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]
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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;
import sys, pickle

from generate_firefiles_odors import *

## generate firefiles i.e. list of spike times
## from the firerates computed by generate_firerates_....py
## USAGE: python2.6 generate_firefiles_variedinhibition.py

RUNTIME = REALRUNTIME + SETTLETIME

# binning to plot odor responses
bindt = (RUNTIME-SETTLETIME)/respbins
tlist = arange(SETTLETIME+bindt/2.0,RUNTIME,bindt)
pulsebindt = PULSE_RUNTIME/pulsebins
pulsetlist = arange(pulsebindt/2.0,PULSE_RUNTIME,pulsebindt)

# time points for the firing rate which is read from a pickled file
firingtsteps = arange(0,RUNTIME+1e-10,FIRINGFILLDT)# include the last RUNTIME point also.
numt = len(firingtsteps)
extratime = arange(0,2*RESPIRATION,FIRINGFILLDT)
pulsetsteps = arange(0,PULSE_RUNTIME+1e-10,FIRINGFILLDT)
numtpulse = len(pulsetsteps)

f = open('firerates/firerates_2sgm_variedinh.pickle','r')
frateOdorList,kernels = pickle.load(f)
f.close()

def variedinh_stimuli():
    ## mitral and PG odor ORNs firing files
    ## Seed numpy's random number generator.
    ## If no parameter is given, it uses current system time
    seed([700.0])
    for glomnum in range(NUM_GLOMS):
        figure()
        title(str(glomnum))
        ylabel('Hz')
        xlabel('time (s)')
        totinh = float(len(frateOdorList[glomnum]))
        for inhidx,frate in enumerate(frateOdorList[glomnum]):
            for avgnum in range(MAXNUMAVG):
                mitralfirefilename = '../firefiles/firefiles_variedinh/firetimes_2sgm_glom_'\
                    +str(glomnum)+'_inhnum'+str(inhidx)+'_avgnum'+str(avgnum)
                ornstimvector_merged = write_odor_files(NUM_ORN_FILES_PER_GLOM, frate,\
                    mitralfirefilename, RUNTIME, firingtsteps)
            ## plotBins here returns firing rate of NUM_ORN_FILES_PER_GLOM combined.
            ## So divide by NUM_ORN_FILES_PER_GLOM.
            ## We just plot ornstimvector_merged for the last avgnum 
            ratebins = [rate/NUM_ORN_FILES_PER_GLOM for rate in plotBins(ornstimvector_merged,\
                respbins, RUNTIME, SETTLETIME)]
            plot(tlist, ratebins, color=(inhidx/totinh,1-inhidx/totinh,0), marker=',')

if __name__ == "__main__":
    ## seeds are set in each of the below functions:
    ## so you may comment / uncomment below functions depending on what you want to generate.
    variedinh_stimuli()
    show()