Distal inhibitory control of sensory-evoked excitation (Egger, Schmitt et al. 2015)

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Model of a cortical layer (L) 2 pyramidal neuron embedded in an anatomically realistic network of two barrel columns in rat vibrissal cortex. This model is used to investigate the effects of spatially and temporally specific inhibition from L1 inhibitory interneurons on the sensory-evoked subthreshold responses of the L2 pyramidal neuron, and can be used to create simulation results underlying Figures 3D, 4B, 4C and 4E from (Egger, Schmitt et al. 2015).
1 . Egger R, Schmitt AC, Wallace DJ, Sakmann B, Oberlaender M, Kerr JN (2015) Robustness of sensory-evoked excitation is increased by inhibitory inputs to distal apical tuft dendrites. Proc Natl Acad Sci U S A 112:14072-7 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex L2/3 pyramidal GLU cell;
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON; Python;
Model Concept(s): Active Dendrites; Synaptic Integration; Sensory processing; Whisking;
Implementer(s): Egger, Robert [robert.egger at nyumc.org];
Search NeuronDB for information about:  Neocortex L2/3 pyramidal GLU cell; GabaA; AMPA; NMDA; Gaba; Glutamate;
L2 neuron model
evoked activity with/without L1 PW neurons
Analysis of sensitivity to inhibitory conductance strength
No variation in functional connectivity
of excitatory synapses between conditions
(i.e., activation times of excitatory synapses
are replayed between conditions)

written by Robert Egger (robert.egger@tuebingen.mpg.de)
(c) 2013-2015 Max Planck Society

import sys
import time
import os, os.path
import glob
import neuron
import single_cell_parser as scp
import single_cell_analyzer as sca
import numpy as np
hasMatplotlib = True
    import matplotlib.pyplot as plt
except ImportError:
    hasMatplotlib = False
h = neuron.h

def evoked_activity_replay_synapses_new(simName, cellName, gGABA, ongoingName, evokedParamName, replayFolder):
    pre-stimulus ongoing activity
    and evoked activity replayed from
    control trial of all variable case
    neuronParameters = scp.build_parameters(cellName)
    ongoingParameters = scp.build_parameters(ongoingName)
    evokedNWParameters = scp.build_parameters(evokedParamName)
    cellParam = neuronParameters.neuron
    paramOngoing = ongoingParameters.network
    paramEvoked = evokedNWParameters.network
    cell = scp.create_cell(cellParam, scaleFunc=dendriteScalingUniform)
    uniqueID = str(os.getpid())
    dirName = simName
    dirName += '_gGABA_'
    dirName += '%.1f' % gGABA
    if not simName.endswith('/'):
        dirName += '/'
    dirName += time.strftime('%Y%m%d-%H%M')
    if not os.path.exists(dirName):
    vTraces = []
    tTraces = []
    nSweeps = 2000
    #nSweeps = 2
    tOffset = 100.0 # avoid numerical transients
    tStim = 200.0
    tStop = 250.0
    neuronParameters.sim.tStop = tStop
    dt = neuronParameters.sim.dt
    offsetBin = int(tOffset/dt + 0.5)
    spikeThresh = -38.0 # Petersen, AS
    tStim = 200.0
    # Load synapse activation times for all trials of corresponding
    # network realization from 'replayFolder'.
    # Replay all synapses and only vary L1D1 evoked gGABA
    synInfoNames = []
    scan_directory(replayFolder, synInfoNames, '_synapses.csv')
    nRun = 0
    while nRun < nSweeps:
        synParameters = paramOngoing
        synParametersEvoked = paramEvoked
        synInfoName = ''
        nRunStr = 'run%04d' % nRun
        for name in synInfoNames:
            if nRunStr in name:
                synInfoName = name
        for synType in synParameters.keys():
            synParameters[synType].synapses.releaseProb = 1.0
        synParameters['L1D1'].synapses.receptors.gaba_syn.weight = gGABA
        print 'Replaying network activity from file %s' % synInfoName
        replayNW = scp.NetworkMapper(cell, synParameters)
        if gGABA == 0.0:
            if cell.synapses.has_key('L1D1'):
                for syn in cell.synapses['L1D1']:
            if cell.synapses.has_key('L1D1_ongoing'):
                for syn in cell.synapses['L1D1_ongoing']:
        if cell.synapses.has_key('L1D1'):
            for i in range(len(cell.synapses['L1D1'])):
                syn = cell.synapses['L1D1'][i]
                if syn.is_active():
                    L1strength = syn.netcons[0].weight[0]
                    print 'L1 synapse weight = %.1fnS' % L1strength
        print 'Testing evoked response properties run %d of %d' % (nRun+1, nSweeps)
        tVec = h.Vector()
        startTime = time.time()
        stopTime = time.time()
        simdt = stopTime - startTime
        print 'NEURON runtime: %.2f s' % simdt
        vmSoma = np.array(cell.soma.recVList[0])
        t = np.array(tVec)
        begin = int((tStim+15.0)/dt+0.5)
        end = int((tStim+50.0)/dt+0.5)
        maxV = np.max(vmSoma[begin:end])
        vTraces.append(np.array(vmSoma[offsetBin:])), tTraces.append(np.array(t[offsetBin:]))
        nRun += 1
        # no max V check here!!! we want the trace to be computed in any case!

        print '-------------------------------'
    vTraces = np.array(vTraces)
    print 'computing Vm STD and histogram'
    vStd = np.std(vTraces, axis=0)
    peakWindow, avgPeak = sca.compute_mean_psp_amplitude(vTraces, tStim=200.0-tOffset, dt=neuronParameters.sim.dt)
    windows, avgVmStd = sca.compute_vm_std_windows(vStd, tStim=200.0-tOffset, dt=neuronParameters.sim.dt)
    hist, bins = sca.compute_vm_histogram(vTraces)
    scp.write_all_traces(dirName+'/'+uniqueID+'_vm_all_traces.csv', t[offsetBin:], vTraces)
    scp.write_sim_results(dirName+'/'+uniqueID+'_vm_std.csv', t[offsetBin:], vStd)
    scp.write_sim_results(dirName+'/'+uniqueID+'_vm_avg_psp.csv', peakWindow, avgPeak)
    scp.write_sim_results(dirName+'/'+uniqueID+'_vm_std_windows.csv', windows, avgVmStd)
    scp.write_sim_results(dirName+'/'+uniqueID+'_vm_hist.csv', hist, bins[:-1])
    print 'writing simulation parameter files'
    if hasMatplotlib:
        ax = []
        for i in range(nSweeps):
            ax.append(plt.plot(tTraces[i], vTraces[i], 'k'))
        plt.xlabel('t [ms]')
        plt.ylabel('Vm [mV]')
        plt.plot(tTraces[0], vStd, 'k')
        plt.xlabel('t [ms]')
        plt.ylabel('Vm STD [mV]')

def scan_directory(path, fnames, suffix):
    for fname in glob.glob(os.path.join(path, '*')):
        if os.path.isdir(fname):
            scan_directory(fname, fnames, suffix)
        elif fname.endswith(suffix):

def dendriteScalingUniform(cell):
    dendScale = 1/1.2
    for sec in cell.sections:
        if sec.label == 'Dendrite' or sec.label == 'ApicalDendrite':
            dummy = h.pt3dclear(sec=sec)
            for i in range(sec.nrOfPts):
                x, y, z = sec.pts[i]
                sec.diamList[i] = sec.diamList[i]*dendScale
                d = sec.diamList[i]
                dummy = h.pt3dadd(x, y, z, d, sec=sec)

if __name__ == '__main__':
    if len(sys.argv) == 7:
        name = sys.argv[1]
        cellName = sys.argv[2]
        gGABA = float(sys.argv[3])
        ongoingName = sys.argv[4]
        evokedName = sys.argv[5]
        replayFolderName = sys.argv[6]
        evoked_activity_replay_synapses_new(name, cellName, gGABA, ongoingName, evokedName, replayFolderName)
        print 'Error! Number of arguments is %d; should be 5!' % (len(sys.argv)-1)