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:
## from node000:
## mpiexec -machinefile ~/hostfile -n <num_sines*numtrials+1> ~/Python-2.6.4/bin/python2.6 generate_firefiles_sinusoids.py
## mpiexec -machinefile ~/hostfile -n 201 ~/Python-2.6.4/bin/python2.6 generate_firefiles_sinusoids.py
## typical value for num_sines = 5
## typical value for num of trials = 40
## (depends on number of available processing nodes and number of odorfiles generated)

# time points for the firing rate which is read from a pickled file
sinepulsetime = arange(0,SIN_RUNTIME,FIRINGFILLDT)
len_pulsetime = len(sinepulsetime)
num_sins = len(sine_frequencies)

fn = 'firerates/firerates_sinusoids_seed'+str(stim_rate_seednum)+\
    '_ampl'+str(sine_amplitude)+'_mean'+str(sine_ORN_mean)+'.pickle'
print "Loading rate file:",fn
f = open(fn,'r')
frateResponseList = pickle.load(f)
f.close()

def sinusoid_stimuli(fnum,trialnum):
    ## mitral and PG odor ORNs firing files
    for glomnum in range(NUM_GLOMS):
        frate = frateResponseList[glomnum][fnum]
        mitralfirefilename = '../firefiles/firefiles_sins/firetimes_sin_glom'\
            +str(glomnum)+'_fnum'+str(fnum)+'_trial'+str(trialnum)
        ornstimvector_merged = write_odor_files(NUM_ORN_FILES_PER_GLOM, frate,\
            mitralfirefilename, SIN_RUNTIME, sinepulsetime)
    return True
    
if __name__ == "__main__":
    if mpirank == boss: # boss collates
        numavgs = (mpisize-1)/num_sins
        for avgnum in range(numavgs):
            for fnum in range(num_sins):
                procnum = avgnum*num_sins + fnum + 1
                print 'waiting for process '+str(procnum)+'.'
                ## below: you get a numpy array of 
                ## rows=NUM_GLOMS*MIT_SISTERS and cols=spike times
                ## mitral responses has spike times
                ## we calculate STA to get kernel from spike times.
                ok = mpicomm.recv(source=procnum, tag=0)
                print 'received from process '+str(procnum)+'.'
    else:
        seed([500.0*mpirank]) ##### Seed numpy's random number generator.
        trialnum = (mpirank-1)/num_sins
        fnum = (mpirank-1)%num_sins
        ok = sinusoid_stimuli(fnum,trialnum)
        mpicomm.send( ok, dest=boss, tag=0 )