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reorganise
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‎NeuroML2/compare_SC/HTC/HTC_reduced_cell.nml‎

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<?xml version="1.0" encoding="UTF-8"?>
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<neuroml xmlns="http://www.neuroml.org/schema/neuroml2"
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xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
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xsi:schemaLocation="http://www.neuroml.org/schema/neuroml2 https://raw.github.com/NeuroML/NeuroML2/development/Schemas/NeuroML2/NeuroML_v2.3.xsd"
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id = "NeuroML2_file_exported_from_NEURON">
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<notes> NeuroML 2 file generated from ModelView by: NEURON -- VERSION 8.2.6+ HEAD (078a34a9d+) 2024-07-24
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Authors: Michael Hines, Sushil Kambampati and Padraig Gleeson,
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Yale University and UCL
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</notes>
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<cell id="HTC_reduced_cell_0_0">
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<notes>Cell: HTC_reduced_cell_0_0 exported from NEURON ModelView</notes>
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<morphology id="morphology">
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<!-- Section: HTC_reduced_cell[0].soma (segmentGroup: soma) which has 2 3D points, so 1 segment(s)-->
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<segment id="0" name = "Seg0_soma">
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<proximal x="0" y="0" z="0" diameter="96"/>
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<distal x="0" y="96" z="0" diameter="96"/>
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</segment>
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<!-- Section: HTC_reduced_cell[0].soma (segmentGroup: soma) which has 2 3D points, so 1 segment(s)-->
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<segmentGroup id = "soma" neuroLexId="sao864921383">
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<notes>This group contains an unbranched set of segments, and all of the segmentGroups marked with
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neuroLexId = sao864921383 form a non-overlapping set of all of the segments.
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These segmentGroups correspond to the 'cables' of NeuroML v1.8.1, and map to/from NEURON sections.</notes>
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<member segment = "0"/>
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</segmentGroup>
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<segmentGroup id="all"> <!-- Replacing subset name ModelViewParmSubset_0 with all, as it contains all sections -->
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<include segmentGroup = "soma"/>
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</segmentGroup>
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<segmentGroup id="OneSecGrp_SectionRef_1"> <!-- Creating a group containing a single section: soma... -->
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<include segmentGroup = "soma"/>
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</segmentGroup>
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</morphology>
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<biophysicalProperties id="biophys">
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<membraneProperties>
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<channelDensityNernst id="cal_all" ionChannel="cal" condDensity="0.001 S_per_cm2" segmentGroup="all" ion="ca"/>
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<channelDensity ionChannel="ia" id="ia_all" segmentGroup="all" ion="k" erev="-95.0 mV" condDensity="0.007 S_per_cm2"/>
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TODO: <channelDensity ionChannel="ittc" id="ittc_all" segmentGroup="all" ion="ca" erev="-104 mV" condDensity="0.002 S_per_cm2"/>
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TODO: htc
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TODO: hh2ad
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<channelDensity ionChannel="kl" id="kl_all" segmentGroup="all" ion="k" erev="-95.0 mV" condDensity="2.3e-05 S_per_cm2"/>
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<channelDensity ionChannel="pas" id="pas_all" segmentGroup="all" ion="non_specific" erev="-50 mV" condDensity="1.8e-05 S_per_cm2"/>
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<spikeThresh value="5mV"/>
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<specificCapacitance segmentGroup="all" value="1.0 uF_per_cm2"/>
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<initMembPotential segmentGroup="all" value="-70 mV"/>
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</membraneProperties>
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<intracellularProperties>
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<species segmentGroup="all" ion="ca" initialExtConcentration="2.0E-6 mol_per_cm3" concentrationModel="cadad_HTC_soma" id="ca" initialConcentration="5.0E-11 mol_per_cm3"/>
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<resistivity value="100 ohm_cm"/>
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</intracellularProperties>
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</biophysicalProperties>
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</cell>
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</neuroml>

‎NeuroML2/compare_SC/HTC/HTC_reduced_cellParams.json‎

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{
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"conds": {
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"cellModel": "HH_reduced",
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"cellType": "HTC",
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"ynorm": [
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1.2,
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1.4
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]
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},
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"globals": {
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"erev_kl": -95.0,
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"q10m_ittc": 3.55,
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"v_init": -70.0
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},
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"secLists": {},
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"secs": {
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"soma": {
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"geom": {
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"L": 96.0,
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"Ra": 100.0,
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"cm": 1.0,
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"diam": 96.0,
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"nseg": 1,
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"pt3d": [
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[
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0,
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0,
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0,
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96.0
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],
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[
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0,
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96.0,
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0,
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96.0
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]
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]
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},
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"ions": {
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"ca": {
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"e": 132.4579341637009,
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"i": 5e-05,
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"o": 2.0
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},
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"k": {
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"e": -95.0,
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"i": 54.4,
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"o": 2.5
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},
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"na": {
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"e": 50.0,
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"i": 10.0,
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"o": 140.0
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}
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},
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"mechs": {
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"cadad": {
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"cainf": 0.00024,
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"depth": 1.0,
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"kd": 0.0,
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"kt": 0.0,
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"taur": 5.0
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},
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"cal": {
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"gcalbar": 0.001
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},
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"hh2ad": {
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"gkbar": 0.012,
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"gnabar": 0.02,
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"vtraub": -63.0
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},
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"htc": {
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"Pc": 0.01,
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"cac": 0.002,
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"exptemp": 36.0,
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"ginc": 2.0,
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"gmax": 1e-06,
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"k2": 0.0004,
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"k4": 0.001,
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"nca": 4.0,
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"nexp": 1.0,
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"shift": 0.0,
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"taum": 20.0
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},
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"ia": {
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"exptemp": 23.5,
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"gmax": 0.007,
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"q10": 3.0
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},
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"ittc": {
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"gmax": 0.002,
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"shift": 3
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},
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"kl": {
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"gmax": 2.3e-05
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},
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"pas": {
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"e": -50,
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"g": 1.8e-05
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}
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},
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"topol": {},
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"vinit": -70.0,
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"weightNorm": [
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0.0020910235715425945
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]
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}
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}
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}

‎NeuroML2/compare_SC/IT2/IT2_json.py‎

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from netpyne import specs, sim
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import pprint
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netParams = specs.NetParams()
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simConfig = specs.SimConfig()
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netParams.loadCellParamsRule(label='IT2_HH_reduced', fileName='IT2_reduced_cellParams.json')
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pprint.pprint(netParams.cellParams['IT2_HH_reduced'])
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netParams.popParams['IT2'] = {'cellType': 'IT2_HH_reduced', 'numCells': 1}
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netParams.stimSourceParams['Input'] = {'type': 'IClamp', 'dur': 1600, 'del': 200, 'amp': 0.3}
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netParams.stimTargetParams['Input->IT2'] = {'source': 'Input', 'sec': 'soma', 'loc': 0.5, 'conds': {'cellType': 'IT2_HH_reduced'}}
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# Simulation options
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simConfig = specs.SimConfig() # object of class SimConfig to store simulation configuration
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simConfig.recordCells = ['all']
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simConfig.hParams['celsius'] = 34
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simConfig.duration = 2000 # Duration of the simulation, in ms
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simConfig.dt = 0.01 # Internal integration timestep to use
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simConfig.verbose = False # Show detailed messages
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simConfig.recordTraces = {'V_soma':{'sec':'soma','loc':0.5,'var':'v'}} # Dict with traces to record
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simConfig.recordStep = 1 # Step size in ms to save data (eg. V traces, LFP, etc)
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simConfig.filename = 'IT2_reduced_json' # Set file output name
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simConfig.savePickle = False # Save params, network and sim output to pickle file
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simConfig.saveDataInclude = ['simData']
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simConfig.saveJson = True
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simConfig.analysis['plotTraces'] = {'include': [0], 'saveFig': True} # Plot recorded traces for this list of cells
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simConfig.analysis['plotRaster'] = {'saveFig': True} # Plot a raster
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# Create network and run simulation
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sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig)
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#import pylab; pylab.show() # this line is only necessary in certain systems where figures appear empty

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