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;
# -*- coding: utf-8 -*-

## USAGE: python2.6 calc_kernel_xcorrs.py

from pylab import *
import pickle
import sys
import math

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

from stimuliConstants import * # has SETTLETIME, PULSE_RUNTIME, pulsebins
from data_utils import * # has axes_labels()

pulsebindt = PULSE_RUNTIME/pulsebins
kernel_size = int(2.0/pulsebindt) # 2.0 seconds kernel size

#listtype = 'firingrate'
listtype = 'non-linear ORNs'

if listtype == 'firingrate':
    ## firingrate, filename
    filelist = [
    (2.0,'../results/odor_pulses/2011_08_19_14_46_odorpulses_SINGLES_JOINTS_PGS_numgloms3.pickle'),
    (3.5,'../results/odor_pulses/2011_08_19_17_15_odorpulses_SINGLES_JOINTS_PGS_numgloms3.pickle'),
    (10,'../results/odor_pulses/2011_08_19_17_19_odorpulses_SINGLES_JOINTS_PGS_numgloms3.pickle')
    ]
elif listtype == 'inhibition':
    ## inhibition, filename
    filelist = [
    (8.0,'../results/odor_pulses/2011_08_20_19_38_odorpulses_SINGLES_JOINTS_PGS_numgloms3.pickle'),
    (10.0,'../results/odor_pulses/2011_08_19_17_19_odorpulses_SINGLES_JOINTS_PGS_numgloms3.pickle')
    ]
elif listtype == 'non-linear ORNs':
    ## ORN frate followed by static non-linearity
    filelist = [
    (2.0,'../results/odor_pulses/2011_08_19_14_46_odorpulses_SINGLES_JOINTS_PGS_numgloms3.pickle'),
    (3.0,'../results/odor_pulses/2011_08_19_21_00_odorpulses_SINGLES_JOINTS_PGS_numgloms3.pickle'),
    (8.0,'../results/odor_pulses/2011_08_19_20_51_odorpulses_SINGLES_JOINTS_PGS_numgloms3.pickle')
    ]


def calc_corrs_chisq(filenamelist):
    corrsR = []
    corrsA = []
    corrsB = []
    chisqs = []
    for i,(frate,filename) in enumerate(filenamelist):
        f = open(filename+'_params','r')
        sigmoid,chisq,params = pickle.load(f)
        f.close()
        kernelR = params[0:kernel_size]
        kernelA = params[kernel_size:2*kernel_size]
        kernelB = params[2*kernel_size:3*kernel_size]
        if i==0:
            kernelR1 = kernelR
            kernelA1 = kernelA
            kernelB1 = kernelB
        else:
            ## find cross-correlation of current kernel with first kernel
            corrsR.append( corrcoef([kernelR1,kernelR],rowvar=1)[0,1] )
            corrsA.append( corrcoef([kernelA1,kernelA],rowvar=1)[0,1] )
            corrsB.append( corrcoef([kernelB1,kernelB],rowvar=1)[0,1] )
        chisqs.append(chisq)
    return corrsR,corrsA,corrsB,chisqs

if __name__ == "__main__":
    
    corrsR,corrsA,corrsB,chisqs = calc_corrs_chisq(filelist)
    frates = [frate for (frate,filename) in filelist]
    #between_frates = [(frate+frates[i+1])/2.0 for i,frate in enumerate(frates[:-1])]

    fig = figure(facecolor='none')
    ax = fig.add_subplot(111)
    plot(frates, chisqs, color=(0,0,1), marker='o', label='chisqs')
    axes_labels(ax,'ORN mean firing rate (Hz)','Chisq')
    ax2 = ax.twinx()
    plot(frates[1:], corrsR, color=(0,0,0), marker='o', label='corrR')
    plot(frates[1:], corrsA, color=(1,0,0), marker='o', label='corrA')
    plot(frates[1:], corrsB, color=(0,1,0), marker='o', label='corrB')
    biglegend('lower left')
    axes_labels(ax2,'ORN mean firing rate (Hz)','Corr')
    title('Chisq/Corrs vs ORN firing rate', fontsize=24)
    xlim(0.0,12.0)

    show()

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