Sensory-evoked responses of L5 pyramidal tract neurons (Egger et al 2020)

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This is the L5 pyramidal tract neuron (L5PT) model from Egger, Narayanan et al., Neuron 2020. It allows investigating how synaptic inputs evoked by different sensory stimuli are integrated by the complex intrinsic properties of L5PTs. The model is constrained by anatomical measurements of the subcellular synaptic input patterns to L5PT neurons, in vivo measurements of sensory-evoked responses of different populations of neurons providing these synaptic inputs, and in vitro measurements constraining the biophysical properties of the soma, dendrites and axon (note: the biophysical model is based on the work by Hay et al., Plos Comp Biol 2011). The model files provided here allow performing simulations and analyses presented in Figures 3, 4 and 5.
1 . Egger R, Narayanan RT, Guest JM, Bast A, Udvary D, Messore LF, Das S, de Kock CP, Oberlaender M (2020) Cortical Output Is Gated by Horizontally Projecting Neurons in the Deep Layers Neuron
Model Information (Click on a link to find other models with that property)
Model Type: Dendrite; Realistic Network; Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex L5/6 pyramidal GLU cell;
Channel(s): I Calcium; I h; I M; I K; I Na,t; I Na,p; I K,Ca;
Gap Junctions:
Receptor(s): AMPA; GabaA; NMDA;
Transmitter(s): Glutamate; Gaba;
Simulation Environment: NEURON; Python;
Model Concept(s): Active Dendrites; Detailed Neuronal Models; Sensory processing; Stimulus selectivity; Synaptic Integration;
Implementer(s): Egger, Robert [robert.egger at];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; GabaA; AMPA; NMDA; I Na,p; I Na,t; I K; I M; I h; I K,Ca; I Calcium; Gaba; Glutamate;
import os, glob
from sumatra.parameters import build_parameters, NTParameterSet

installationDirectory = os.path.abspath(os.path.dirname(__file__))

controlFolder = os.path.join(installationDirectory, 'evoked_activity/control')
manipulation1Folder = os.path.join(installationDirectory, 'evoked_activity/manipulation1')
if not os.path.exists(manipulation1Folder):

for fname in glob.glob(os.path.join(controlFolder, '*')):
    if fname.endswith('.param'):
        outName = fname.replace('control', 'manipulation1')
        # load control file as ParameterSet
        controlParameters = build_parameters(fname)
        # pop population (manipulation 1: L5/6BS in E2)
        newParameters = controlParameters.tree_copy()
        for population in
            if 'L6cc_E2' == population:
        # save ParameterSet as new file

header = '#!/bin/bash\n'
header += '\n'
header += 'cd '
header += manipulation1Folder
header += '\n'

whiskers = ['B1','B2','B3','C1','C2','C3','D1','D2','D3','E2']
cellLocations = ['B1border','B2border','B3border','C1border','C2center',\

scriptFolder = os.path.join(installationDirectory, 'manipulation1_scripts')
if not os.path.exists(scriptFolder):

for whisker in whiskers:
    for cellLocation in cellLocations:
        suffix = whisker + '_deflection_manipulation1_cell_location_' + cellLocation + '.sh'
        scriptName = os.path.join(scriptFolder, suffix)
        with open(scriptName, 'w') as scriptFile:
            line = 'python '
            line += os.path.join(controlFolder, '')
            line += ' '

            # output name
            line += whisker
            line += '_deflection_manipulation1_cell_location_'
            line += cellLocation
            # neuron model parameters
            line += ' ../../connectome/control/'
            line += cellLocation
            line += '/86_CDK_20041214_BAC_run5_soma_Hay2013_'
            line += cellLocation
            line += '_apic_rec_scaled_diameters.param '
            # network model parameters
            line += whisker
            line += '_deflection_manipulation1_cell_location_'
            line += cellLocation
            line += '.param\n'
        # make sure file is also executable
        os.chmod(scriptName, 0777)

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