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 test_corrs.py

from pylab import *
import pickle
import sys
import math

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

from stimuliConstants import * # has SETTLETIME
from data_utils import *
from simset_odor import* # has REALRUNTIME

f = open('../generators/firerates_2sgm_'+str(rate_seednum)+'.pickle','r')
frateOdorList,fratePulseList,randomPulseList,randomPulseStepsList,randomResponseList,kernels\
    = pickle.load(f)
f.close()

RUNTIME = REALRUNTIME + SETTLETIME
firingtsteps = arange(0,RUNTIME+1e-10,FIRINGFILLDT)# include the last RUNTIME point also.
numt = len(firingtsteps)

def calc_corrs():

    ## frateOdorList[glomnum][odornum]
    ## ORN firing rates are very low - increase.
    
    ## compare increases in firing rate
    #frate1 = 0.5*50*frateOdorList[0][1] # glom0 0.8*odorB+0.2*odorA
    #frate2 = 50*frateOdorList[1][0] # glom1 odorB
    #frate3 = 0.5*3*frate1
    
    ## compare wide (v1) vs narrow (v2) vs phase shift (v3)
    frate1 = 50*frateOdorList[0][0] # glom0 odorB
    frate2 = 200*frateOdorList[1][5] # glom1 odorA
    frate3 = 50*frateOdorList[8][0] # glom8 odorB

    peakfrate = max(max(frate1),max(frate2),max(frate3))*2.0
    v1 = []
    v2 = []
    v3 = []
    for i in range(10):
        v1.append(poissonTrainVaryingRate(RUNTIME,peakfrate,REFRACTORY,firingtsteps,frate1))
        v2.append(poissonTrainVaryingRate(RUNTIME,peakfrate,REFRACTORY,firingtsteps,frate2))
        v3.append(poissonTrainVaryingRate(RUNTIME,peakfrate,REFRACTORY,firingtsteps,frate3))

    ## pairs of vectors varnames between which to calculate xcorrgrams.
    ## CHANGE THIS FOR DIFFERENT PAIRS:
    #corrgram_pairs = (('v1','v1'),('v1','v2'),('v1','v3'))
    corrgram_pairs = (('v3','v3'),('v3','v2'),('v3','v1'))
    #corrgram_pairs = (('v1','v3'),('v3','v1'))
    #corrgram_pairs = (('v1','v1'),('v3','v3'))

    starttime = REALRUNTIME+SETTLETIME-2*RESPIRATION
    endtime = REALRUNTIME+SETTLETIME
    T = endtime-starttime
    ## Dhawale et al 2010: 5 ms time bin, T=0.5s.
    ## I have T=1.0s as rat resp is 1.0s whereas mouse is 0.5s.
    ## refractory period in my poisson generator is 1ms, so have that as the bin size:
    ## must ensure that there are never more than one spike per bin per moving window.
    dt = 1e-3
    tcorrlist = arange(-T/4.0,T/4.0+1e-6,dt)

    colorlist = ['r','g','b','c','m','y','k']
    labellist = [va+' '+vb for (va,vb) in corrgram_pairs]
    
    ## calc xcorrgrams for each type of normalization.
    for (norm_str,title_str) in (('none','no norm'),('overall','integral norm'),\
            ('ref','ref norm'),('analogous','standard norm')):
        xcorrgrams = []
        for (va_str,vb_str) in corrgram_pairs:
            ## lookup the variable value given the variable name
            ## don't use eval() as it can evaluate any python expression
            ## hackable if user enters a string
            va = locals()[va_str]
            vb = locals()[vb_str]
            xcorrgrams.append(crosscorrgram( va, vb, dt, T/4.0, starttime, endtime, norm_str ))
        plot_corrs(tcorrlist,xcorrgrams,title_str,colorlist,labellist)

    return (v1,v2,v3)

def plot_corrs(tcorrlist,xcorrgrams,titlestr,colorlist,labellist):
    fig = figure(facecolor='none')
    ax = fig.add_subplot(111)
    for xcgnum,xcorrgram in enumerate(xcorrgrams):
        plot(tcorrlist, xcorrgram, color=colorlist[xcgnum], label=labellist[xcgnum])
    biglegend()
    axes_labels(ax,'time shift (s)','')
    title(titlestr+' sisters xcorrelogram', fontsize=24)

if __name__ == "__main__":
    rasters = calc_corrs()
    plot_rasters(rasters, RUNTIME)
    show()


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