multiple .hoc cells import

Michael

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Oct 9, 2021, 7:57:52 AM10/9/21

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Good Day!

Is it possible to load multiple .hoc cells to the model? running 2 hoc cells separately is ok. However running them together gives error in this code

from netpyne import specs, sim

import os

MyDir = os.getcwd()

# Network parameters

netParams = specs.NetParams() # object of class NetParams to store the network parameters

os.chdir(MyDir)

os.chdir('L4_BP_bAC217_1')

cellRule = netParams.importCellParams(

label='PYR_HH3D_hoc',

conds={'cellType': 'PYR3D', 'cellModel': 'HH3D'},

fileName='createsimulation.hoc',

cellName='bAC217_L4_BP_d04c4872bd',

importSynMechs=True)

os.chdir(MyDir)

os.chdir('L6_TPC_L1_cADpyr231_1')

cellRule2 = netParams.importCellParams(

label='PYR_HH3DL6_hoc',

conds={'cellType': 'PYR3DL6', 'cellModel': 'HH3D'},

fileName='createsimulation.hoc',

cellName='cADpyr231_L6_TPC_L1_68efb03f39',

importSynMechs=True)

netParams.popParams['INPUTosc_CE_ConL4Pyr3D'] = {'cellType': 'PYR3D', 'numCells': 5, 'yRange': [700,800]}

netParams.popParams['INPUTosc_CE_ConL4Pyr3DL6'] = {'cellType': 'PYR3DL6', 'numCells': 5, 'yRange': [500,600]}

netParams.cellParams['PYR_HH3D_hoc']['conds'] = {'cellType': ['PYR3D']}

netParams.cellParams['PYR_HH3DL6_hoc']['conds'] = {'cellType': ['PYR3DL6']}

..........

error log

......

Added inhibitory synapse 38 originating from cell 149000 of m-type L6_LBC on basal section 5(0.732000) and dep 557.000000

Added inhibitory synapse 39 originating from cell 149929 of m-type L6_LBC on basal section 5(0.446000) and dep 370.000000

Added inhibitory synapse 40 originating from cell 149929 of m-type L6_LBC on basal section 5(0.457000) and dep 1076.000000

Traceback (most recent call last):

File "tut2input6.py", line 95, in <module>

importSynMechs=True)

File "/home/ubuntu/.local/lib/python3.6/site-packages/netpyne/specs/netParams.py", line 401, in importCellParams

secs, secLists, synMechs, globs = conversion.importCell(fileName, cellName, cellArgs, cellInstance)

File "/home/ubuntu/.local/lib/python3.6/site-packages/netpyne/conversion/neuronPyHoc.py", line 280, in importCell

cell = getattr(h, cellName)(**cellArgs) # create cell using template, passing dict with args

AttributeError: 'hoc.HocObject' object has no attribute 'cADpyr231_L6_TPC_L1_68efb03f39'

Michael

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Oct 22, 2021, 11:05:48 AM10/22/21

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This is important. I want to build different cortex layer 2-6 circuit. I didn't find any examples or anywhere mentioned that more than 1 .hoc neuron model import is possible

Joe Graham

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Oct 26, 2021, 4:10:35 PM10/26/21

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Hello,

It should be possible to import more than one hoc cell model. But, your project layout looks a little odd with only one fileName for multiple cellNames... If you could share your hoc file here (or better, provide a link to a repo with all your code), then I could be of more help. At this point I'm not sure if the error comes from NEURON or from NetPyNE...

Best wishes,

Joe

Michael

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Oct 27, 2021, 7:31:39 AM10/27/21

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I put everything into this archive.

https://mp1233.github.io/bg/nrml_3d.zip

Michael

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Nov 27, 2021, 7:09:19 AM11/27/21

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Maybe I can try to fix it. Just tell me what exactly should be fixed. Or if problem is with the data maybe there are some other models for spiny and aspiny neurons in cortex layers

Joe Graham

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Nov 30, 2021, 10:14:43 PM11/30/21

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Hello,

We really need a minimal example that demonstrates the problem in order to help out, something like the file I attached. I don't see that you have properly imported any cells... It's really better to start a model very simply and make sure everything is working before you add complexity. I suggest you start with the attached file, and get it working for importing one cell type. The call to `plotShape` should produce a figure of the morphology when it's been properly imported.

There are quite a few problems with your code. For one thing, you can't change directories the way you're doing. NetPyNE doesn't create a cell model when you define the `cellRule`, it creates all cell models when you execute `sim.createSimulateAnalyze`. So you need to include paths to the necessary files instead of changing directories (see attached file). For another thing, you need to point to a file that defines the cell template, not the hoc simulation. In looking through your many files, it seems you should set fileName='template.hoc' instead of fileName='createsimulation.hoc'.

When I execute the attached file, I get the following hoc error:

ipython -i tut2input6_joe.py

Python 3.8.2 (default, Jun 8 2021, 11:59:35)

Type 'copyright', 'credits' or 'license' for more information

IPython 7.30.0 -- An enhanced Interactive Python. Type '?' for help.

Warning: no DISPLAY environment variable.

--No graphics will be displayed.

1

WAS HERE 1WAS HERE 2could not open morphology/C300797C-I1_-_Clone_16.asc

NEURON: morphology/C300797C-I1_-_Clone_16.asc :file is not open

near line 0

^

File[0].eof()

Import3d_Neurolucida3[0].rdfile("morphology...")

Import3d_Neurolucida3[0].input("morphology...")

morphology_d04c4872bd[0].morphology(bAC217_L4_BP_d04c4872bd[0])

and others

To me, this indicates that there is a problem in your hoc code with linking to the morphology ascii file. If you can correct this in your hoc code, we can continue to help you with your NetPyNE code. It is not a simple task to convert a complex hoc model into NetPyNE! Start very simply, and only add complexity when everything is working.

Best wishes,

Joe

tut2input6_joe.py

Joe Graham

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Dec 1, 2021, 11:00:04 AM12/1/21

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To clarify, I executed the above attached file in the L4_BP_bAC217_1 directory (see that fileName='template.hoc'). In order to execute it from the main directory you will need to change the cellRule to fileName='L4_BP_bAC217_1/template.hoc'

You need to compile the mechanisms first, obviously. To run from the main directory, you would execute `nrnivmodl L4_BP_bAC217_1/mechanisms`.

When I tried to compile the mod files for both of your cells (nrnivmodl L4_BP_bAC217_1/mechanisms L6_TPC_L1_cADpyr231_1/mechanisms), there was an error in NEURON because of duplicated mechanism names. I would suggest putting all needed mechanisms into a single directory in the main directory.

Michael

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Dec 2, 2021, 3:04:24 PM12/2/21

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There were a lot of local path links that were replaced. New archive is at the same url

https://mp1233.github.io/bg/nrml_3d.zip

Still, if I use cellRule = netParams.importCellParams(..) only once it runs ok. But if I use it twice it returns error. How should I specify 2 morphologies L4PYR vs L4STEL? Should I join 2 cells into 1 hoc file?

Joe Graham

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Dec 3, 2021, 6:44:16 PM12/3/21

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Hello,

In the future, could you please put your code in a GitHub repo and share links instead of files? I have no idea what changes you've made since the last attachment, and I don't want these files filling up my email quota.

This was quite a confounding problem. I was able to replicate your error: each cell loads fine individually, but when loading both, a NEURON crash happens when loading the second one.

After putting a bunch of print statements in your hoc files, I was able to determine that NEURON was failing to load the second `morphology.hoc` file and thus failing to find the cell template and crashing. When I pasted the cell template code into `template.hoc`, the simulation loaded the second morphology but crashed because NEURON wasn't loading the second `synapses.hoc` file. The problem didn't seem to be with the code, but with finding/loading the files. Out of frustration, I changed the names of the hoc files for the second cell import and that worked.

After changing the file names for the second cell, you need to edit its template.hoc file also:

load_file("morphology_1.hoc")

load_file("biophysics_1.hoc")

load_file("synapses/synapses_1.hoc")

Once I did this, both cells loaded successfully. The problem seems to be that NEURON wants unique file names, but you might want to ask on the NEURON forum about that (https://www.neuron.yale.edu/phpBB/index.php). Perhaps you can use the same file name repeatedly if you close the earlier file first?

Anyway, it runs now and you should be set to add more complexity. Good luck and let us know if you have additional questions.

Cheers,

Joe

Michael

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Dec 4, 2021, 6:58:07 AM12/4/21

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Thank you very much! Now it works. I will place this working example at the same URL in case someone needs it

I also tried to change directories etc in python code to deal with their file system errors. but lost hope. if I knew there is a way I would make it

Michael

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Dec 13, 2021, 6:10:59 AM12/13/21

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Sorry, me again. Now that it works I'm trying to oscillate some population of pyramids. For some reason something blocks them and I'm trying to debug and "log" whats going on in synapses, dendrites, soma etc.
Probably there are some better tools. I know only

simConfig.analysis['plotTraces'] = {'include': [0,51,12,13,14,15], 'saveFig': True, 'saveData' : 'wow.json'}

does fine for spherical cells, but ignores morphology cells and outputs empty traces.

It would be nice if it displayed traces for soma at least. Ideally not only cells soma, but also synapses I,U and different sections of dendrites with ion channels

I have questions 1) Is there some trick to enable simConfig.analysis['plotTraces'] ? 2) What are the best tools to trace whats happening into multi department morphology cells?

Regards, Michael

Joe Graham

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Jan 3, 2022, 12:43:24 PM1/3/22

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Hello again Michael,

NetPyNE allows recording from morphologically-detailed cells. Please see the "Recording Configuration" section on the NetPyNE website: http://netpyne.org/reference.html#recording-configuration

In order to record traces, you must specify which traces to record (simConfig.recordTraces) and which cells to record the traces from (simConfig.recordCells). Note that the 'include' in plotTraces is a convenient shortcut that automatically adds the cells in 'include' to the list in simConfig.recordCells.

My best guess is that your traces are empty because the names of the compartments in morphologically-detailed cells are different from what you specified in recordTraces. For example, instead of a `soma` compartment, detailed cells often have a `soma_1` compartment. To see the names of compartments, run your sim and then look at, e.g. `sim.net.allCells[0]['secs'].keys()`.

Hopefully this will solve your problem. If not, please post a minimal example demonstrating the problem and I will take another look.

Good luck and Happy New Year,

Joe

Message has been deleted

Michael

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Feb 4, 2022, 6:31:08 AM2/4/22

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Thanks! It works fine now. I'm using smth like this

'NMDA_i_soma0':{'sec':'soma_0','loc':0.5,'synMech': 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_2844','var':'i_NMDA'}, 'AMPA_i_soma0':{'sec':'soma_0','loc':0.5,'synMech': 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_2844','var':'i_AMPA'}, 'CAHVA_ica_soma0':{'sec':'soma_0','loc':0.5,'mech': 'Ca_HVA','var':'ica'}}

and I can see all variables (Mg, Ca, g_AMPA etc)

However, there are 2 new questions.

1 I have sequence of neurons linked

N1 >>> N2 >>> N3 >>> N4 etc

when I change WEIGHT between N2 N3 netParams.connParams['Axon_N2_N3] = { 'preConds': {'pop': N2}, 'postConds': {'pop':N3}, 'probability':1, 'weight': SOME_NEW_WEIGHT ...

V plots of N1 & N2 are also affected. However there are no feedbacks from N3 to N2 or N1. So changes in connection between N2>N3 cannot affect N1 or N2 plots

Why could that be?

2 When I print out location of all Mechs & SynMechs :

for each in sim.net.allCells :
print(each['secs'])

I see that all synapses are attached to soma_0. and none of them attached to dendrites. (I would expect my synapses to be attached to apical dendrites). Does that mean that all synapses in model are at soma instead of dendrites? How can I attach synapses to apical dendrites to see dendrites bursting?

This could be fixed by specifying

netParams.connParams['Axon_N1_N2'] = { 'preConds': {'pop': N1}, 'postConds': {'pop': N2}, 'probability': 1, 'weight': _weight, 'delay': _delay_curr, 'synMech': 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_2494' , 'sec': 'apic_1'}

However NEURON model already specifies 'sec' location of synapses. By default I was expected not 'soma_0', but rather 'apic_2'

On Tuesday, 1 February 2022 at 19:02:03 UTC+3 Michael wrote:

Thanks! It works fine now. I'm using smth like this

'NMDA_i_soma0':{'sec':'soma_0','loc':0.5,'synMech': 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_2844','var':'i_NMDA'}, 'AMPA_i_soma0':{'sec':'soma_0','loc':0.5,'synMech': 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_2844','var':'i_AMPA'}, 'CAHVA_ica_soma0':{'sec':'soma_0','loc':0.5,'mech': 'Ca_HVA','var':'ica'}}

and I can see all variables (Mg, Ca, g_AMPA etc)

However, there is a new question. When I print out location of all Mechs & SynMechs :

for each in sim.net.allCells :
print(each['secs'])

I see that all synapses are attached to soma_0. and none of them attached to dendrites. (I would expect my synapses to be attached to apical dendrites). Does that mean that all synapses in model are at soma instead of dendrites? How can I attach synapses to apical dendrites to see dendrites bursting? Log part here:

{soma_0: {
mechs: {CaDynamics_E2: {gamma: 0.002253, decay: 739.416497, depth: 0.1, minCai: 0.0001}, Ca_HVA: {gCa_HVAbar: 0.000379}, Ca_LVAst: {gCa_LVAstbar: 0.006868}, Ih: {gIhbar: 8e-05}, NaTs2_t: {gNaTs2_tbar: 0.999812}, SK_E2: {gSK_E2bar: 0.002971}, SKv3_1: {gSKv3_1bar: 0.283745}, pas: {g: 3e-05, e: -75.0}}, ions: {ca: {e: 132.4579341637009, i: 5e-05, o: 2.0}, k: {e: -85.0, i: 54.4, o: 2.5}, na: {e: 50.0, i: 10.0, o: 140.0}},
geom: {L: 19.70778977844592, nseg: 1, diam: 11.817695207012488, Ra: 100.0, cm: 1.0, pt3d: [(-5.983231648802757, -7.466749489307404, 2.3553842306137085, 4.325639724731445), (-5.384908780455589, -6.720074474811554, 2.119845747947693, 7.715178966522217), (-4.786585435271263, -5.973399460315704, 1.8843073844909668, 11.251543998718262), (-4.188262328505516, -5.226724445819855, 1.6487689018249512, 12.147934913635254), (-3.589939221739769, -4.480049669742584, 1.413230538368225, 12.83663558959961), (-2.991615876555443, -3.7333746552467346, 1.1776920557022095, 13.498978614807129), (-2.3932927697896957, -2.986699879169464, 0.9421536922454834, 14.161320686340332), (-1.7949695438146591, -2.2400248646736145, 0.7066152691841125, 14.816628456115723), (-1.1966463774442673, -1.4933499097824097, 0.4710768461227417, 15.125845909118652), (-0.598323181271553, -0.746674969792366, 0.23553841561079025, 15.289164543151855), (0.0, 0.0, 0.0, 15.452483177185059), (0.598323181271553, 0.7466749548912048, -0.23553842306137085, 15.767749786376953), (1.1966463476419449, 1.493349850177765, -0.4710768461227417, 15.504411697387695), (1.794969692826271, 2.240024745464325, -0.7066152691841125, 14.388397216796875), (2.393292799592018, 2.9866997599601746, -0.9421536922454834, 13.268117904663086), (2.991615906357765, 3.733374774456024, -1.1776920557022095, 11.944937705993652), (3.5899390131235123, 4.480049788951874, -1.413230538368225, 10.226757049560547), (4.188262358307838, 5.226724803447723, -1.6487689018249512, 8.508577346801758), (4.786585703492165, 5.973399817943573, -1.8843072652816772, 6.779858112335205), (5.384908571839333, 6.720074355602264, -2.119845747947693, 4.194440841674805), (5.983231917023659, 7.466749370098114, -2.3553842306137085, 2.6242411136627197)]}, topol: {},
synMechs: [{label: 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_2844', loc: 0.5, mod: 'ProbAMPANMDA_EMS', tau_r_AMPA: 0.2, tau_d_AMPA: 1.5830986499786377, tau_r_NMDA: 0.29, tau_d_NMDA: 43.0, Use: 0.4825665056705475, Dep: 656.0, Fac: 13.0, e: 0.0, mg: 1.0, u0: 0.0, synapseID: 0.0, verboseLevel: 0.0, NMDA_ratio: 0.4}, {label: 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_2844', loc: 0.5, mod: 'ProbAMPANMDA_EMS', tau_r_AMPA: 0.2, tau_d_AMPA: 1.5830986499786377, tau_r_NMDA: 0.29, tau_d_NMDA: 43.0, Use: 0.4825665056705475, Dep: 656.0, Fac: 13.0, e: 0.0, mg: 1.0, u0: 0.0, synapseID: 0.0, verboseLevel: 0.0, NMDA_ratio: 0.4}, {label: 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_2844', loc: 0.5, mod: 'ProbAMPANMDA_EMS', tau_r_AMPA: 0.2, tau_d_AMPA: 1.5830986499786377, tau_r_NMDA: 0.29, tau_d_NMDA: 43.0, Use: 0.4825665056705475, Dep: 656.0, Fac: 13.0, e: 0.0, mg: 1.0, u0: 0.0, synapseID: 0.0, verboseLevel: 0.0, NMDA_ratio: 0.4}, {label: 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_2844', loc: 0.5, mod: 'ProbAMPANMDA_EMS', tau_r_AMPA: 0.2, tau_d_AMPA: 1.5830986499786377, tau_r_NMDA: 0.29, tau_d_NMDA: 43.0, Use: 0.4825665056705475, Dep: 656.0, Fac: 13.0, e: 0.0, mg: 1.0, u0: 0.0, synapseID: 0.0, verboseLevel: 0.0, NMDA_ratio: 0.4}, {label: 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_2844', loc: 0.5, mod: 'ProbAMPANMDA_EMS', tau_r_AMPA: 0.2, tau_d_AMPA: 1.5830986499786377, tau_r_NMDA: 0.29, tau_d_NMDA: 43.0, Use: 0.4825665056705475, Dep: 656.0, Fac: 13.0, e: 0.0, mg: 1.0, u0: 0.0, synapseID: 0.0, verboseLevel: 0.0, NMDA_ratio: 0.4}, {label: 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_1076', loc: 0.5, mod: 'ProbAMPANMDA_EMS', tau_r_AMPA: 0.2, tau_d_AMPA: 1.816912293434143, tau_r_NMDA: 0.29, tau_d_NMDA: 43.0, Use: 0.5085458159446716, Dep: 678.0, Fac: 19.0, e: 0.0, mg: 1.0, u0: 0.0, synapseID: 0.0, verboseLevel: 0.0, NMDA_ratio: 0.86}, {label: 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_1076', loc: 0.5, mod: 'ProbAMPANMDA_EMS', tau_r_AMPA: 0.2, tau_d_AMPA: 1.816912293434143, tau_r_NMDA: 0.29, tau_d_NMDA: 43.0, Use: 0.5085458159446716, Dep: 678.0, Fac: 19.0, e: 0.0, mg: 1.0, u0: 0.0, synapseID: 0.0, verboseLevel: 0.0, NMDA_ratio: 0.86}, {label: 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_1076', loc: 0.5, mod: 'ProbAMPANMDA_EMS', tau_r_AMPA: 0.2, tau_d_AMPA: 1.816912293434143, tau_r_NMDA: 0.29, tau_d_NMDA: 43.0, Use: 0.5085458159446716, Dep: 678.0, Fac: 19.0, e: 0.0, mg: 1.0, u0: 0.0, synapseID: 0.0, verboseLevel: 0.0, NMDA_ratio: 0.86}, {label: 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_1076', loc: 0.5, mod: 'ProbAMPANMDA_EMS', tau_r_AMPA: 0.2, tau_d_AMPA: 1.816912293434143, tau_r_NMDA: 0.29, tau_d_NMDA: 43.0, Use: 0.5085458159446716, Dep: 678.0, Fac: 19.0, e: 0.0, mg: 1.0, u0: 0.0, synapseID: 0.0, verboseLevel: 0.0, NMDA_ratio: 0.86}, {label: 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_1076', loc: 0.5, mod: 'ProbAMPANMDA_EMS', tau_r_AMPA: 0.2, tau_d_AMPA: 1.816912293434143, tau_r_NMDA: 0.29, tau_d_NMDA: 43.0, Use: 0.5085458159446716, Dep: 678.0, Fac: 19.0, e: 0.0, mg: 1.0, u0: 0.0, synapseID: 0.0, verboseLevel: 0.0, NMDA_ratio: 0.86}, {label: 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_3474', loc: 0.5, mod: 'ProbAMPANMDA_EMS', tau_r_AMPA: 0.2, tau_d_AMPA: 1.7753068208694458, tau_r_NMDA: 0.29, tau_d_NMDA: 43.0, Use: 0.5039229989051819, Dep: 674.0, Fac: 18.0, e: 0.0, mg: 1.0, u0: 0.0, synapseID: 0.0, verboseLevel: 0.0, NMDA_ratio: 0.4}, {label: 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_3474', loc: 0.5, mod: 'ProbAMPANMDA_EMS', tau_r_AMPA: 0.2, tau_d_AMPA: 1.7753068208694458, tau_r_NMDA: 0.29, tau_d_NMDA: 43.0, Use: 0.5039229989051819, Dep: 674.0, Fac: 18.0, e: 0.0, mg: 1.0, u0: 0.0, synapseID: 0.0, verboseLevel: 0.0, NMDA_ratio: 0.4}, {label: 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_3474', loc: 0.5, mod: 'ProbAMPANMDA_EMS', tau_r_AMPA: 0.2, tau_d_AMPA: 1.7753068208694458, tau_r_NMDA: 0.29, tau_d_NMDA: 43.0, Use: 0.5039229989051819, Dep: 674.0, Fac: 18.0, e: 0.0, mg: 1.0, u0: 0.0, synapseID: 0.0, verboseLevel: 0.0, NMDA_ratio: 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tau_r_NMDA: 0.29, tau_d_NMDA: 43.0, Use: 0.5139469504356384, Dep: 683.0, Fac: 20.0, e: 0.0, mg: 1.0, u0: 0.0, synapseID: 0.0, verboseLevel: 0.0, NMDA_ratio: 0.4}, {label: 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_3519', loc: 0.5, mod: 'ProbAMPANMDA_EMS', tau_r_AMPA: 0.2, tau_d_AMPA: 1.6473129987716675, tau_r_NMDA: 0.29, tau_d_NMDA: 43.0, Use: 0.48970144987106323, Dep: 662.0, Fac: 14.0, e: 0.0, mg: 1.0, u0: 0.0, synapseID: 0.0, verboseLevel: 0.0, NMDA_ratio: 0.4}, {label: 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_3519', loc: 0.5, mod: 'ProbAMPANMDA_EMS', tau_r_AMPA: 0.2, tau_d_AMPA: 1.6473129987716675, tau_r_NMDA: 0.29, tau_d_NMDA: 43.0, Use: 0.48970144987106323, Dep: 662.0, Fac: 14.0, e: 0.0, mg: 1.0, u0: 0.0, synapseID: 0.0, verboseLevel: 0.0, NMDA_ratio: 0.4}, {label: 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_3519', loc: 0.5, mod: 'ProbAMPANMDA_EMS', tau_r_AMPA: 0.2, tau_d_AMPA: 1.6473129987716675, tau_r_NMDA: 0.29, tau_d_NMDA: 43.0, Use: 0.48970144987106323, Dep: 662.0, Fac: 14.0, e: 0.0, mg: 1.0, u0: 0.0, synapseID: 0.0, verboseLevel: 0.0, NMDA_ratio: 0.4}, {label: 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_3519', loc: 0.5, mod: 'ProbAMPANMDA_EMS', tau_r_AMPA: 0.2, tau_d_AMPA: 1.6473129987716675, tau_r_NMDA: 0.29, tau_d_NMDA: 43.0, Use: 0.48970144987106323, Dep: 662.0, Fac: 14.0, e: 0.0, mg: 1.0, u0: 0.0, synapseID: 0.0, verboseLevel: 0.0, NMDA_ratio: 0.4}, {label: 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_3519', loc: 0.5, mod: 'ProbAMPANMDA_EMS', tau_r_AMPA: 0.2, tau_d_AMPA: 1.6473129987716675, tau_r_NMDA: 0.29, tau_d_NMDA: 43.0, Use: 0.48970144987106323, Dep: 662.0, Fac: 14.0, e: 0.0, mg: 1.0, u0: 0.0, synapseID: 0.0, verboseLevel: 0.0, NMDA_ratio: 0.4}, {label: 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_3499', loc: 0.5, mod: 'ProbAMPANMDA_EMS', tau_r_AMPA: 0.2, tau_d_AMPA: 1.7609988451004028, tau_r_NMDA: 0.29, tau_d_NMDA: 43.0, Use: 0.5023332238197327, Dep: 673.0, Fac: 18.0, e: 0.0, mg: 1.0, u0: 0.0, synapseID: 0.0, verboseLevel: 0.0, 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loc: 0.5, mod: 'ProbAMPANMDA_EMS', tau_r_AMPA: 0.2, tau_d_AMPA: 1.7609988451004028, tau_r_NMDA: 0.29, tau_d_NMDA: 43.0, Use: 0.5023332238197327, Dep: 673.0, Fac: 18.0, e: 0.0, mg: 1.0, u0: 0.0, synapseID: 0.0, verboseLevel: 0.0, NMDA_ratio: 0.4}, {label: 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_3486', loc: 0.5, mod: 'ProbAMPANMDA_EMS', tau_r_AMPA: 0.2, tau_d_AMPA: 1.7491434812545776, tau_r_NMDA: 0.29, tau_d_NMDA: 43.0, Use: 0.5010159611701965, Dep: 672.0, Fac: 17.0, e: 0.0, mg: 1.0, u0: 0.0, synapseID: 0.0, verboseLevel: 0.0, NMDA_ratio: 0.4}, {label: 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_3486', loc: 0.5, mod: 'ProbAMPANMDA_EMS', tau_r_AMPA: 0.2, tau_d_AMPA: 1.7491434812545776, tau_r_NMDA: 0.29, tau_d_NMDA: 43.0, Use: 0.5010159611701965, Dep: 672.0, Fac: 17.0, e: 0.0, mg: 1.0, u0: 0.0, synapseID: 0.0, verboseLevel: 0.0, NMDA_ratio: 0.4}, {label: 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_3486', loc: 0.5, mod: 'ProbAMPANMDA_EMS', tau_r_AMPA: 0.2, tau_d_AMPA: 1.7491434812545776, tau_r_NMDA: 0.29, tau_d_NMDA: 43.0, Use: 0.5010159611701965, Dep: 672.0, Fac: 17.0, e: 0.0, mg: 1.0, u0: 0.0, synapseID: 0.0, verboseLevel: 0.0, NMDA_ratio: 0.4}, {label: 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_3486', loc: 0.5, mod: 'ProbAMPANMDA_EMS', tau_r_AMPA: 0.2, tau_d_AMPA: 1.7491434812545776, tau_r_NMDA: 0.29, tau_d_NMDA: 43.0, Use: 0.5010159611701965, Dep: 672.0, Fac: 17.0, e: 0.0, mg: 1.0, u0: 0.0, synapseID: 0.0, verboseLevel: 0.0, NMDA_ratio: 0.4}, {label: 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_3486', loc: 0.5, mod: 'ProbAMPANMDA_EMS', tau_r_AMPA: 0.2, tau_d_AMPA: 1.7491434812545776, tau_r_NMDA: 0.29, tau_d_NMDA: 43.0, Use: 0.5010159611701965, Dep: 672.0, Fac: 17.0, e: 0.0, mg: 1.0, u0: 0.0, synapseID: 0.0, verboseLevel: 0.0, NMDA_ratio: 0.4}, {label: 'cADpyr230_L4_PC_f15e35e578_ProbAMPANMDA_EMS_328', loc: 0.5, mod: 'ProbAMPANMDA_EMS', tau_r_AMPA: 0.2, tau_d_AMPA: 1.8135448694229126, tau_r_NMDA: 0.29, tau_d_NMDA: 43.0, Use: 0.5081716775894165, 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dend_0: {mechs: {Ih: {gIhbar: 8e-05}, pas: {g: 3e-05, e: -75.0}}, geom: {L: 11.80063463734862, nseg: 1, diam: 1.583131310883715, Ra: 100.0, cm: 2.0, pt3d: [(8.301841631531715, -3.3300259709358215, -1.3833943605422974, 1.9900000095367432), (6.875161066651344, -2.8574958443641663, -1.1316944360733032, 1.9900000095367432), (9.680810824036598, -8.11527568101883, -0.40068739652633667, 1.3300000429153442), (11.677301302552223, -11.856755554676056, 0.11950594640802592, 1.3300000429153442)]}, topol: {parentSec: 'soma_0', parentX: 0.5, childX: 0.0}},

dend_1: {mechs: {Ih: {gIhbar: 8e-05}, pas: {g: 3e-05, e: -75.0}}, geom: {L: 10.292680259551412, nseg: 1, diam: 1.3300000429153442, Ra: 100.0, cm: 2.0, pt3d: [(11.677301302552223, -11.856755554676056, 0.11950594640802592, 1.3300000429153442), (16.754000559449196, -12.54315596818924, 9.046725392341614, 1.3300000429153442)]}, topol: {parentSec: 'dend_0', parentX: 1.0, childX: 0.0}},

dend_2: {mechs: {Ih: {gIhbar: 8e-05}, pas: {g: 3e-05, e: -75.0}}, geom: {L: 12.407076679122701, nseg: 1, diam: 0.7724935556142847, Ra: 100.0, cm: 2.0, pt3d: [(16.754000559449196, -12.54315596818924, 9.046725392341614, 1.3300000429153442), (18.340201273560524, -15.300155937671661, 11.737655758857727, 0.6600000262260437), (20.45790185034275, -18.981256783008575, 15.330355763435364, 0.6600000262260437), (21.477501764893532, -20.75345641374588, 17.060155034065247, 0.6600000262260437)]}, topol: {parentSec: 'dend_1', parentX: 1.0, childX: 0.0}},

dend_3: {mechs: {Ih: {gIhbar: 8e-05}, pas: {g: 3e-05, e: -75.0}}, geom: {L: 34.644825458904926, nseg: 1, diam: 0.5215487261268827, Ra: 100.0, cm: 2.0, pt3d: [(21.477501764893532, -20.75345641374588, 17.060155034065247, 0.6600000262260437), (21.473900690674782, -22.36195594072342, 19.223456501960754, 0.33000001311302185), (21.48300065100193, -23.900554955005646, 21.153555989265442, 0.6600000262260437), (21.546101465821266, -26.16535598039627, 23.73115646839142, 0.33000001311302185), (21.557202234864235, -26.86045676469803, 24.478156208992004, 0.6600000262260437), (21.508301630616188, -32.3583567738533, 30.182854771614075, 0.33000001311302185), (21.491101160645485, -34.30055648088455, 32.19805443286896, 0.33000001311302185), (21.47080124914646, -35.081856071949005, 33.03045570850372, 0.6600000262260437), (21.499001398682594, -36.465557396411896, 34.37375557422638, 0.33000001311302185), (21.549500361084938, -37.15515547990799, 34.99235451221466, 0.33000001311302185), (21.586100473999977, -37.90935546159744, 35.659255146980286, 0.6600000262260437), (21.323201075196266, -43.243755638599396, 43.2739554643631, 0.6600000262260437)]}, topol: {parentSec: 'dend_2', parentX: 1.0, childX: 0.0}},

dend_4: {mechs: {Ih: {gIhbar: 8e-05}, pas: {g: 3e-05, e: -75.0}}, geom: {L: 66.83271743097727, nseg: 3, diam: 0.33000001311302185, Ra: 100.0, cm: 2.0, pt3d: [(21.323201075196266, -43.243755638599396, 43.2739554643631, 0.6600000262260437), (21.18630112707615, -48.32155638933182, 48.64925682544708, 0.33000001311302185), (20.653700724244118, -54.509956657886505, 52.73015511035919, 0.33000001311302185), (20.754401102662086, -60.63695555925369, 56.93745720386505, 0.33000001311302185), (19.514000788331032, -66.5905573964119, 61.187655568122864, 0.33000001311302185), (17.773600473999977, -72.56865340471268, 65.22115242481232, 0.33000001311302185), (16.63590134680271, -79.16275435686111, 68.48885262012482, 0.33000001311302185), (16.43950165808201, -85.1425593495369, 72.88675606250763, 0.33000001311302185), (15.908600702881813, -91.26525527238846, 77.06105720996857, 0.33000001311302185), (16.34000100195408, -97.31875258684158, 81.35155403614044, 0.33000001311302185)]}, topol: {parentSec: 'dend_3', parentX: 1.0, childX: 0.0}},

dend_5: {mechs: {Ih: {gIhbar: 8e-05}, pas: {g: 3e-05, e: -75.0}}, geom: {L: 60.7746917130186, nseg: 3, diam: 0.33000001311302185, Ra: 100.0, cm: 2.0, pt3d: [(21.323201075196266, -43.243755638599396, 43.2739554643631, 0.6600000262260437), (22.358900919556618, -49.68665724992752, 53.49355614185333, 0.33000001311302185), (21.53930176794529, -57.807956993579865, 62.539355397224426, 0.33000001311302185), (19.398701563477516, -65.41295272111893, 71.80855476856232, 0.33000001311302185), (18.022301569581032, -72.99945479631424, 81.22775185108185, 0.33000001311302185), (16.15630044043064, -78.10085707902908, 92.05485451221466, 0.33000001311302185)]}, topol: {parentSec: 'dend_3', parentX: 1.0, childX: 0.0}},

dend_6: {mechs: {Ih: {gIhbar: 8e-05}, pas: {g: 3e-05, e: -75.0}}, geom: {L: 49.0320178050447, nseg: 3, diam: 0.3300000131130219, Ra: 100.0, cm: 2.0, pt3d: [(16.15630044043064,

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