Realistic barrel cortical column - NetPyNE (Huang et al., 2022)

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Reconstructed rodent barrel cortical column (thalamic filter-and-fire input, L4 and L2/3 spiking neurons) based on measured distributions, so each run will create a different connectivity). Includes 13 types of inhibitory and excitatory neurons, implemented as Izhikevich neurons. Includes both a Matlab and a Python (NetPyNe) implementation.
1 . Huang C, Zeldenrust F, Celikel T (2022) Cortical Representation of Touch in Silico Neuroinformatics [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Spiking neural network;
Brain Region(s)/Organism: Barrel cortex;
Cell Type(s): Abstract Izhikevich neuron; Barrel cortex L2/3 pyramidal cell;
Gap Junctions:
Simulation Environment: NetPyNE;
Model Concept(s): Long-term Synaptic Plasticity; Action Potentials; Synaptic Integration; Synaptic Plasticity; Calcium dynamics; Sensory coding; Spike Frequency Adaptation; Spatial connectivity;
Implementer(s): Zeldenrust, Fleur [fleurzeldenrust at]; Huang, Chao; Celikel, T;
import os

def read_data(model):
    from import  loadmat

    folder = model['Folder']
    data_premodel = model['PreModel']
    data_full = model['FullModel']
    reading_folder  = folder
    datamodel_folder = os.path.join(os.getcwd(),reading_folder)

    # Data about barrel structure (among others)
    filename = data_premodel
    reading_filename = os.path.join(datamodel_folder,filename)
    inst_premodel = loadmat(reading_filename, struct_as_record=False, squeeze_me=True)

    # Structured data about cells and connections
    filename = data_full
    reading_filename = os.path.join(datamodel_folder,filename)
    inst_model = loadmat(reading_filename, struct_as_record=False, squeeze_me=True)

    return inst_premodel, inst_model

def read_input(Input):
    option = Input['Option']
    folder = Input['Folder']
    filename = Input['Filename']

    reading_folder  = folder
    datamodel_folder = os.path.join(os.getcwd(),reading_folder)

    if option == 'Svoboda':
        # Psth and spike trains from Svoboda dataset
        from import  loadmat
        inst_input = loadmat(os.path.join(datamodel_folder,filename), struct_as_record=False, squeeze_me=True)

    elif option == 'Multitrial':
        # Psth data from Aguilar's paper
        import pandas as pd
        inst_input = pd.read_csv(os.path.join(datamodel_folder,filename), sep=" ", header=None)
        print('Input option not available')
    return inst_input