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 string, os
import moose
from moose.utils import *

## mpi_uniqueid could be for example: 'pulses'+str(mpirank)
def attach_spikes(filebase, timetable, mpi_uniqueid):
    ## read the file that contains all the ORN firing times for this glom, odor and avgnum
    filehandle = open(filebase+'.txt','r')
    spiketimelists = filehandle.readlines()
    filehandle.close()

    filenums = string.split(timetable.getField('fileNumbers'),'_')
    ## Merge all the filenums into a temp file, load it and delete it.
    spiketimes = []
    for filenum in filenums: # loop through file numbers
        timesstr = spiketimelists[int(filenum)]
        if timesstr != '\n':
            timestrlist = string.split(timesstr,' ')
            ## convert to float for sorting else '10.0'<'6.0'
            spiketimes.extend([float(timestr) for timestr in timestrlist])
    spiketimes.sort()
    ## ensure that different processes do not write to the same file by using mpi_uniqueid
    ## mpi_uniqueid could be for example: 'pulses'+str(mpirank)
    fn = os.getenv('HOME')+'/tempspikes_'+str(mpi_uniqueid)+'.txt'
    filehandle = open(fn,'w')
    filehandle.write('\n'.join([str(spiketime) for spiketime in spiketimes]))
    filehandle.close()

    ############# OB model specific hack to give all ORN inputs to tuft-base compartment
    ### tt_path = postcomp.path+'/'+syn_name_full+glomstr+'_tt' ## glomstr is '' for us
    #tt_split = timetable.path.split('/')
    #if tt_split[-1]=='ORN_mitral_tt':
    #    tt_path = string.join(tt_split[:-2],'/')+'/Seg0_glom_1_22/ORN_mitral_tt_'+tt_split[-2] # unique timetable
    #    ## Choose one of the below two
    #    #syn_path = string.join(tt_split[:-2],'/')+'/Seg0_glom_1_22/ORN_mitral' # connect new tt to glom[1] synapse
    #    syn_path = string.join(tt_split[:-1],'/')+'/ORN_mitral' # connect new tt to original synapse
    #    syn = moose.SynChan(syn_path) # wrapping created synapse
    #    tt = moose.TimeTable(tt_path) # new timetable
    #    # Be careful to connect the timetable only once while creating it as below:
    #    tt.connect("event", syn, "synapse")
    #    print "Connecting",timetable.path,"to",syn_path,"via",tt_path
    #    timetable = tt

    timetable.filename = fn
    os.remove(fn)