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
 
## from node000:
## mpiexec -machinefile ~/hostfile -n <numtrains*numtrials+1> ~/Python-2.6.4/bin/python2.6 odor_whitenoise.py <simtype>
## nohup mpiexec -machinefile ~/hostfile -n 251 ~/Python-2.6.4/bin/python2.6 odor_whitenoise.py -1 < /dev/null &
## typical value for numtrains = 250
## typical value for num of trials = 1
## (depends on number of available processing nodes and number of odorfiles generated)

## Need to set the type of simulation i.e. whether for mainmitral kernel (simtype = -1)
## or simtype = 0..6 index of the varied_mainrate, etc. variable in stimuliConstants.py

## See varied_mainrate, varied_distance, varied_gran_baseline in stimuliConstants.py after
## setting the option of 'varied' to one of ('mainrate', 'distance', 'gran_baseline') )
## Set various option like NO_PGs or ONLY_TWO_MITS in simset_odor.py

## single simulation USAGE:
## python2.6 odor_whitenoise.py -1

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, varied...
from simset_odor import *

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

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

class odorResponse:
    
    def __init__(self, simtype):
        self.mpirank = mpirank
        self.context = moose.PyMooseBase.getContext()
        self.simtype = int(simtype)

    def setupStim(self,network,trainnum,trialnum):
        self.setupOdor(network, trainnum, trialnum)        
        print "Setup trainnum =",trainnum,"at",self.mpirank

    def setupOdor(self, network, trainnum, trailnum):
        ## first figure out which PG belongs to which glom
        ## PG_glom_map[pgname] returns the glom num of the PG:
        ## needed for ORN to PG connections.
        PG_glom_map = {}
        for projname in network.projectionDict.keys():
            if 'PG_mitral' in projname:
                for i,proj in enumerate(network.projectionDict[projname][2]):
                    ## get the glomnum from the post path proj[2]
                    ## name of the mitral cell from '/mitrals_2/...'
                    mitname = string.split(proj[2],'/')[1]
                    ## glomerulus number from 'mitrals_2' by integer division i.e. 2/2 = 1
                    glomnum = int(string.split(mitname,'_')[1]) / 2
                    ## name of the PG cell from '/PGs_2/...'
                    pgname = string.split(proj[1],'/')[1]
                    PG_glom_map[pgname] = glomnum
        ## Now 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]
                    ## name of the mitral cell from '/mitrals_2/...'
                    mitname = string.split(proj[2],'/')[1]
                    ## glomerulus number from 'mitrals_2' by integer division i.e. 2/2 = 1
                    glomnum = int(string.split(mitname,'_')[1]) / 2
                    self.attach_appropriate_files_to_glom(proj[0],proj[2],trainnum,trialnum,glomnum)
            ## connect ORNs to PG
            if 'ORN_PG' in projname:
                print "connecting ORN files to PGs"
                for i,proj in enumerate(network.projectionDict[projname][2]):
                    pgname = string.split(proj[2],'/')[1] # name of the PG cell from '/PGs_2/...'
                    glomnum = PG_glom_map[pgname]
                    self.attach_appropriate_files_to_glom(proj[0],proj[2],trainnum,trialnum,glomnum)

    def attach_appropriate_files_to_glom(self,proj1,proj2,trainnum,trialnum,glomnum):
        ## for the excitatory kernel
        if self.simtype == -1:
            ## only connect glom0 to noise train to get exc kernel
            if glomnum == central_glom:
                filebase = ORNpathINHstr+'firetimes_whitenoise_glom'+str(glomnum)
                self.attach_files_uniquely(filebase,proj1,proj2,trainnum,trialnum)
        ## for inhibitory kernel (simtype != -1)
        else:
            ## connect glom0 to a constant firing rate
            if glomnum == central_glom:
                if varied == 'mainrate':
                    firingrate = varied_mainrate[self.simtype]
                    filebase = ORNpathINHstr+'firetimes_constrate'\
                        +str(firingrate)+'_avg'+str(trainnum)
                    self.attach_files_uniquely(filebase,proj1,proj2)                                
            ## connect other glom to noise train to get inh kernel
            else:
                filebase = ORNpathINHstr+'firetimes_whitenoise_glom'+str(glomnum)
                self.attach_files_uniquely(filebase,proj1,proj2,trainnum,trialnum)

    def attach_files_uniquely(self,filebase,synname,postsegpath,trainnum=None,trialnum=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!
        if trainnum is not None: filebase += '_train'+str(trainnum)
        if trialnum is not None: filebase += '_trial'+str(trialnum)
        ## 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 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(PULSE_RUNTIME)
        mitral_responses = []
        if ONLY_TWO_MITS: mits = [mitralidx, mitralsidekickidx]
        else: mits = range(NUM_GLOMS*MIT_SISTERS)
        ## network.mitralTable is a dictionary.
        for mitnum in mits:
            mitral = network.mitralTable[mitnum]
            ## 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

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

if __name__ == "__main__":
    ## simtype == '-1': main mitral kernel
    ## other simtype gives the index of varied... variable
    ## to set the conditions for inh kernel due to nearby glomerulus.
    simtype = sys.argv[1]

    #### if only one process is called, plot one sim directly
    if mpisize == 1:
        #### run the slave processes
        sim = odorResponse(simtype)
        ## includeProjections gets used only if ONLY_TWO_MITS is True:
        ## Keep below projections to 'second order cells'
        ## i.e. to cells (granules) connected to mits0&1.
        ## The connections between second order cell
        ## and mits0&1 are automatically retained of course.
        # 'PG' includes 'ORN_PG', 'PG_mitral', 'mitral_PG' and 'SA_PG'
        includeProjections = ['PG','granule_baseline']
        tweaks = build_tweaks(CLUB_MITRALS, NO_SPINE_INH, NO_SINGLES, NO_JOINTS,\
            NO_MULTIS, NO_PGS, ONLY_TWO_MITS, includeProjections, mitralsidekickidx)
        network = OBNetwork(OBNet_file, synchan_activation_correction, tweaks,\
            mpirank, granfilebase+'_noresp', spiketable=False)
        #printNetTree() # from moose_utils.py

        trialnum = 0
        trainnum = 0
        sim.setupStim(network, trainnum, trialnum)
        mitral_responses = sim.run(network, binned=False)
        timevec = arange(SETTLETIME,SETTLETIME+PULSE_RUNTIME+3*PLOTDT/2.0,PLOTDT)
        plot(timevec, mitral_responses[0])
        show()

    #### multiple processes
    else:
        if mpirank == boss:
            #### collate at boss process
            ## mitral_responses_list[avgnum][trainnum][mitnum][spikenum]
            mitral_responses_list = []
            numavgs = (mpisize-1)/NUMWHITETRAINS
            for avgnum in range(numavgs):
                response_set = []
                for trainnum in range(NUMWHITETRAINS):
                    procnum = avgnum*NUMWHITETRAINS + trainnum + 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.
                    mitral_responses = mpicomm.recv(source=procnum, tag=0)
                    response_set.append( mitral_responses )
                mitral_responses_list.append(response_set)
            
            # write results to a file
            today = datetime.date.today()
            if NO_SINGLES: singles_str = '_NOSINGLES'
            else: singles_str = '_SINGLES'
            if NO_JOINTS: joints_str = '_NOJOINTS'
            else: joints_str = '_JOINTS'
            if NO_PGS: pgs_str = '_NOPGS'
            else: pgs_str = '_PGS'
            now =  datetime.datetime.now().strftime("%Y_%m_%d_%H_%M")
            outfilename = '../results/odor_whitenoise/'+now+'_whitenoise'+singles_str+\
                joints_str+pgs_str+'_numgloms'+str(NUM_GLOMS)+'_simtype'+simtype+'.pickle'
            f = open(outfilename,'w')
            pickle.dump(mitral_responses_list, f)
            f.close()
            print "Wrote", outfilename

        else:
            #### run the slave processes
            sim = odorResponse(simtype)
            ## includeProjections gets used only if ONLY_TWO_MITS is True:
            ## Keep below projections to 'second order cells'
            ## i.e. to cells (granules) connected to mits0&1.
            ## The connections between second order cell
            ## and mits0&1 are automatically retained of course.
            # 'PG' includes 'ORN_PG', 'PG_mitral', 'mitral_PG' and 'SA_PG'
            includeProjections = ['PG','granule_baseline']
            tweaks = build_tweaks(CLUB_MITRALS, NO_SPINE_INH, NO_SINGLES, NO_JOINTS,\
                NO_MULTIS, NO_PGS, ONLY_TWO_MITS, includeProjections, mitralsidekickidx)
            network = OBNetwork(OBNet_file, synchan_activation_correction, tweaks,\
                mpirank, granfilebase+'_noresp', spiketable=True)
            #printNetTree() # from moose_utils.py

            trialnum = (mpirank-1)/NUMWHITETRAINS
            trainnum = (mpirank-1)%NUMWHITETRAINS
            sim.setupStim(network, trainnum, trialnum)
            mitral_responses = sim.run(network, binned=True)
            mpicomm.send( mitral_responses, dest=boss, tag=0 )
            print 'sent from process',mpirank

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