Olfactory bulb microcircuits model with dual-layer inhibition (Gilra & Bhalla 2015)

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Accession:153574
A detailed network model of the dual-layer dendro-dendritic inhibitory microcircuits in the rat olfactory bulb comprising compartmental mitral, granule and PG cells developed by Aditya Gilra, Upinder S. Bhalla (2015). All cell morphologies and network connections are in NeuroML v1.8.0. PG and granule cell channels and synapses are also in NeuroML v1.8.0. Mitral cell channels and synapses are in native python.
Reference:
1 . Gilra A, Bhalla US (2015) Bulbar microcircuit model predicts connectivity and roles of interneurons in odor coding. PLoS One 10:e0098045 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Olfactory bulb;
Cell Type(s): Olfactory bulb main mitral GLU cell; Olfactory bulb main interneuron periglomerular GABA cell; Olfactory bulb main interneuron granule MC GABA cell;
Channel(s): I A; I h; I K,Ca; I Sodium; I Calcium; I Potassium;
Gap Junctions:
Receptor(s): AMPA; NMDA; Gaba;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: Python; MOOSE/PyMOOSE;
Model Concept(s): Sensory processing; Sensory coding; Markov-type model; Olfaction;
Implementer(s): Bhalla, Upinder S [bhalla at ncbs.res.in]; Gilra, Aditya [aditya_gilra -at- yahoo -period- com];
Search NeuronDB for information about:  Olfactory bulb main mitral GLU cell; Olfactory bulb main interneuron periglomerular GABA cell; Olfactory bulb main interneuron granule MC GABA cell; AMPA; NMDA; Gaba; I A; I h; I K,Ca; I Sodium; I Calcium; I Potassium; Gaba; Glutamate;
#!/usr/bin/env python
# -*- coding: utf-8 -*-

import sys
import pickle
sys.path.extend(["..","../networks","../generators","../simulations"])

from OBNetwork import *
from stimuliConstants import *
from simset_odor import * # has ONLY_TWO_MITS
from sim_utils import *

from pylab import * # part of matplotlib that depends on numpy but not scipy

bindt = 2e-3

def plot_sin_responses(picklefile):
    f = open(picklefile,'r')
    mitral_responses_list = pickle.load(f)
    f.close()
    ## mitral_responses_list[avgnum][sinnum][mitnum][spikenum]

    numbins = int(SIN_RUNTIME/bindt)
    mitral_responses_binned_list = \
        rebin_pulses(mitral_responses_list, numbins, SIN_RUNTIME, 0.0)
    numavgs = len(mitral_responses_list)
    mitral_responses_avg = mean(mitral_responses_binned_list, axis=0)

    sintlist = arange(0.0, SIN_RUNTIME, bindt)
    mitnum = 0
    for sinnum in range(num_sins):
        figure(facecolor='w')
        sincolor = (sinnum+1) / float(num_sins)
        plot(sintlist,mitral_responses_avg[sinnum][mitnum],
            color=(0,1-sincolor,sincolor))

if __name__ == "__main__":
    if len(sys.argv)<2:
        print "You need to specify the whitenoise responses pickle filename."
        sys.exit(1)
    plot_sin_responses(sys.argv[1])
    show()

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