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;
#!/usr/bin/env python
# -*- coding: utf-8 -*-

import os
import sys
import math
import datetime
import pickle

## python2.6 tuft_nonlinearity.py
## Remove all interneurons in simset_odor

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

from moose_utils import * # imports moose
from data_utils import * # has mpi import and variables also
from OBNetwork import *
from sim_utils import *

from stimuliConstants import * # has SETTLETIME, inputList and pulseList, GLOMS_ODOR, GLOMS_NIL
from simset_odor import * # has REALRUNTIME, NUMBINS
from generate_constrate_files import * # has the firingrates list

RUNTIME = REALRUNTIME + SETTLETIME

from pylab import * # part of matplotlib that depends on numpy but not scipy
from plot_odor_morphs import *

#-----------------------------------------------------------

class constOdorResponse:
    
    def __init__(self):
        self.mpirank = mpirank
        self.context = moose.PyMooseBase.getContext()

    def setupStim(self, network, avgnum):
        ### connect the ORNs
        for projname in network.projectionDict.keys():
            #### Calling attach_spikes() for each projection,
            #### would reconnect files to the same segment multiple times.
            #### But attach_files_uniquely() checks whether timetable.tableSize is zero or not
            #### i.e. files already attached or not.
            ### connect ORNs to mitrals
            if 'ORN_mitral' in projname:
                print "connecting ORN files to mitrals"
                for i,proj in enumerate(network.projectionDict[projname][2]):
                    #get the glomnum from the post path proj[2]
                    mitname = string.split(proj[2],'/')[1] # name of the mitral cell from '/mitrals_2/...'
                    mitnum = int(string.split(mitname,'_')[1]) # mitral number from 'mitrals_2'
                    firingrate = firingrates[mitnum]
                    filebase = '../firefiles/firetimes_constrate_'+str(firingrate)+'.txt'
                    self.attach_files_uniquely(filebase,proj[0],proj[2],avgnum)

    def attach_files_uniquely(self,filebase,synname,postsegpath,avgnum=None):
        ttpath = postsegpath+'/'+synname+'_tt'
        if self.context.exists(ttpath):
            # timetable already created by networkML reader - just wrap it below.
            tt = moose.TimeTable(ttpath) # post_segment_path+'/'+syn_name+'_tt'
        else:
            ## if timetable was not already created by networkML reader,
            ## it means that the synaptic weights must be zero!
            ## (no extra inhibition - only main inhibition)
            ## hence do not attach spikefiles
            return
        if tt.tableSize != 0: return # if files are already attached, do nothing!
        filebase += '_odor_'+str(odorA)+'_'+str(odorB)
        if avgnum is not None: filebase += '_avgnum'+str(avgnum)
        ## attach_spikes() accesses filenumbers to this segment
        ## from 'fileNumbers' field (of the timetable object in MOOSE)
        ## which is created while reading in networkML.
        attach_spikes(filebase, tt, self.mpirank)

    def connect_granule_baselines_to_files(self, network, avgnum):
        for projname in network.projectionDict.keys():
            if 'granule_baseline' in projname:
                for i,proj in enumerate(network.projectionDict[projname][2]):
                    ## just wrap the existing timetable created when loading in neuroml
                    ## it has a field fileNumbers that has which lines to take from the firingfile
                    tt = moose.TimeTable(proj[2]+'/'+proj[0]+'_tt') # post_segment_path+'/'+syn_name+'_tt'
                    if IN_VIVO:
                        if gran_base_resp_tuned:
                            filebase = '../firefiles/firetimes_gran_baseline_'+str(avgnum)
                        else:
                            filebase = '../firefiles/firetimes_gran_baseline_noresp_'+str(avgnum)
                    else:
                        filebase = '../firefiles/firetimes_gran_baseline_invitro_'+str(avgnum)
                    attach_spikes(filebase, tt, self.mpirank)

    def run(self,network, binned):
        print "Resetting MOOSE."
        # from moose_utils.py sets clocks and resets
        resetSim(network.context, SIMDT, PLOTDT)
        print "Running at",self.mpirank
        network.context.step(RUNTIME)
        mitral_responses = []
        mitral_responses_binned = []
        if ONLY_TWO_MITS: num_mits = MIT_SISTERS
        else: num_mits = NUM_GLOMS*MIT_SISTERS
        for mitnum in range(num_mits):
            mitral = network.mitralTable[mitnum]
            ## only the last respiration cycle is taken
            if binned: mitral_responses_binned.append(
                plotBins(mitral._vmTableSoma, NUMBINS, RUNTIME, (NUM_RESPS-1)*RESPIRATION+SETTLETIME) )
            ## need to convert to numpy's array(),
            ## else MOOSE table cannot be pickled for mpi4py send()
            mitral_responses.append(array(mitral._vmTableSoma))
        return (mitral_responses,mitral_responses_binned)

#----------------------------------------------------------------

if __name__ == "__main__":

    sim =  constOdorResponse()
    includeProjections = []
    tweaks = build_tweaks(CLUB_MITRALS, NO_SPINE_INH, NO_SINGLES,\
        NO_JOINTS, NO_MULTIS, NO_PGS, ONLY_TWO_MITS, includeProjections)
    BINNED = True
    ## if not BINNED, save the full mitral Vm-s
    ## and not just their spiketimes by setting spiketable = False below.
    network = OBNetwork(OBNet_file, synchan_activation_correction, tweaks,\
        mpirank=mpirank, invivo=IN_VIVO, spiketable=BINNED)
    #printNetTree() # from moose_utils.py
    ## monitor those interneurons that are connected to mitral indices 0 and 1
    ## save only spiketimes by setting extras_spikes_only=True
    sim.setupStim(network, avgnum=0)
    mitral_responses,mitral_responses_binned = sim.run(network,BINNED)


    figure()
    title('Glomerulus 0')
    if BINNED:
        deltabin = RESPIRATION/NUMBINS
        # Take only the last respiration cycle
        timevec = arange(SETTLETIME+(NUM_RESPS-1)*RESPIRATION+deltabin/2,RUNTIME,deltabin)
        mitral_responses = mitral_responses_binned
    else:
        timevec = arange(0.0,RUNTIME+1e-12,PLOTDT)
    plot(timevec,mitral_responses[0],color=(0.0,1.0,0.0))
    plot(timevec,mitral_responses[1],color=(0.0,1.0,0.5))
    show()

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