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

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Accession:239145
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.
Reference:
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
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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;
Gene(s):
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 nyumc.org];
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;
'''
Created on Feb 1, 2013

Implements standard scripts for
anatomical and functional network realizations.

@author: regger
'''

import os, time
import reader
import writer
import cell_parser
from synapse_mapper import SynapseMapper
from network import NetworkMapper
from sumatra.parameters import build_parameters
import neuron

def create_synapse_realization(pname):
    parameters = build_parameters(pname)
    cellParam = parameters.network.post
    preParam = parameters.network.pre
    
    parser = cell_parser.CellParser(cellParam.filename)
    parser.spatialgraph_to_cell()
    cell = parser.cell
    for preType in preParam.keys():
        synapseFName = preParam[preType].synapses.distributionFile
        synDist = reader.read_scalar_field(synapseFName)
        mapper = SynapseMapper(cell, synDist)
        mapper.create_synapses(preType)
    
    for synType in cell.synapses.keys():
        name = parameters.info.outputname
        name += '_'
        name += synType
        name += '_syn_realization'
        uniqueID = str(os.getpid())
        timeStamp = time.strftime('%Y%m%d-%H%M')
        name += '_' + timeStamp + '_' + uniqueID
        synapseList = []
        for syn in cell.synapses[synType]:
            synapseList.append(syn.coordinates)
        writer.write_landmark_file(name, synapseList)
        tmpSyns = {}
        tmpSyns[synType] = cell.synapses[synType]
        writer.write_cell_synapse_locations(name+'.syn', tmpSyns, cell.id)


def create_functional_network(cellParamName, nwParamName):
    '''
    Public interface:
    used for creating fixed functional connectivity.
    cellParamName - parameter file of postsynaptic neuron
    nwParamName - parameter file of anatomical network
    '''
    preParam = build_parameters(cellParamName)
    neuronParam = preParam.neuron
    nwParam = build_parameters(nwParamName)
    for mech in nwParam.NMODL_mechanisms.values():
        neuron.load_mechanisms(mech)
    parser = cell_parser.CellParser(neuronParam.filename)
    parser.spatialgraph_to_cell()
    nwMap = NetworkMapper(parser.cell, nwParam)
    nwMap.create_functional_realization()