+margin], [0-margin, 100+margin]\n",
+    "regions['ecs'] = {'extracellular': True, 'xlo': x[0], 'ylo': y[0], 'zlo': z[0], 'xhi': x[1], 'yhi': y[1], 'zhi': z[1], 'dx': 5, 'volume_fraction': 0.2, 'tortuosity': 1.6} \n",
+    "\n",
+    "netParams.rxdParams['regions'] = regions\n",
+    "\n",
+    "### species \n",
+    "species = {}\n",
+    "species['ca'] = {'regions': ['cyt', 'er', 'ecs'], 'd': constants['caDiff'], 'charge': 2,\n",
+    "                'initial': 'caco_init if isinstance(node,rxd.node.NodeExtracellular) else (0.0017 - caci_init * fc) / fe if node.region == er else caci_init'}\n",
+    "species['ip3'] = {'regions': ['cyt'], 'd': constants['ip3Diff'], 'initial': constants['ip3_init']}\n",
+    "netParams.rxdParams['species'] = species\n",
+    "\n",
+    "### states\n",
+    "netParams.rxdParams['states'] = {'ip3r_gate_state': {'regions': ['cyt_er_membrane'], 'initial': 0.8}}\n",
+    "\n",
+    "### reactions\n",
+    "minf = 'ip3[cyt] * 1000. * ca[cyt] / (ip3[cyt] + kip3) / (1000. * ca[cyt] + kact)'\n",
+    "h_gate = 'ip3r_gate_state[cyt_er_membrane]'\n",
+    "kip3 = 'gip3r * (%s * %s) ** 3' % (minf, h_gate)\n",
+    "\n",
+    "mcReactions = {}\n",
+    "mcReactions['serca'] = {'reactant': 'ca[cyt]', 'product': 'ca[er]', 'rate_f': 'gserca / ((kserca / (1000. * ca[cyt])) ** 2 + 1)', 'membrane': 'cyt_er_membrane', 'custom_dynamics': True}\n",
+    "mcReactions['leak'] = {'reactant': 'ca[er]', 'product': 'ca[cyt]', 'rate_f': constants['gleak'], 'rate_b': constants['gleak'], 'membrane': 'cyt_er_membrane'}\n",
+    "mcReactions['ip3r'] = {'reactant': 'ca[er]', 'product': 'ca[cyt]', 'rate_f': kip3, 'rate_b': kip3, 'membrane': 'cyt_er_membrane'}\n",
+    "netParams.rxdParams['multicompartmentReactions'] = mcReactions\n",
+    "\n",
+    "### rates\n",
+    "netParams.rxdParams['rates'] = {'ip3rg': {'species': h_gate, 'rate': '(1. / (1 + 1000. * ca[cyt] / (0.3)) - %s) / ip3rtau'%(h_gate)}}\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "#------------------------------------------------------------------------------\n",
+    "#\n",
+    "# SIMULATION CONFIGURATION\n",
+    "#\n",
+    "#------------------------------------------------------------------------------\n",
+    "\n",
+    "# Run parameters\n",
+    "simConfig = specs.SimConfig()       # object of class simConfig to store simulation configuration\n",
+    "simConfig.duration = 1.0*1e3        # Duration of the simulation, in ms\n",
+    "simConfig.hParams['v_init'] = -65   # set v_init to -65 mV\n",
+    "simConfig.dt = 0.1                  # Internal integration timestep to use\n",
+    "simConfig.verbose = False            # Show detailed messages \n",
+    "simConfig.recordStep = 1             # Step size in ms to save data (eg. V traces, LFP, etc)\n",
+    "simConfig.filename = 'rxd_net'   # Set file output name\n",
+    "\n",
+    "\n",
+    "# Recording/plotting parameters\n",
+    "simConfig.recordTraces = {'V_soma':{'sec': 'soma','loc': 0.5,'var': 'v'},\n",
+    "                          'ik_soma': {'sec': 'soma', 'loc': 0.5, 'var': 'ik'},\n",
+    "                          'cai_soma': {'sec': 'soma', 'loc':0.5, 'var': 'cai'},\n",
+    "                          'cao_soma': {'sec': 'soma', 'loc': 0.5, 'var': 'cao'}}\n",
+    "\n",
+    "simConfig.recordLFP = [[-15, y, 1.0*netParams.sizeZ] for y in range(int(netParams.sizeY/3), int(netParams.sizeY), int(netParams.sizeY/3))]\n",
+    "\n",
+    "#simConfig.analysis['iplotTraces'] ={'include': [0]}\n",
+    "simConfig.analysis['plotTraces'] = {'include': [('E2', 0), ('I2', 0), ('E4', 0), ('I4', 0), ('E5', 0), ('I5', 0)]}\n",
+    "\n",
+    "#simConfig.analysis['iplotRaster'] = {'orderBy': 'y', 'orderInverse': True, 'saveFig': True, 'figSize': (9,3)}      # Plot a raster\n",
+    "#simConfig.analysis['iplotLFP'] = {'includeAxon': False, 'figSize': (6,10), 'saveFig': True} \n",
+    "#simConfig.analysis['iplotRxDConcentration'] = {'speciesLabel': 'ca', 'regionLabel': 'ecs'}\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "grBSV-Q7B7m0"
+   },
+   "source": [
+    "Now we will run the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "YxETBf6o4ZKo"
+   },
+   "outputs": [],
+   "source": [
+    "from netpyne import sim\n",
+    "sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "YdFBW3FLCLnN"
+   },
+   "source": [
+    "Now we can begin exploring the analyses available in NetPyNE.\n",
+    "\n",
+    "## Analyses in NetPyNE\n",
+    "\n",
+    "Let's take a look at the NetPyNE Package Index for analysis:\n",
+    "http://netpyne.org/netpyne.analysis.html#module-netpyne.analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "y1KUEz4k5WRv"
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "HnfgLlZ-I1BR"
+   },
+   "outputs": [],
+   "source": [
+    "sa = sim.analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 732
+    },
+    "executionInfo": {
+     "elapsed": 1699,
+     "status": "ok",
+     "timestamp": 1623997129802,
+     "user": {
+      "displayName": "Evgenia Karunus",
+      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhA2ccNLhFnt0hUfl13RoguCJ6cDbCPCY8_SVjQAg=s64",
+      "userId": "04024508215281503990"
+     },
+     "user_tz": -300
+    },
+    "id": "6tdR7PeEJ7IN",
+    "outputId": "e005a06b-63a0-4039-c4e0-1c89ea61f510"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plot2Dnet();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "W8gBC8ctJ6-X"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plot2Dnet(include=['E2', 'E4', 'E5']);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "mCZYDT0vUYKO"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plot2Dnet(view='xz');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "vxUwdfpcKAJa"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plotConn();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "8VfaAnOpKGCL"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plotConn(includePre=['E2', 'E4', 'E5'], includePost=['I2', 'I4', 'I5']);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "iSs0pXZL8UHl"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plotConn(feature='numConns');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "7zkzJ67eJN5F"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plotConn(groupBy='cell', feature='weight');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "cMdkvqfsWG_7"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plotConn(groupBy='cell', feature='weight', orderBy='y');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "pMOGSbkVG74P"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plotRateSpectrogram();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "AitZZEXRG8rC"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plotRateSpectrogram(include=['allCells']);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "jjQvp_vjXJoY"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plotRateSpectrogram(include=['allCells'], timeRange=[0, 400]);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "KaKF75Z7IJW5"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plotSpikeHist();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ZmBxr458X1MD"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plotSpikeHist(binSize=20);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "pSpumAJSX9TR"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plotSpikeHist(binSize=20, measure='count');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "xiOjsRCIIJd5"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plotSpikeStats();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Rrg-G0OvYcDD"
+   },
+   "outputs": [],
+   "source": [
+    "fig, data = sa.plotSpikeStats(stats=['rate', 'isicv']);\n",
+    "# more options are 'sync' and 'pairsync', but they require:\n",
+    "# !pip install pyspike"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "7-Fy9mfaIJol"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plotTraces();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "p0HU13QRIJq-"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plotTraces(oneFigPer='trace');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 164
+    },
+    "executionInfo": {
+     "elapsed": 155,
+     "status": "error",
+     "timestamp": 1624138083622,
+     "user": {
+      "displayName": "Jessica Feldman",
+      "photoUrl": "",
+      "userId": "14873178662011488670"
+     },
+     "user_tz": 240
+    },
+    "id": "Mj7oWGhNawI7",
+    "outputId": "cdb86540-1f11-461a-abd3-6fe26ee857e3"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plotTraces(oneFigPer='trace', overlay=True);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "gjjzCJA2a_Do"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plotTraces(oneFigPer='trace', overlay=True, axis=False);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "RGPPfq8HbReU"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plotRaster();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "6aX4Bz16bojv"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plotRaster(orderBy='y');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "AXTW0XTab29b"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plotRaster(orderInverse=True, popRates=True);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "rb2422fscOKG"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plotRaster(orderInverse=True, labels='overlay');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "FR1lkfY9cc2V"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plotRaster(orderInverse=True, spikeHist='subplot');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "I84uEulgcrow"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plotRaster(orderInverse=True, syncLines=True);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "z-2N6_5ic7Q9"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plotRaster(orderInverse=True, marker='o');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "xv6wtVSXdJSq"
+   },
+   "outputs": [],
+   "source": [
+    "colors = {'E2': 'red'}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Y0yZIQ3TdUZt"
+   },
+   "outputs": [],
+   "source": [
+    "colors['E4'] = 'pink'\n",
+    "colors['E5'] = 'orange'\n",
+    "colors['I2'] = 'blue'\n",
+    "colors['I4'] = 'purple'\n",
+    "colors['I5'] = 'black'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "c2ePsdhYdpdC"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plotRaster(orderInverse=True, marker='o', popColors=colors);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "f8c0KO_SIJta"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plotLFP();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "hG07GrYWIJv3"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "UbnIkgDiIJyP"
+   },
+   "outputs": [],
+   "source": [
+    "sa.plotRxDConcentration(speciesLabel='ca', regionLabel='ecs');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Z073XSVZIJ0Z"
+   },
+   "outputs": [],
+   "source": [
+    "for species in ['ca', 'ip3']:\n",
+    "    for region in ['cyt', 'er', 'cyt_er_membrane', 'ecs']:\n",
+    "        sa.plotRxDConcentration(speciesLabel=species, regionLabel=region);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "TKRh_vTPIJ6v"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "a2WthFkxIJ9E"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "XD7E_q-eIJ_U"
+   },
+   "outputs": [],
+   "source": [
+    "sim.simData"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "djzxMEVZIKBz"
+   },
+   "outputs": [],
+   "source": [
+    "from IPython import display\n",
+    "display.Image(\"docs/netstruct.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sim.net.cells[0].secs.soma"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [],
+   "name": "NetPyNE2021_Analysis_Plotting.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/netpyne/tutorials/tut06_oscillation.ipynb b/netpyne/tutorials/tut06_oscillation.ipynb
new file mode 100644
index 000000000..da302d19a
--- /dev/null
+++ b/netpyne/tutorials/tut06_oscillation.ipynb
@@ -0,0 +1,316 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# NetPyNE Tutorial 6: Oscillations\n",
+    "\n",
+    "This tutorial shows an example of a simple two-populations network that exhibits oscillatory behaviour. In the end you are proposed to try to modifiy different parameters to change the freqency of observed oscillations."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preliminaries\n",
+    "\n",
+    "If you are going to run this notebook locally using Jupyter Notebook, start from following instructions https://github.com/Neurosim-lab/netpyne/blob/development/netpyne/tutorials/README.md.\n",
+    "\n",
+    "If you are using Open Source Brain or EBRAINS, everything is already set up.\n",
+    "\n",
+    "On any other online platform (e.g. on Google Collab) you might need to run the following commmands to install NEURON and NetPyNE using **pip**:\n",
+    "```\n",
+    "!pip install neuron\n",
+    "!pip install netpyne\n",
+    "```\n",
+    "\n",
+    "Now we are ready to start the tutorial."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "executionInfo": {
+     "elapsed": 5480,
+     "status": "ok",
+     "timestamp": 1621462283899,
+     "user": {
+      "displayName": "Salvador Dura-Bernal",
+      "photoUrl": "",
+      "userId": "10473966374056868820"
+     },
+     "user_tz": 240
+    },
+    "id": "f0P--qg5YUT6",
+    "outputId": "a0d41a9f-ac05-434f-dc1f-2182ad919206"
+   },
+   "outputs": [],
+   "source": [
+    "from netpyne import specs, sim\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Network parameters\n",
+    "netParams = specs.NetParams()  # object of class NetParams to store the network parameters\n",
+    "\n",
+    "\n",
+    "## Cell parameters\n",
+    "netParams.cellParams['pyr'] = {\n",
+    "    'secs': {\n",
+    "        'soma': {\n",
+    "            'geom': {\n",
+    "                'diam': 18.8,   \n",
+    "                'L': 18.8, \n",
+    "                'Ra': 123.0},\n",
+    "            'mechs': {\n",
+    "                'hh': {\n",
+    "                    'gnabar': 0.12, \n",
+    "                    'gkbar': 0.036, \n",
+    "                    'gl': 0.0003, \n",
+    "                    'el': -70}\n",
+    "            }\n",
+    "        }\n",
+    "    }\n",
+    "}                \n",
+    "\n",
+    "\n",
+    "## Population parameters\n",
+    "netParams.popParams['E'] = {\n",
+    "    'cellType': 'pyr', \n",
+    "    'numCells': 20}\n",
+    "\n",
+    "netParams.popParams['I'] = {\n",
+    "    'cellType': 'pyr', \n",
+    "    'numCells': 20}\n",
+    "\n",
+    "\n",
+    "## Synaptic mechanism parameters\n",
+    "netParams.synMechParams['exc'] = {\n",
+    "    'mod': 'Exp2Syn', \n",
+    "    'tau1': 0.1, \n",
+    "    'tau2': 5.0, \n",
+    "    'e': 0}  # excitatory synaptic mechanism\n",
+    "\n",
+    "netParams.synMechParams['inh'] = {\n",
+    "    'mod': 'Exp2Syn', \n",
+    "    'tau1': 0.1, \n",
+    "    'tau2': 5.0, \n",
+    "    'e': -70}  # inhibitory synaptic mechanism\n",
+    "\n",
+    "\n",
+    "# Stimulation parameters\n",
+    "netParams.stimSourceParams['bkg'] = {\n",
+    "    'type': 'NetStim', \n",
+    "    'rate': 50, \n",
+    "    'noise': 0.5}\n",
+    "    \n",
+    "netParams.stimTargetParams['bkg->E'] = {\n",
+    "    'source': 'bkg', \n",
+    "    'conds': {'pop': 'E'}, \n",
+    "    'weight': 0.01, \n",
+    "    'delay': 5, \n",
+    "    'synMech': 'exc'}\n",
+    "\n",
+    "\n",
+    "## Connectivity rules\n",
+    "netParams.connParams['E->I'] = {    #  S -> I label\n",
+    "    'preConds': {'pop': 'E'},       # conditions of presyn cells\n",
+    "    'postConds': {'pop': 'I'},      # conditions of postsyn cells\n",
+    "    'divergence': 5,               # probability of connection\n",
+    "    'weight': 0.01,                 # synaptic weight\n",
+    "    'delay': 5,                     # transmission delay (ms)\n",
+    "    'synMech': 'exc'}               # synaptic mechanism\n",
+    "\n",
+    "netParams.connParams['I->E'] = {    #  I -> S label\n",
+    "    'preConds': {'pop': 'I'},       # conditions of presyn cells\n",
+    "    'postConds': {'pop': 'E'},      # conditions of postsyn cells\n",
+    "    'probability': 0.7,               # probability of connection\n",
+    "    'weight': 0.02,                 # synaptic weight\n",
+    "    'delay': 5,                     # transmission delay (ms)\n",
+    "    'synMech': 'inh'}               # synaptic mechanism\n",
+    "\n",
+    "\n",
+    "# Simulation options\n",
+    "simConfig = specs.SimConfig()       # object of class SimConfig to store simulation configuration\n",
+    "\n",
+    "simConfig.duration = 1*1e3          # Duration of the simulation, in ms\n",
+    "simConfig.dt = 0.01                # Internal integration timestep to use\n",
+    "simConfig.verbose = False           # Show detailed messages\n",
+    "simConfig.recordTraces = {'V_soma':{'sec':'soma','loc':0.5,'var':'v'}}  # Dict with traces to record\n",
+    "simConfig.recordStep = 0.1          # Step size in ms to save data (eg. V traces, LFP, etc)\n",
+    "simConfig.filename = 'tut_oscillation'  # Set file output name\n",
+    "simConfig.saveJson = False\n",
+    "\n",
+    "simConfig.recordLFP = [[50, 50, 50]]\n",
+    "simConfig.recordDipole = True\n",
+    "\n",
+    "simConfig.analysis['plotTraces'] = {'include': [1], 'saveFig': True}  # Plot recorded traces for this list of cells\n",
+    "simConfig.analysis['plotRaster'] = {'showFig': True}                  # Plot a raster\n",
+    "simConfig.analysis['plotSpikeHist'] = {'include': ['E', 'I'], 'showFig': True}\n",
+    "simConfig.analysis['plotRateSpectrogram'] = {'include': ['all'], 'saveFig': True}\n",
+    "\n",
+    "\n",
+    "# Create network and run simulation\n",
+    "sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "executionInfo": {
+     "elapsed": 5480,
+     "status": "ok",
+     "timestamp": 1621462283899,
+     "user": {
+      "displayName": "Salvador Dura-Bernal",
+      "photoUrl": "",
+      "userId": "10473966374056868820"
+     },
+     "user_tz": 240
+    },
+    "id": "f0P--qg5YUT6",
+    "outputId": "a0d41a9f-ac05-434f-dc1f-2182ad919206"
+   },
+   "outputs": [],
+   "source": [
+    "sim.plotting.plotLFPTimeSeries(electrodes=[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "executionInfo": {
+     "elapsed": 5480,
+     "status": "ok",
+     "timestamp": 1621462283899,
+     "user": {
+      "displayName": "Salvador Dura-Bernal",
+      "photoUrl": "",
+      "userId": "10473966374056868820"
+     },
+     "user_tz": 240
+    },
+    "id": "f0P--qg5YUT6",
+    "outputId": "a0d41a9f-ac05-434f-dc1f-2182ad919206"
+   },
+   "outputs": [],
+   "source": [
+    "sim.plotting.plotLFPSpectrogram(electrodes=[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "executionInfo": {
+     "elapsed": 5480,
+     "status": "ok",
+     "timestamp": 1621462283899,
+     "user": {
+      "displayName": "Salvador Dura-Bernal",
+      "photoUrl": "",
+      "userId": "10473966374056868820"
+     },
+     "user_tz": 240
+    },
+    "id": "f0P--qg5YUT6",
+    "outputId": "a0d41a9f-ac05-434f-dc1f-2182ad919206"
+   },
+   "outputs": [],
+   "source": [
+    "sim.analysis.plotDipole()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "executionInfo": {
+     "elapsed": 5480,
+     "status": "ok",
+     "timestamp": 1621462283899,
+     "user": {
+      "displayName": "Salvador Dura-Bernal",
+      "photoUrl": "",
+      "userId": "10473966374056868820"
+     },
+     "user_tz": 240
+    },
+    "id": "f0P--qg5YUT6",
+    "outputId": "a0d41a9f-ac05-434f-dc1f-2182ad919206"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install h5py\n",
+    "sim.analysis.plotEEG()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "P8deeT59anO8"
+   },
+   "source": [
+    "## Hands-on exercise\n",
+    "\n",
+    "Modify parameters and rerun the model to get different oscillation frequencies.\n",
+    "\n",
+    "Hint 1: try changing connection parameters (weights, probability, delay, synaptic time constants) and cell biophysics \n",
+    "\n",
+    "Hint 2: if you want the simulation to run faster comment out the `simConfig.recordLFP = [[50, 50, 50]]` and `simConfig.recordDipole = True` lines"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [],
+   "name": "tut_netpyne_osc_start.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/netpyne/tutorials/tut07_multiscale_network.ipynb b/netpyne/tutorials/tut07_multiscale_network.ipynb
new file mode 100644
index 000000000..2470fb7ec
--- /dev/null
+++ b/netpyne/tutorials/tut07_multiscale_network.ipynb
@@ -0,0 +1,394 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# NetPyNE Tutorial 7: Multiscale Network\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preliminaries\n",
+    "\n",
+    "If you are going to run this notebook locally using Jupyter Notebook, start from following instructions https://github.com/Neurosim-lab/netpyne/blob/development/netpyne/tutorials/README.md.\n",
+    "\n",
+    "If you are using Open Source Brain or EBRAINS, everything is already set up.\n",
+    "\n",
+    "On any other online platform (e.g. on Google Collab) you might need to run the following commmands to install NEURON and NetPyNE using **pip**:\n",
+    "```\n",
+    "!pip install neuron\n",
+    "!pip install netpyne\n",
+    "```\n",
+    "\n",
+    "Now we are ready to start the tutorial."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Compile mechanisms\n",
+    "\n",
+    "The cell models require membrane mechanisms (e.g. channel models) that are not built-in to NEURON.  So, now we will compile the necessary mechanisms (in the `mod` directory) using `nrnivmodl`.  You can learn more about mechanisms and `.mod` files in the [NEURON documentation](https://www.neuron.yale.edu/neuron/static/py_doc/modelspec/programmatic/mechanisms/nmodl.html). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!nrnivmodl mod"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "executionInfo": {
+     "elapsed": 5480,
+     "status": "ok",
+     "timestamp": 1621462283899,
+     "user": {
+      "displayName": "Salvador Dura-Bernal",
+      "photoUrl": "",
+      "userId": "10473966374056868820"
+     },
+     "user_tz": 240
+    },
+    "id": "f0P--qg5YUT6",
+    "outputId": "a0d41a9f-ac05-434f-dc1f-2182ad919206"
+   },
+   "outputs": [],
+   "source": [
+    "from netpyne import specs, sim\n",
+    "\n",
+    "# should go after importing netpyne\n",
+    "import matplotlib\n",
+    "%matplotlib inline\n",
+    "\n",
+    "#------------------------------------------------------------------------------\n",
+    "#\n",
+    "# NETWORK PARAMETERS\n",
+    "#\n",
+    "#------------------------------------------------------------------------------\n",
+    "\n",
+    "netParams = specs.NetParams()  # object of class NetParams to store the network parameters\n",
+    "\n",
+    "netParams.sizeX = 100 # x-dimension (horizontal length) size in um\n",
+    "netParams.sizeY = 500 # y-dimension (vertical height or cortical depth) size in um\n",
+    "netParams.sizeZ = 100 # z-dimension (horizontal length) size in um\n",
+    "netParams.propVelocity = 100.0 # propagation velocity (um/ms)\n",
+    "netParams.probLengthConst = 150.0 # length constant for conn probability (um)\n",
+    "\n",
+    "#------------------------------------------------------------------------------\n",
+    "## Cell parameters\n",
+    "netParams.loadCellParams(label='E', fileName='cells/CSTR_cellParams.json')\n",
+    "netParams.importCellParams(label='I', fileName='cells/FScell.hoc', cellName='FScell')\n",
+    "\n",
+    "\n",
+    "#------------------------------------------------------------------------------\n",
+    "## Population parameters\n",
+    "netParams.popParams['E2'] = {'cellType': 'E', 'numCells': 10, 'yRange': [50,150]}\n",
+    "netParams.popParams['I2'] = {'cellType': 'I', 'numCells': 10, 'yRange': [50,150]}\n",
+    "netParams.popParams['E4'] = {'cellType': 'E', 'numCells': 10, 'yRange': [150,300]}\n",
+    "netParams.popParams['I4'] = {'cellType': 'I', 'numCells': 10, 'yRange': [150,300]}\n",
+    "netParams.popParams['E5'] = {'cellType': 'E', 'numCells': 10, 'ynormRange': [0.6,1.0]}\n",
+    "netParams.popParams['I5'] = {'cellType': 'I', 'numCells': 10, 'ynormRange': [0.6,1.0]}\n",
+    "\n",
+    "#------------------------------------------------------------------------------\n",
+    "## Synaptic mechanism parameters\n",
+    "netParams.synMechParams['exc'] = {'mod': 'Exp2Syn', 'tau1': 0.8, 'tau2': 5.3, 'e': 0}  # NMDA synaptic mechanism\n",
+    "netParams.synMechParams['inh'] = {'mod': 'Exp2Syn', 'tau1': 0.6, 'tau2': 8.5, 'e': -75}  # GABA synaptic mechanism\n",
+    "\n",
+    "#------------------------------------------------------------------------------\n",
+    "# Stimulation parameters\n",
+    "netParams.stimSourceParams['bkg'] = {'type': 'NetStim', 'rate': 20, 'noise': 0.3}\n",
+    "netParams.stimTargetParams['bkg->E'] = {'source': 'bkg', 'conds': {'cellType': ['E']}, 'weight': 0.02, 'sec': 'soma', 'delay': 'max(1, normal(5,2))', 'synMech': 'exc'}\n",
+    "netParams.stimTargetParams['bkg->I'] = {'source': 'bkg', 'conds': {'cellType': ['I']}, 'weight': 0.004, 'sec': 'soma', 'delay': 'max(1, normal(5,2))', 'synMech': 'exc'}\n",
+    "\n",
+    "#------------------------------------------------------------------------------\n",
+    "# Cell connectivity rules\n",
+    "netParams.connParams['E->all'] = {\n",
+    "  'preConds': {'cellType': 'E'}, \n",
+    "  'postConds': {'y': [50,500]},  #  E -> all (100-1000 um)\n",
+    "  'probability': 0.1,                  # probability of connection\n",
+    "  'weight': '0.04*post_ynorm',         # synaptic weight \n",
+    "  'delay': 'dist_3D/propVelocity',      # transmission delay (ms) \n",
+    "  'synMech': 'exc'}                     # synaptic mechanism \n",
+    "\n",
+    "netParams.connParams['I->E'] = {\n",
+    "  'preConds': {'cellType': 'I'}, \n",
+    "  'postConds': {'pop': ['E2','E4','E5']},       #  I -> E\n",
+    "  'probability': '0.3*exp(-dist_3D/probLengthConst)',   # probability of connection\n",
+    "  'weight': 0.01,                                      # synaptic weight \n",
+    "  'delay': 'dist_3D/propVelocity',                      # transmission delay (ms) \n",
+    "  'sec': ['soma','Bdend'], \n",
+    "  'synMech': 'inh'}                                     # synaptic mechanism \n",
+    "\n",
+    "\n",
+    "\n",
+    "#------------------------------------------------------------------------------\n",
+    "## RxD params\n",
+    "\n",
+    "### constants\n",
+    "\n",
+    "## Change ip3_init from 0 to 0.1 to observe multiscale effect:  \n",
+    "## netParams.rxdParams['constants']['ip3_init'] = 0.1\n",
+    "## high ip3 -> ER Ca released to Cyt -> kBK channels open -> less firing \n",
+    "\n",
+    "constants = {'ip3_init': 0.0,  # initial ip3 concentration \n",
+    "            'caDiff': 0.08,  # calcium diffusion coefficient\n",
+    "            'ip3Diff': 1.41,  # ip3 diffusion coefficient\n",
+    "            'caci_init': 1e-5,  # intracellular calcium initial concentration\n",
+    "            'caco_init': 2.0,   # extracellular calcium initial concentration\n",
+    "            'gip3r': 12040 * 100,  # ip3 receptors density\n",
+    "            'gserca': 0.3913,  # SERCA conductance\n",
+    "            'gleak': 6.020,   # ER leak channel conductance\n",
+    "            'kserca': 0.1,  # SERCA reaction constant\n",
+    "            'kip3': 0.15,  # ip3 reaction constant\n",
+    "            'kact': 0.4,  #\n",
+    "            'ip3rtau': 2000,  # ip3 receptors time constant\n",
+    "            'fc': 0.8,  # fraction of cytosol\n",
+    "            'fe': 0.2,  # fraction of ER\n",
+    "            'margin': 20}  # extracellular volume additional margin \n",
+    "\n",
+    "netParams.rxdParams['constants'] = constants\n",
+    "\n",
+    "### regions\n",
+    "regions = {}\n",
+    "regions['cyt'] = {'cells': 'all', 'secs': 'all', 'nrn_region': 'i', 'geometry': {'class': 'FractionalVolume', 'args': {'volume_fraction': constants['fc'], 'surface_fraction': 1}}}\n",
+    "regions['er'] = {'cells': 'all', 'secs': 'all', 'geometry': {'class': 'FractionalVolume', 'args': {'volume_fraction': constants['fe']}}}\n",
+    "regions['cyt_er_membrane'] = {'cells': 'all', 'secs': 'all', 'geometry': {'class': 'ScalableBorder', 'args': {'scale': 1, 'on_cell_surface': False}}}\n",
+    "\n",
+    "margin = 20  # extracellular volume additional margin \n",
+    "x, y, z = [0-margin, 100+margin], [-500-margin, 0+margin], [0-margin, 100+margin]\n",
+    "regions['ecs'] = {'extracellular': True, 'xlo': x[0], 'ylo': y[0], 'zlo': z[0], 'xhi': x[1], 'yhi': y[1], 'zhi': z[1], 'dx': 5, 'volume_fraction': 0.2, 'tortuosity': 1.6} \n",
+    "\n",
+    "netParams.rxdParams['regions'] = regions\n",
+    "\n",
+    "### species \n",
+    "species = {}\n",
+    "species['ca'] = {'regions': ['cyt', 'er', 'ecs'], 'd': constants['caDiff'], 'charge': 2,\n",
+    "                'initial': 'caco_init if isinstance(node,rxd.node.NodeExtracellular) else (0.0017 - caci_init * fc) / fe if node.region == er else caci_init'}\n",
+    "species['ip3'] = {'regions': ['cyt'], 'd': constants['ip3Diff'], 'initial': constants['ip3_init']}\n",
+    "netParams.rxdParams['species'] = species\n",
+    "\n",
+    "### states\n",
+    "netParams.rxdParams['states'] = {'ip3r_gate_state': {'regions': ['cyt_er_membrane'], 'initial': 0.8}}\n",
+    "\n",
+    "### reactions\n",
+    "minf = 'ip3[cyt] * 1000. * ca[cyt] / (ip3[cyt] + kip3) / (1000. * ca[cyt] + kact)'\n",
+    "h_gate = 'ip3r_gate_state[cyt_er_membrane]'\n",
+    "kip3 = 'gip3r * (%s * %s) ** 3' % (minf, h_gate)\n",
+    "\n",
+    "mcReactions = {}\n",
+    "mcReactions['serca'] = {'reactant': 'ca[cyt]', 'product': 'ca[er]', 'rate_f': 'gserca / ((kserca / (1000. * ca[cyt])) ** 2 + 1)', 'membrane': 'cyt_er_membrane', 'custom_dynamics': True}\n",
+    "mcReactions['leak'] = {'reactant': 'ca[er]', 'product': 'ca[cyt]', 'rate_f': constants['gleak'], 'rate_b': constants['gleak'], 'membrane': 'cyt_er_membrane'}\n",
+    "mcReactions['ip3r'] = {'reactant': 'ca[er]', 'product': 'ca[cyt]', 'rate_f': kip3, 'rate_b': kip3, 'membrane': 'cyt_er_membrane'}\n",
+    "netParams.rxdParams['multicompartmentReactions'] = mcReactions\n",
+    "\n",
+    "### rates\n",
+    "netParams.rxdParams['rates'] = {'ip3rg': {'species': h_gate, 'rate': '(1. / (1 + 1000. * ca[cyt] / (0.3)) - %s) / ip3rtau'%(h_gate)}}\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "#------------------------------------------------------------------------------\n",
+    "#\n",
+    "# SIMULATION CONFIGURATION\n",
+    "#\n",
+    "#------------------------------------------------------------------------------\n",
+    "\n",
+    "# Run parameters\n",
+    "simConfig = specs.SimConfig()       # object of class simConfig to store simulation configuration\n",
+    "simConfig.duration = 1.0*1e3        # Duration of the simulation, in ms\n",
+    "simConfig.hParams['v_init'] = -65   # set v_init to -65 mV\n",
+    "simConfig.dt = 0.1                  # Internal integration timestep to use\n",
+    "simConfig.verbose = False            # Show detailed messages \n",
+    "simConfig.recordStep = 1             # Step size in ms to save data (eg. V traces, LFP, etc)\n",
+    "simConfig.filename = 'rxd_net'   # Set file output name\n",
+    "\n",
+    "\n",
+    "# Recording/plotting parameters\n",
+    "simConfig.recordTraces = {'V_soma':{'sec': 'soma','loc': 0.5,'var': 'v'},\n",
+    "                          'ik_soma': {'sec': 'soma', 'loc': 0.5, 'var': 'ik'},\n",
+    "                          'cai_soma': {'sec': 'soma', 'loc':0.5, 'var': 'cai'},\n",
+    "                          'cao_soma': {'sec': 'soma', 'loc': 0.5, 'var': 'cao'}}\n",
+    "\n",
+    "simConfig.recordLFP = [[-15, y, 1.0*netParams.sizeZ] for y in range(int(netParams.sizeY/3), int(netParams.sizeY), int(netParams.sizeY/3))]\n",
+    "simConfig.recordDipole = True\n",
+    "\n",
+    "simConfig.analysis['plotTraces'] ={'include': [0]}\n",
+    "simConfig.analysis['plotRaster'] = {'orderBy': 'y', 'orderInverse': True, 'saveFig': True, 'figSize': (9,3)}      # Plot a raster\n",
+    "simConfig.analysis['plotLFP'] = {'includeAxon': False, 'figSize': (6,10), 'saveFig': True} \n",
+    "simConfig.analysis['plotRxDConcentration'] = {'speciesLabel': 'ca', 'regionLabel': 'ecs'}\n",
+    "\n",
+    "\n",
+    "#from netpyne import sim\n",
+    "sim.createSimulateAnalyze(netParams, simConfig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "executionInfo": {
+     "elapsed": 5480,
+     "status": "ok",
+     "timestamp": 1621462283899,
+     "user": {
+      "displayName": "Salvador Dura-Bernal",
+      "photoUrl": "",
+      "userId": "10473966374056868820"
+     },
+     "user_tz": 240
+    },
+    "id": "f0P--qg5YUT6",
+    "outputId": "a0d41a9f-ac05-434f-dc1f-2182ad919206"
+   },
+   "outputs": [],
+   "source": [
+    "sim.plotting.plotLFPTimeSeries()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "executionInfo": {
+     "elapsed": 5480,
+     "status": "ok",
+     "timestamp": 1621462283899,
+     "user": {
+      "displayName": "Salvador Dura-Bernal",
+      "photoUrl": "",
+      "userId": "10473966374056868820"
+     },
+     "user_tz": 240
+    },
+    "id": "f0P--qg5YUT6",
+    "outputId": "a0d41a9f-ac05-434f-dc1f-2182ad919206"
+   },
+   "outputs": [],
+   "source": [
+    "sim.plotting.plotLFPSpectrogram(electrodes=['avg'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "executionInfo": {
+     "elapsed": 5480,
+     "status": "ok",
+     "timestamp": 1621462283899,
+     "user": {
+      "displayName": "Salvador Dura-Bernal",
+      "photoUrl": "",
+      "userId": "10473966374056868820"
+     },
+     "user_tz": 240
+    },
+    "id": "f0P--qg5YUT6",
+    "outputId": "a0d41a9f-ac05-434f-dc1f-2182ad919206"
+   },
+   "outputs": [],
+   "source": [
+    "sim.analysis.plotDipole()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "executionInfo": {
+     "elapsed": 5480,
+     "status": "ok",
+     "timestamp": 1621462283899,
+     "user": {
+      "displayName": "Salvador Dura-Bernal",
+      "photoUrl": "",
+      "userId": "10473966374056868820"
+     },
+     "user_tz": 240
+    },
+    "id": "f0P--qg5YUT6",
+    "outputId": "a0d41a9f-ac05-434f-dc1f-2182ad919206"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install h5py\n",
+    "sim.analysis.plotEEG()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "P8deeT59anO8"
+   },
+   "source": [
+    "Try changing the 'ip3_init' concentration to 0.1 and checking the effect it has on LFP and EEG. \n",
+    "\n",
+    "This demonstrates the effects of changes at the molecular scale affect the cellular and circuit scales, and are reflected in large-scale recording modalities (LFP, EEG). "
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [],
+   "name": "tut_netpyne_osc_start.ipynb",
+   "provenance": []
+  },
+  "interpreter": {
+   "hash": "3c21b5cca3d53abc9a13c749d6f1f5a0a8097b3d1301b053fb89097dcdc54130"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/netpyne/tutorials/tut08_batch_grid.ipynb b/netpyne/tutorials/tut08_batch_grid.ipynb
new file mode 100644
index 000000000..33b85bec7
--- /dev/null
+++ b/netpyne/tutorials/tut08_batch_grid.ipynb
@@ -0,0 +1 @@
+{"cells":[{"cell_type":"markdown","metadata":{},"source":["# NetPyNE Tutorial 8: Grid Parameter Search"]},{"cell_type":"markdown","metadata":{},"source":["## Preliminaries\n","\n","If you are going to run this notebook locally using Jupyter Notebook, start from following instructions https://github.com/suny-downstate-medical-center/netpyne/blob/development/netpyne/tutorials/README.md.\n","\n","If you are using Open Source Brain or EBRAINS, everything is already set up.\n","\n","On any other online platform (e.g. on Google Collab) you might need to run the following commmands to install NEURON and NetPyNE using **pip**:\n","```\n","!pip install neuron\n","!pip install netpyne\n","```\n","\n"]},{"cell_type":"markdown","metadata":{},"source":["Code in this tutorial provides the set up of batch simulation itself, while the network paramters and default configuration of individual simulations are provided in separate files in `batch_grid_tut` folder. In summary, `netParams.py` for fixed (network) parameters, `cfg.py` for variable (simulation) parameters, `init.py` to run a simulation, and `analysis.py` to read simulation results and plot them. We will change to this folder. The results will be saved there as well."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":550,"status":"ok","timestamp":1622453714013,"user":{"displayName":"Samuel Bolland","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjhWpP7mf88kT980MPiR0spUh3By9DWIm5EYVUoaA=s64","userId":"06928499349223853710"},"user_tz":-480},"id":"xAt-SfNS8rQt","outputId":"310bd66b-2a4b-47cc-a038-c8c7d58b1e98"},"outputs":[],"source":["import os\n","os.chdir(os.getcwd() + '/tut_batch_grid')"]},{"cell_type":"markdown","metadata":{},"source":["Lets say we want to explore how the connection weight and the synaptic decay time constant affect the firing rate of the motor population. For this purpose, this parameters in [netParams.py](tut_batch_grid/netParams.py) made variable, i.e. different in each simulation. Instead of having fixed values (e.g. 5.0 and 0.01), they depend on a variable from simConfig: `cfg.synMechTau2` and `cfg.connWeight`.\n","\n","Note that these two variables to the [cfg.py](tut_batch_grid/cfg.py) so that they exist and can be used by netParams and modified in the batch simulation."]},{"cell_type":"markdown","metadata":{},"source":["The first thing we do is create an ordered dictionary `params` – this will be of a special NetPyNE type (`specs.ODict`) but it essentially behaves like an ordered dictionary. Next we add the parameters to explore as keys of this dictionary – `synMechTau2` and `connWeight` – and add the list of parameter values to try in the batch simulation as the dictionary keys – [3.0, 5.0, 7.0] and [0.005, 0.01, 0.15]. **Note that parameter names should coincide with the variables defined in `cfg`**."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from netpyne import specs\n","\n","# Create variable of type ordered dictionary (NetPyNE's customized version)\n","params = specs.ODict()\n","\n","# fill in with parameters to explore and range of values (key has to coincide with a variable in simConfig)\n","params['synMechTau2'] = [3.0, 5.0, 7.0]\n","params['connWeight'] = [0.005, 0.01, 0.15]"]},{"cell_type":"markdown","metadata":{},"source":["We then create an object `b` of the NetPyNE class `Batch` and pass as arguments the parameters to explore, and the files containing the netParams and simConfig modules. Finally, we customize some attributes of the `Batch` object, including the the batch label (`'tauWeight'`), used to create the output file; the folder where to save the data (`'data'`), the method used to explore parameters (`'grid'`), meaning all combinations of the parameter values; and the run configuration indicating we want to use `'mpi'` (this uses MPI and NEURON’s Bulletin Board; other options are available for supercomputers), the `'init.py'` to run each sims, and to `'skip'` runs if the output files already exist.\n","\n","At the end we just need to add the command to launch the batch simulation: `b.run()`."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":21953,"status":"ok","timestamp":1622453741538,"user":{"displayName":"Samuel Bolland","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjhWpP7mf88kT980MPiR0spUh3By9DWIm5EYVUoaA=s64","userId":"06928499349223853710"},"user_tz":-480},"id":"f0P--qg5YUT6","outputId":"2b853a24-a62f-4826-b261-386654379f50"},"outputs":[],"source":["from netpyne.batch import Batch\n","\n","# create Batch object with parameters to modify, and specifying files to use\n","b = Batch(params=params, cfgFile='cfg.py', netParamsFile='netParams.py',)\n","\n","# Set output folder, grid method (all param combinations), and run configuration\n","b.batchLabel = 'tauWeight'\n","b.saveFolder = 'data'\n","b.method = 'grid' \n","b.runCfg = {'type': 'mpi_bulletin',\n","            'script': 'init.py',\n","            'skip': True}\n","\n","# Run batch simulations\n","b.run()"]},{"cell_type":"markdown","metadata":{},"source":["To analyze the output data we use utility functions defined in [analysis.py](tut_batch_grid/analysis.py). These functions read and plot a matrix showing the results from the batch simulations. It requires the Pandas and Seaborn packages, so we will first install them."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["!pip install pandas\n","!pip install seaborn\n","\n","from analysis import readBatchData, plot2DRate\n","dataFolder = 'data/'\n","batchLabel = 'tauWeight'\n","\n","params, data = readBatchData(dataFolder, batchLabel, loadAll=0, saveAll=1, vars=None, maxCombs=None)\n","%matplotlib inline\n","plot2DRate(dataFolder, batchLabel, params, data, 'synMechTau2', 'connWeight', 'M', \"'M' pop rate (Hz)\")\n"]},{"cell_type":"markdown","metadata":{},"source":["Running the above cell should produce a color plot showing the relation between the two parameters explored and the firing rate of the M populations. Notice how the rate initially increases as a function of connection weight, but then decreases due to depolarization blockade; and how the effect of the synaptic time decay constant (synMechTau2) depends on whether the cell is spiking normally or in blockade. Batch simulations and analyses facilitate exploration and understanding of these complex interactions."]}],"metadata":{"colab":{"collapsed_sections":[],"name":"netpyne_batch_tut8.ipynb","provenance":[{"file_id":"1P9Y-rLqpKTP_cJZmWQY8qYsUMfNnju4N","timestamp":1621556006673},{"file_id":"1xcqB5I_iBlz3TNopuNERCJ1StlvZZJw5","timestamp":1621531137101},{"file_id":"19y6MLKhDAdBxLUZm2sHOuQx-5bqSODs-","timestamp":1621524871397}]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.16"}},"nbformat":4,"nbformat_minor":0}
diff --git a/netpyne/tutorials/tut09_batch_evolutionary.ipynb b/netpyne/tutorials/tut09_batch_evolutionary.ipynb
new file mode 100644
index 000000000..11e4e4578
--- /dev/null
+++ b/netpyne/tutorials/tut09_batch_evolutionary.ipynb
@@ -0,0 +1 @@
+{"cells":[{"cell_type":"markdown","metadata":{},"source":["# NetPyNE Tutorial 9: Evolutionary Algorithm for Parameters Optimization"]},{"cell_type":"markdown","metadata":{},"source":["## Preliminaries\n","\n","If you are going to run this notebook locally using Jupyter Notebook, start from following instructions https://github.com/suny-downstate-medical-center/netpyne/blob/development/netpyne/tutorials/README.md.\n","\n","If you are using Open Source Brain or EBRAINS, everything is already set up.\n","\n","On any other online platform (e.g. on Google Collab) you might need to run the following commmands to install NEURON and NetPyNE using **pip**:\n","```\n","!pip install neuron\n","!pip install netpyne\n","```\n","\n"]},{"cell_type":"markdown","metadata":{},"source":["Code in this tutorial provides the set up of batch simulation itself, while the network paramters and default configuration of individual simulations are provided in separate files in `tut_batch_evol` folder. We will switch to this folder to make these files accessible from this notebook. We also need to install `inspyred`, which implements the core of evolutionary algorithm."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":99,"status":"ok","timestamp":1621615838144,"user":{"displayName":"Rammohan Shukla","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgfcDr2KHdiGwdZp-DCPyR3RvG0VfxRFktQ-0JlgQ=s64","userId":"13669639583442617912"},"user_tz":240},"id":"xAt-SfNS8rQt","outputId":"8f15b253-0cdf-4a4d-add1-1e5666938673"},"outputs":[],"source":["!pip install inspyred\n","import os\n","os.chdir(os.getcwd()+'/tut_batch_evol')"]},{"cell_type":"markdown","metadata":{},"source":["\n","\n","Two examples are provided: 'simple' and 'complex'.\n","In 'simple', 3 parameters are optimized to match target firing rates in 2 populations.\n","In 'complex', 6 parameters are optimized to match target firing rates in 6 populations.\n","\n","First we import all necessary modules, and then describe parameters space to explore."]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# %matplotlib inline\n","from netpyne import specs\n","from netpyne.batch import Batch\n","\n","networkType = 'simple' # 'simple' or 'complex'\n","\n","if networkType == 'simple':\n","    ## simple net\n","    params = specs.ODict()\n","    params['prob'] = [0.01, 0.5]\n","    params['weight'] = [0.001, 0.1]\n","    params['delay'] = [1, 20]\n","\n","    pops = {}\n","    pops['S'] = {'target': 5, 'width': 2, 'min': 2}\n","    pops['M'] = {'target': 15, 'width': 2, 'min': 0.2}\n","\n","elif networkType == 'complex':\n","    # complex net\n","    params = specs.ODict()\n","    params['probEall'] = [0.05, 0.2] # 0.1\n","    params['weightEall'] = [0.0025, 0.0075] #5.0\n","    params['probIE'] = [0.2, 0.6] #0.4\n","    params['weightIE'] = [0.0005, 0.002]\n","    params['probLengthConst'] = [100,200]\n","    params['stimWeight'] = [0.05, 0.2]\n","\n","    pops = {}\n","    pops['E2'] = {'target': 5, 'width': 2, 'min': 1}\n","    pops['I2'] = {'target': 10, 'width': 5, 'min': 2}\n","    pops['E4'] = {'target': 30, 'width': 10, 'min': 1}\n","    pops['I4'] = {'target': 10, 'width': 3, 'min': 2}\n","    pops['E5'] = {'target': 40, 'width': 4, 'min': 1}\n","    pops['I5'] = {'target': 25, 'width': 5, 'min': 2}"]},{"cell_type":"markdown","metadata":{},"source":["Provide fitness function and it's arguments:"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["fitnessFuncArgs = {}\n","fitnessFuncArgs['pops'] = pops\n","fitnessFuncArgs['maxFitness'] = 1000\n","\n","def fitnessFunc(simData, **kwargs):\n","    import numpy as np\n","    pops = kwargs['pops']\n","    maxFitness = kwargs['maxFitness']\n","    popFitness = [None for i in pops.items()]\n","    popFitness = [min(np.exp(  abs(v['target'] - simData['popRates'][k])  /  v['width']), maxFitness)\n","            if simData[\"popRates\"][k]>v['min'] else maxFitness for k,v in pops.items()]\n","    fitness = np.mean(popFitness)\n","    popInfo = '; '.join(['%s rate=%.1f fit=%1.f'%(p,r,f) for p,r,f in zip(list(simData['popRates'].keys()), list(simData['popRates'].values()), popFitness)])\n","    print('  '+popInfo)\n","    return fitness"]},{"cell_type":"markdown","metadata":{},"source":["Here we create `Batch` object with paramaters to tune, and set output folder, optimization method ('evol'), fitness function and all the configurations from above."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":131343,"status":"error","timestamp":1621615972335,"user":{"displayName":"Rammohan Shukla","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgfcDr2KHdiGwdZp-DCPyR3RvG0VfxRFktQ-0JlgQ=s64","userId":"13669639583442617912"},"user_tz":240},"id":"f0P--qg5YUT6","outputId":"e63a68dd-05ae-4c36-f46a-eaf7b6550e02"},"outputs":[],"source":["batch = Batch(params=params)\n","\n","# Set output folder, grid method (all param combinations), and run configuration\n","batch.batchLabel = 'simple'\n","batch.saveFolder = './' + batch.batchLabel\n","batch.method = 'evol'\n","batch.runCfg = {\n","\t'type': 'mpi_bulletin',#'hpc_slurm',\n","\t'script': 'init.py',\n","\t# options required only for hpc\n","\t'mpiCommand': 'mpirun',\n","\t'nodes': 1,\n","\t'coresPerNode': 2,\n","\t'allocation': 'default',\n","\t'email': 'salvadordura@gmail.com',\n","\t'reservation': None,\n","\t'folder': '/home/salvadord/evol'\n","\t#'custom': 'export LD_LIBRARY_PATH=\"$HOME/.openmpi/lib\"' # only for conda users\n","}\n","batch.evolCfg = {\n","\t'evolAlgorithm': 'custom',\n","\t'fitnessFunc': fitnessFunc, # fitness expression (should read simData)\n","\t'fitnessFuncArgs': fitnessFuncArgs,\n","\t'pop_size': 6,\n","\t'num_elites': 1, # keep this number of parents for next generation if they are fitter than children\n","\t'mutation_rate': 0.4,\n","\t'crossover': 0.5,\n","\t'maximize': False, # maximize fitness function?\n","\t'max_generations': 4,\n","\t'time_sleep': 5, # wait this time before checking again if sim is completed (for each generation)\n","\t'maxiter_wait': 40, # max number of times to check if sim is completed (for each generation)\n","\t'defaultFitness': 1000 # set fitness value in case simulation time is over\n","}"]},{"cell_type":"markdown","metadata":{},"source":["Now we can run this code. When completed, you can inspect folder `tut_batch_evol/{networkType}`, where `{networkType}` is either `simple` or `complex`, to find the output of each generation and the summary for all, including the optimal values of requested parameters (short summary will also appear in the output of the cell). \n","\n","Note that the algorithm is stochastic, so you will not get the same results if re-run it again."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aij9ZSq0UnLd"},"outputs":[],"source":["batch.run()"]}],"metadata":{"colab":{"collapsed_sections":[],"name":"netpyne_batch_evol.ipynb","provenance":[{"file_id":"1Vywfic5grokY-kOE4nQLtCFiw9diDGgR","timestamp":1621558581181},{"file_id":"1P9Y-rLqpKTP_cJZmWQY8qYsUMfNnju4N","timestamp":1621556006673},{"file_id":"1xcqB5I_iBlz3TNopuNERCJ1StlvZZJw5","timestamp":1621531137101},{"file_id":"19y6MLKhDAdBxLUZm2sHOuQx-5bqSODs-","timestamp":1621524871397}]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.16"}},"nbformat":4,"nbformat_minor":0}
diff --git a/netpyne/tutorials/tut_batch_evol/batchRun.py b/netpyne/tutorials/tut_batch_evol/batchRun.py
new file mode 100644
index 000000000..f50534e91
--- /dev/null
+++ b/netpyne/tutorials/tut_batch_evol/batchRun.py
@@ -0,0 +1,100 @@
+from netpyne import specs
+from netpyne.batch import Batch
+
+''' Example of evolutionary algorithm optimization of a network using NetPyNE
+2 examples are provided: 'simple' and 'complex'
+In 'simple', 3 parameters are optimized to match target firing rates in 2 populations
+In 'complex', 6 parameters are optimized to match target firing rates in 6 populations
+
+To run use: mpiexec -np [num_cores] nrniv -mpi batchRun.py
+'''
+
+def batchEvol(networkType):
+	# parameters space to explore
+
+	if networkType == 'simple':
+		## simple net
+		params = specs.ODict()
+		params['prob'] = [0.01, 0.5]
+		params['weight'] = [0.001, 0.1]
+		params['delay'] = [1, 20]
+
+		pops = {}
+		pops['S'] = {'target': 5, 'width': 2, 'min': 2}
+		pops['M'] = {'target': 15, 'width': 2, 'min': 0.2}
+
+	elif networkType == 'complex':
+		# complex net
+		params = specs.ODict()
+		params['probEall'] = [0.05, 0.2] # 0.1
+		params['weightEall'] = [0.0025, 0.0075] #5.0
+		params['probIE'] = [0.2, 0.6] #0.4
+		params['weightIE'] = [0.0005, 0.002]
+		params['probLengthConst'] = [100,200]
+		params['stimWeight'] = [0.05, 0.2]
+
+		pops = {}
+		pops['E2'] = {'target': 5, 'width': 2, 'min': 1}
+		pops['I2'] = {'target': 10, 'width': 5, 'min': 2}
+		pops['E4'] = {'target': 30, 'width': 10, 'min': 1}
+		pops['I4'] = {'target': 10, 'width': 3, 'min': 2}
+		pops['E5'] = {'target': 40, 'width': 4, 'min': 1}
+		pops['I5'] = {'target': 25, 'width': 5, 'min': 2}
+
+	# fitness function
+	fitnessFuncArgs = {}
+	fitnessFuncArgs['pops'] = pops
+	fitnessFuncArgs['maxFitness'] = 1000
+
+	def fitnessFunc(simData, **kwargs):
+		import numpy as np
+		pops = kwargs['pops']
+		maxFitness = kwargs['maxFitness']
+		popFitness = [None for i in pops.items()]
+		popFitness = [min(np.exp(  abs(v['target'] - simData['popRates'][k])  /  v['width']), maxFitness)
+				if simData["popRates"][k]>v['min'] else maxFitness for k,v in pops.items()]
+		fitness = np.mean(popFitness)
+		popInfo = '; '.join(['%s rate=%.1f fit=%1.f'%(p,r,f) for p,r,f in zip(list(simData['popRates'].keys()), list(simData['popRates'].values()), popFitness)])
+		print('  '+popInfo)
+		return fitness
+
+	# create Batch object with paramaters to modify, and specifying files to use
+	b = Batch(params=params)
+
+	# Set output folder, grid method (all param combinations), and run configuration
+	b.batchLabel = 'simple'
+	b.saveFolder = './'+b.batchLabel
+	b.method = 'evol'
+	b.runCfg = {
+		'type': 'mpi_bulletin',#'hpc_slurm',
+		'script': 'init.py',
+		# options required only for hpc
+		'mpiCommand': 'mpirun',
+		'nodes': 1,
+		'coresPerNode': 2,
+		'allocation': 'default',
+		'email': 'salvadordura@gmail.com',
+		'reservation': None,
+		'folder': '/home/salvadord/evol'
+		#'custom': 'export LD_LIBRARY_PATH="$HOME/.openmpi/lib"' # only for conda users
+	}
+	b.evolCfg = {
+		'evolAlgorithm': 'custom',
+		'fitnessFunc': fitnessFunc, # fitness expression (should read simData)
+		'fitnessFuncArgs': fitnessFuncArgs,
+		'pop_size': 6,
+		'num_elites': 1, # keep this number of parents for next generation if they are fitter than children
+		'mutation_rate': 0.4,
+		'crossover': 0.5,
+		'maximize': False, # maximize fitness function?
+		'max_generations': 4,
+		'time_sleep': 5, # wait this time before checking again if sim is completed (for each generation)
+		'maxiter_wait': 40, # max number of times to check if sim is completed (for each generation)
+		'defaultFitness': 1000 # set fitness value in case simulation time is over
+	}
+	# Run batch simulations
+	b.run()
+
+# Main code
+if __name__ == '__main__':
+	batchEvol('simple')  # 'simple' or 'complex'
diff --git a/netpyne/tutorials/tut_batch_evol/cfg.py b/netpyne/tutorials/tut_batch_evol/cfg.py
new file mode 100644
index 000000000..21c20fced
--- /dev/null
+++ b/netpyne/tutorials/tut_batch_evol/cfg.py
@@ -0,0 +1,49 @@
+from netpyne import specs
+
+cfg = specs.SimConfig()
+
+cfg.networkType = 'simple' # 'complex'
+
+# --------------------------------------------------------
+# Simple network
+# --------------------------------------------------------
+if cfg.networkType == 'simple':
+	# Simulation options
+	cfg.dt = 0.025
+	cfg.duration = 2*1e3
+
+	cfg.verbose = False
+	cfg.saveJson = True
+	cfg.filename = 'simple_net'
+	cfg.saveDataInclude = ['simData']
+	cfg.recordStep = 0.1
+	cfg.printPopAvgRates = [500, cfg.duration]
+
+	# cfg.recordCells = [1]
+	# cfg.recordTraces = {'V_soma':{'sec':'soma','loc':0.5,'var':'v'}}
+
+	# Variable parameters (used in netParams)
+	cfg.prob = 0.2
+	cfg.weight = 0.025
+	cfg.delay = 2
+
+# --------------------------------------------------------
+# Complex network
+# --------------------------------------------------------
+elif cfg.networkType == 'complex':
+	cfg.duration = 1*1e3           # Duration of the simulation, in ms
+	cfg.dt = 0.1                # Internal integration timestep to use
+	cfg.verbose = False            # Show detailed messages
+	cfg.recordStep = 1             # Step size in ms to save data (eg. V traces, LFP, etc)
+	cfg.filename = 'simple_net'   # Set file output name
+	cfg.saveDataInclude = ['simData']
+	cfg.saveJson = True
+	cfg.printPopAvgRates = [100, cfg.duration]
+
+	# Variable parameters (used in netParams)
+	cfg.probEall = 0.1
+	cfg.weightEall = 0.005
+	cfg.probIE = 0.4
+	cfg.weightIE = 0.001
+	cfg.probLengthConst = 150
+	cfg.stimWeight = 0.1
diff --git a/netpyne/tutorials/tut_batch_evol/init.py b/netpyne/tutorials/tut_batch_evol/init.py
new file mode 100644
index 000000000..11066fe3d
--- /dev/null
+++ b/netpyne/tutorials/tut_batch_evol/init.py
@@ -0,0 +1,8 @@
+import matplotlib; matplotlib.use('Agg')
+from netpyne import sim
+
+# read cfg and netParams from command line arguments if available; otherwise use default
+simConfig, netParams = sim.readCmdLineArgs(simConfigDefault='simConfig.py', netParamsDefault='netParams.py')
+
+# Create network and run simulation
+sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig)
diff --git a/netpyne/tutorials/tut_batch_evol/netParams.py b/netpyne/tutorials/tut_batch_evol/netParams.py
new file mode 100644
index 000000000..1eede2e59
--- /dev/null
+++ b/netpyne/tutorials/tut_batch_evol/netParams.py
@@ -0,0 +1,102 @@
+from netpyne import specs, sim
+
+try:
+	from __main__ import cfg
+except:
+	from simConfig import cfg
+
+# Network parameters
+netParams = specs.NetParams()
+
+# --------------------------------------------------------
+# Simple network
+# --------------------------------------------------------
+if cfg.networkType == 'simple':
+
+	# Population parameters
+	netParams.popParams['S'] = {'cellType': 'PYR', 'numCells': 20, 'cellModel': 'HH'}
+	netParams.popParams['M'] = {'cellType': 'PYR', 'numCells': 20, 'cellModel': 'HH'}
+
+	# Cell property rules
+	cellRule = {'conds': {'cellType': 'PYR'},  'secs': {}}
+	cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}}
+	cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0}
+	cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70}
+	netParams.cellParams['PYRrule'] = cellRule
+
+	# Synaptic mechanism parameters
+	netParams.synMechParams['exc'] = {'mod': 'Exp2Syn', 'tau1': 0.1, 'tau2': 5, 'e': 0}
+
+	# Stimulation parameters
+	netParams.stimSourceParams['bkg'] = {'type': 'NetStim', 'rate': 10, 'noise': 0.5}
+	netParams.stimTargetParams['bkg->PYR'] = {'source': 'bkg', 'conds': {'cellType': 'PYR'}, 'weight': 0.01, 'delay': 5, 'synMech': 'exc'}
+
+	# Cell connectivity rules
+	netParams.connParams['S->M'] = {
+		'preConds': {'pop': 'S'},
+		'postConds': {'pop': 'M'},
+		'probability': cfg.prob,
+		'weight': cfg.weight,
+		'delay': cfg.delay,
+		'synMech': 'exc'
+	}
+
+# --------------------------------------------------------
+# Complex network
+# --------------------------------------------------------
+elif cfg.networkType == 'complex':
+
+	netParams.sizeX = 100 # x-dimension (horizontal length) size in um
+	netParams.sizeY = 1000 # y-dimension (vertical height or cortical depth) size in um
+	netParams.sizeZ = 100 # z-dimension (horizontal length) size in um
+	netParams.propVelocity = 100.0 # propagation velocity (um/ms)
+	netParams.probLengthConst = cfg.probLengthConst # length constant for conn probability (um)
+
+
+	## Population parameters
+	netParams.popParams['E2'] = {'cellType': 'E', 'numCells': 50, 'yRange': [100,300], 'cellModel': 'HH'}
+	netParams.popParams['I2'] = {'cellType': 'I', 'numCells': 50, 'yRange': [100,300], 'cellModel': 'HH'}
+	netParams.popParams['E4'] = {'cellType': 'E', 'numCells': 50, 'yRange': [300,600], 'cellModel': 'HH'}
+	netParams.popParams['I4'] = {'cellType': 'I', 'numCells': 50, 'yRange': [300,600], 'cellModel': 'HH'}
+	netParams.popParams['E5'] = {'cellType': 'E', 'numCells': 50, 'ynormRange': [0.6,1.0], 'cellModel': 'HH'}
+	netParams.popParams['I5'] = {'cellType': 'I', 'numCells': 50, 'ynormRange': [0.6,1.0], 'cellModel': 'HH'}
+
+
+	## Cell property rules
+	cellRule = {'conds': {'cellType': 'E'},  'secs': {}}  # cell rule dict
+	cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}}                              # soma params dict
+	cellRule['secs']['soma']['geom'] = {'diam': 15, 'L': 14, 'Ra': 120.0}                   # soma geometry
+	cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.13, 'gkbar': 0.036, 'gl': 0.003, 'el': -70}      # soma hh mechanism
+	netParams.cellParams['Erule'] = cellRule                          # add dict to list of cell params
+
+	cellRule = {'conds': {'cellType': 'I'},  'secs': {}}  # cell rule dict
+	cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}}                              # soma params dict
+	cellRule['secs']['soma']['geom'] = {'diam': 10.0, 'L': 9.0, 'Ra': 110.0}                  # soma geometry
+	cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.11, 'gkbar': 0.036, 'gl': 0.003, 'el': -70}      # soma hh mechanism
+	netParams.cellParams['Irule'] = cellRule                          # add dict to list of cell params
+
+
+	## Synaptic mechanism parameters
+	netParams.synMechParams['exc'] = {'mod': 'Exp2Syn', 'tau1': 0.8, 'tau2': 5.3, 'e': 0}  # NMDA synaptic mechanism
+	netParams.synMechParams['inh'] = {'mod': 'Exp2Syn', 'tau1': 0.6, 'tau2': 8.5, 'e': -75}  # GABA synaptic mechanism
+
+
+	# Stimulation parameters
+	netParams.stimSourceParams['bkg'] = {'type': 'NetStim', 'rate': 20, 'noise': 0.3}
+	netParams.stimTargetParams['bkg->all'] = {'source': 'bkg', 'conds': {'cellType': ['E','I']}, 'weight': cfg.stimWeight, 'delay': 'max(1, normal(5,2))', 'synMech': 'exc'}
+
+
+	## Cell connectivity rules
+	netParams.connParams['E->all'] = {
+	  'preConds': {'cellType': 'E'}, 'postConds': {'y': [100,1000]},  #  E -> all (100-1000 um)
+	  'probability': cfg.probEall,                  # probability of connection
+	  'weight': str(cfg.weightEall)+'*post_ynorm',         # synaptic weight
+	  'delay': 'dist_3D/propVelocity',      # transmission delay (ms)
+	  'synMech': 'exc'}                     # synaptic mechanism
+
+	netParams.connParams['I->E'] = {
+	  'preConds': {'cellType': 'I'}, 'postConds': {'pop': ['E2','E4','E5']},       #  I -> E
+	  'probability': str(cfg.probIE)+'*exp(-dist_3D/probLengthConst)',   # probability of connection
+	  'weight': cfg.weightIE,                                      # synaptic weight
+	  'delay': 'dist_3D/propVelocity',                      # transmission delay (ms)
+	  'synMech': 'inh'}                                     # synaptic mechanism
diff --git a/netpyne/tutorials/tut_batch_grid/analysis.py b/netpyne/tutorials/tut_batch_grid/analysis.py
new file mode 100644
index 000000000..6f8eca8e9
--- /dev/null
+++ b/netpyne/tutorials/tut_batch_grid/analysis.py
@@ -0,0 +1,208 @@
+"""
+analysis.py
+
+Functions to read and plot figures from the batch simulation results.
+"""
+
+import json
+import pandas as pd
+import seaborn as sb
+import matplotlib.pyplot as plt
+import pickle
+import numpy as np
+from pylab import *
+from itertools import product
+from pprint import pprint
+from netpyne import specs
+from collections import OrderedDict
+
+
+
+#--------------------------------------------------------------------
+# Function to read batch data
+#--------------------------------------------------------------------
+def readBatchData(dataFolder, batchLabel, loadAll=False, saveAll=True, vars=None, maxCombs=None, listCombs=None):
+    # load from previously saved file with all data
+    if loadAll:
+        print('\nLoading single file with all data...')
+        filename = '%s/%s/%s_allData.json' % (dataFolder, batchLabel, batchLabel)
+        with open(filename, 'r') as fileObj:
+            dataLoad = json.load(fileObj, object_pairs_hook=OrderedDict)
+        params = dataLoad['params']
+        data = dataLoad['data']
+        return params, data
+
+    if isinstance(listCombs, str):
+        filename = str(listCombs)
+        with open(filename, 'r') as fileObj:
+            dataLoad = json.load(fileObj)
+        listCombs = dataLoad['paramsMatch']
+
+    # read the batch file and cfg
+    batchFile = '%s/%s_batch.json' % (dataFolder, batchLabel)
+    with open(batchFile, 'r') as fileObj:
+        b = json.load(fileObj)['batch']
+
+    # read params labels and ranges
+    params = b['params']
+
+    # reorder so grouped params come first
+    preorder = [p for p in params if 'group' in p and p['group']]
+    for p in params:
+        if p not in preorder: preorder.append(p)
+    params = preorder
+
+    # read vars from all files - store in dict
+    if b['method'] == 'grid':
+        labelList, valuesList = list(zip(*[(p['label'], p['values']) for p in params]))
+        valueCombinations = product(*(valuesList))
+        indexCombinations = product(*[list(range(len(x))) for x in valuesList])
+        data = {}
+        print('Reading data...')
+        missing = 0
+        for i,(iComb, pComb) in enumerate(zip(indexCombinations, valueCombinations)):
+            if (not maxCombs or i<= maxCombs) and (not listCombs or list(pComb) in listCombs):
+                print(i, iComb)
+                # read output file
+                iCombStr = ''.join([''.join('_'+str(i)) for i in iComb])
+                simLabel = b['batchLabel']+iCombStr
+                outFile = b['saveFolder']+'/'+simLabel+'_data.json'
+                try:
+                    with open(outFile, 'r') as fileObj:
+                        output = json.load(fileObj, object_pairs_hook=OrderedDict)
+                    # save output file in data dict
+                    data[iCombStr] = {}
+                    data[iCombStr]['paramValues'] = pComb  # store param values
+                    if not vars: vars = list(output.keys())
+
+                    for key in vars:
+                        if isinstance(key, tuple):
+                            container = output
+                            for ikey in range(len(key)-1):
+                                container = container[key[ikey]]
+                            data[iCombStr][key[1]] = container[key[-1]]
+
+                        elif isinstance(key, str):
+                            data[iCombStr][key] = output[key]
+
+                except:
+                    print('... file missing')
+                    missing = missing + 1
+                    output = {}
+            else:
+                missing = missing + 1
+
+        print('%d files missing' % (missing))
+
+        # save
+        if saveAll:
+            print('Saving to single file with all data')
+            filename = '%s/%s_allData.json' % (dataFolder, batchLabel)
+            dataSave = {'params': params, 'data': data}
+            with open(filename, 'w') as fileObj:
+                json.dump(dataSave, fileObj)
+
+        return params, data
+
+#--------------------------------------------------------------------
+# Function to convert data to Pandas
+#--------------------------------------------------------------------
+def toPandas(params, data):
+    if 'simData' in data[list(data.keys())[0]]:
+        rows = [list(d['paramValues'])+[s for s in list(d['simData'].values())] for d in list(data.values())]
+        cols = [str(d['label']) for d in params]+[s for s in list(data[list(data.keys())[0]]['simData'].keys())]
+    else:
+        rows = [list(d['paramValues'])+[s for s in list(d.values())] for d in list(data.values())]
+        cols = [str(d['label']) for d in params]+[s for s in list(data[list(data.keys())[0]].keys())]
+
+    df = pd.DataFrame(rows, columns=cols)
+    df['simLabel'] = list(data.keys())
+
+    colRename=[]
+    for col in list(df.columns):
+        if col.startswith("[u'"):
+            colName = col.replace(", u'","_'").replace("[u","").replace("'","").replace("]","").replace(", ","_")
+            colRename.append(colName)
+        else:
+            colRename.append(col)
+    #print(colRename)
+    df.columns = colRename
+
+    return df
+
+#--------------------------------------------------------------------
+# Function to colors and style of figures
+#--------------------------------------------------------------------
+def setPlotFormat(numColors=8):
+    plt.style.use('seaborn-whitegrid')
+
+    plt.rcParams['font.size'] = 12
+    plt.rcParams['axes.titlesize'] = 14
+    plt.rcParams['axes.labelsize'] = 12
+    plt.rcParams['legend.fontsize'] = 'large'
+
+    NUM_COLORS = numColors
+    colormap = plt.get_cmap('nipy_spectral')
+    colorlist = [colormap(1.*i/NUM_COLORS) for i in range(NUM_COLORS)]
+
+    plt.rc('axes', prop_cycle=(cycler('color', colorlist)))
+
+
+#--------------------------------------------------------------------
+# Function to plot relation between parameters (tau2 and weight) and firing rate
+#--------------------------------------------------------------------
+def plot2DRate(dataFolder, batchLabel, params, data, par1, par2, val, valLabel, graphType='matrix', saveFile=None):
+    df = toPandas(params, data)
+    # dfpop = dfPopRates(df1, 7)
+
+    dfpop = df.iloc[:,0:5] # get param columns of all rows
+    # dfpop['simLabel'] = df['simLabel']
+    for k in list(df.popRates[0].keys()): dfpop[k] = [r[k] for r in df.popRates]
+    #return dfpop
+
+    #print(dfpop)
+    # if not valLabel: valLabel = val
+    dfsubset = dfpop[[par1,par2,val]]
+        # dfgroup = dfsubset.groupby(by=[par1,par2])
+        # if groupStat=='first':
+        #     dfgroup2 = dfgroup.first()
+        # elif groupStat=='last':
+        #     dfgroup2 = dfgroup.last()
+        # elif groupStat=='mean':
+        #     dfgroup2 = dfgroup.mean()
+        # elif groupStat=='sum':
+        #     dfgroup2 = dfgroup.sum()
+        # dffinal = pd.DataFrame(dfgroup2).reset_index()
+
+    dfpiv = pd.pivot_table(dfsubset, index=par1, columns=par2, values=val)
+#    pandas.pivot_table(df,values='count',index='site_id',columns='week')
+    if graphType=='matrix':
+        sb.heatmap(dfpiv, square=True, cbar_kws={'label': valLabel})
+    elif graphType=='line':
+        setPlotFormat(numColors = len(dfpiv.columns))
+        #dfpiv = dfpiv[['IT2','IT4','IT5A','IT5B','PT5B','IT6','CT6']]
+        dfpiv.plot(marker='o')
+    try:
+        if saveFile:
+            plt.savefig(saveFile)
+        else:
+            plt.savefig(dataFolder+'/'+batchLabel+'_matrix_'+par1+'_'+par2+'_'+val+'.png')
+    except:
+        print('Error saving figure...')
+
+    plt.show()
+
+#--------------------------------------------------------------------
+# Function to read batch data and plot figure
+#--------------------------------------------------------------------
+def readPlot():
+    dataFolder = 'tauWeight_data/'
+    batchLabel = 'tauWeight'
+
+    params, data = readBatchData(dataFolder, batchLabel, loadAll=0, saveAll=1, vars=None, maxCombs=None)
+    plot2DRate(dataFolder, batchLabel, params, data, 'synMechTau2', 'connWeight', 'M', "'M' pop rate (Hz)")
+
+
+# Main code
+if __name__ == '__main__':
+    readPlot()
diff --git a/netpyne/tutorials/tut_batch_grid/cfg.py b/netpyne/tutorials/tut_batch_grid/cfg.py
new file mode 100644
index 000000000..10ce94aaa
--- /dev/null
+++ b/netpyne/tutorials/tut_batch_grid/cfg.py
@@ -0,0 +1,21 @@
+from netpyne import specs
+
+# Simulation options
+cfg = specs.SimConfig()     # object of class SimConfig to store simulation configuration
+
+cfg.duration = 1*1e3        # Duration of the simulation, in ms
+cfg.dt = 0.025              # Internal integration timestep to use
+cfg.verbose = False         # Show detailed messages
+cfg.recordTraces = {'V_soma':{'sec':'soma','loc':0.5,'var':'v'}}  # Dict with traces to record
+cfg.recordStep = 0.1        # Step size in ms to save data (eg. V traces, LFP, etc)
+cfg.filename = 'tut8'       # Set file output name
+cfg.saveJson = True
+cfg.printPopAvgRates = True
+cfg.analysis['plotRaster'] = {'saveFig': True}                   # Plot a raster
+cfg.analysis['plotTraces'] = {'include': [0], 'saveFig': True}  # Plot recorded traces for this list of cells
+
+cfg.saveDataInclude = ['simData', 'simConfig', 'netParams', 'net']
+
+# Variable parameters (used in netParams)
+cfg.synMechTau2 = 5
+cfg.connWeight = 0.01
diff --git a/netpyne/tutorials/tut_batch_grid/init.py b/netpyne/tutorials/tut_batch_grid/init.py
new file mode 100644
index 000000000..059ce44be
--- /dev/null
+++ b/netpyne/tutorials/tut_batch_grid/init.py
@@ -0,0 +1,7 @@
+from netpyne import sim
+
+# read cfg and netParams from command line arguments if available; otherwise use default
+simConfig, netParams = sim.readCmdLineArgs(simConfigDefault='tut8_cfg.py', netParamsDefault='tut8_netParams.py')
+
+# Create network and run simulation
+sim.createSimulateAnalyze(netParams=netParams, simConfig=simConfig)
diff --git a/netpyne/tutorials/tut_batch_grid/netParams.py b/netpyne/tutorials/tut_batch_grid/netParams.py
new file mode 100644
index 000000000..d62822498
--- /dev/null
+++ b/netpyne/tutorials/tut_batch_grid/netParams.py
@@ -0,0 +1,36 @@
+from netpyne import specs, sim
+
+try:
+    from __main__ import cfg  # import SimConfig object with params from parent module
+except:
+    from tut8_cfg import cfg  # if no simConfig in parent module, import directly from tut8_cfg module
+
+# Network parameters
+netParams = specs.NetParams()  # object of class NetParams to store the network parameters
+
+## Cell parameters/rules
+PYRcell = {'secs': {}}
+PYRcell['secs']['soma'] = {'geom': {}, 'mechs': {}}  # soma params dict
+PYRcell['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0}  # soma geometry
+PYRcell['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70}  # soma hh mechanism
+netParams.cellParams['PYR'] = PYRcell
+
+## Population parameters
+netParams.popParams['S'] = {'cellType': 'PYR', 'numCells': 20}
+netParams.popParams['M'] = {'cellType': 'PYR', 'numCells': 20}
+
+## Synaptic mechanism parameters
+netParams.synMechParams['exc'] = {'mod': 'Exp2Syn', 'tau1': 0.1, 'tau2': cfg.synMechTau2, 'e': 0}  # excitatory synaptic mechanism
+
+# Stimulation parameters
+netParams.stimSourceParams['bkg'] = {'type': 'NetStim', 'rate': 10, 'noise': 0.5}
+netParams.stimTargetParams['bkg->PYR'] = {'source': 'bkg', 'conds': {'cellType': 'PYR'}, 'weight': 0.01, 'delay': 5, 'synMech': 'exc'}
+
+## Cell connectivity rules
+netParams.connParams['S->M'] = {    #  S -> M label
+    'preConds': {'pop': 'S'},       # conditions of presyn cells
+    'postConds': {'pop': 'M'},      # conditions of postsyn cells
+    'probability': 0.5,             # probability of connection
+    'weight': cfg.connWeight,       # synaptic weight
+    'delay': 5,                     # transmission delay (ms)
+    'synMech': 'exc'}               # synaptic mechanism
diff --git a/netpyne/tutorials/saving_loading_tut/saving_dist_cfg.py b/netpyne/tutorials/tut_saving_loading/saving_dist_cfg.py
similarity index 100%
rename from netpyne/tutorials/saving_loading_tut/saving_dist_cfg.py
rename to netpyne/tutorials/tut_saving_loading/saving_dist_cfg.py
diff --git a/netpyne/tutorials/saving_loading_tut/saving_dist_init.py b/netpyne/tutorials/tut_saving_loading/saving_dist_init.py
similarity index 76%
rename from netpyne/tutorials/saving_loading_tut/saving_dist_init.py
rename to netpyne/tutorials/tut_saving_loading/saving_dist_init.py
index 1abab40ef..1e29fd5db 100644
--- a/netpyne/tutorials/saving_loading_tut/saving_dist_init.py
+++ b/netpyne/tutorials/tut_saving_loading/saving_dist_init.py
@@ -1,4 +1,8 @@
+import os
+os.chdir('netpyne/tutorials/saving_loading_tut')
+
 from netpyne import sim
+# sim.gatherDataFromFiles(simLabel='saving_dist', saveFolder='saving_dist_data')
 
 cfg, netParams = sim.readCmdLineArgs(simConfigDefault='saving_dist_cfg.py', netParamsDefault='saving_netParams.py')
 sim.initialize(simConfig=cfg, netParams=netParams)
diff --git a/netpyne/tutorials/saving_loading_tut/saving_int_cfg.py b/netpyne/tutorials/tut_saving_loading/saving_int_cfg.py
similarity index 100%
rename from netpyne/tutorials/saving_loading_tut/saving_int_cfg.py
rename to netpyne/tutorials/tut_saving_loading/saving_int_cfg.py
diff --git a/netpyne/tutorials/saving_loading_tut/saving_int_init.py b/netpyne/tutorials/tut_saving_loading/saving_int_init.py
similarity index 100%
rename from netpyne/tutorials/saving_loading_tut/saving_int_init.py
rename to netpyne/tutorials/tut_saving_loading/saving_int_init.py
diff --git a/netpyne/tutorials/saving_loading_tut/saving_netParams.py b/netpyne/tutorials/tut_saving_loading/saving_netParams.py
similarity index 100%
rename from netpyne/tutorials/saving_loading_tut/saving_netParams.py
rename to netpyne/tutorials/tut_saving_loading/saving_netParams.py
diff --git a/netpyne/tutorials/saving_loading_tut/saving_normal_cfg.py b/netpyne/tutorials/tut_saving_loading/saving_normal_cfg.py
similarity index 91%
rename from netpyne/tutorials/saving_loading_tut/saving_normal_cfg.py
rename to netpyne/tutorials/tut_saving_loading/saving_normal_cfg.py
index 53730ce7b..6c14f5310 100644
--- a/netpyne/tutorials/saving_loading_tut/saving_normal_cfg.py
+++ b/netpyne/tutorials/tut_saving_loading/saving_normal_cfg.py
@@ -6,6 +6,9 @@
 cfg.simLabel = 'saving_normal'
 cfg.saveFolder = cfg.simLabel + '_data'
 cfg.savePickle = True
+cfg.saveDat = True
+cfg.saveJson = True
+cfg.saveMat = True
 cfg.saveDataInclude = ['simData', 'simConfig', 'netParams', 'net']
 
 # Simulation parameters
diff --git a/netpyne/tutorials/saving_loading_tut/saving_normal_init.py b/netpyne/tutorials/tut_saving_loading/saving_normal_init.py
similarity index 100%
rename from netpyne/tutorials/saving_loading_tut/saving_normal_init.py
rename to netpyne/tutorials/tut_saving_loading/saving_normal_init.py
diff --git a/netpyne/tutorials/saving_loading_tut/saving_tut.ipynb b/netpyne/tutorials/tut_saving_loading/saving_tut.ipynb
similarity index 100%
rename from netpyne/tutorials/saving_loading_tut/saving_tut.ipynb
rename to netpyne/tutorials/tut_saving_loading/saving_tut.ipynb
diff --git a/netpyne/tutorials/rxd_movie_tut/rxd_movie_tut.ipynb b/netpyne/tutorials/tut_voltage_and_rxd_movie/rxd_movie_tut.ipynb
similarity index 100%
rename from netpyne/tutorials/rxd_movie_tut/rxd_movie_tut.ipynb
rename to netpyne/tutorials/tut_voltage_and_rxd_movie/rxd_movie_tut.ipynb
diff --git a/netpyne/tutorials/voltage_movie_tut/voltage_movie_tut.ipynb b/netpyne/tutorials/tut_voltage_and_rxd_movie/voltage_movie_tut.ipynb
similarity index 100%
rename from netpyne/tutorials/voltage_movie_tut/voltage_movie_tut.ipynb
rename to netpyne/tutorials/tut_voltage_and_rxd_movie/voltage_movie_tut.ipynb
diff --git a/netpyne/tutorials/voltage_movie_tut/voltage_movie_tut.py b/netpyne/tutorials/tut_voltage_and_rxd_movie/voltage_movie_tut.py
similarity index 100%
rename from netpyne/tutorials/voltage_movie_tut/voltage_movie_tut.py
rename to netpyne/tutorials/tut_voltage_and_rxd_movie/voltage_movie_tut.py