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 pickle
import datetime

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

from OBNetwork import *

from stimuliConstants import * # has SETTLETIME
from simset_activinhibition import * # has REALRUNTIME
from sim_utils import * # has build_tweaks(), and print_extras_activity()
from data_utils import *

########## You need to run:
## From any node on the gj cluster:
## python2.6 activdep_inhibition_mitspikeinit.py [SAVEFIG]

SPIKERUNTIME = 50e-3
RUNTIME = SPIKERUNTIME + SETTLETIME

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

## 33 for first soma, then tuft; 35 onwards for first tuft, then soma
## Weak shock -- 33 mit, 4 PG; Transition shock -- 35 mit, 4 PG; Strong shock -- 66 mit, 8 PG.
#num_tuft_comps_shock_mit = 33
##num_tuft_comps_shock_mit = 40
num_tuft_comps_shock_mit = 66
#num_tuft_comps_shock_pg = 4 # keep this a multiple of 2, distributed on 2 PG dendrites
num_tuft_comps_shock_pg = 8 # keep this a multiple of 2, distributed on 2 PG dendrites
PLOT_EXTRAS = True

## set lateral_mitnum to 1 for 2MITS / 2 for 2GLOMS option set in generate_neuroML.py
## Note that for directed connectivity, mit 3 is used for directed conn to mit 0 in generate_neuroml.py,
## thus mit 2 represents a non-directed conn cell.
## If you want to show asymm inhibition between directed cells, you should use mit 3 below.
lateral_mitnum = 2#3
if REVERSED_ADI:
    mitralmainidx = lateral_mitnum
    mitralsidekickidx = 0
else:
    mitralmainidx = 0
    mitralsidekickidx = lateral_mitnum

from mpi4py import MPI

mpicomm = MPI.COMM_WORLD
mpisize = mpicomm.Get_size() # Total number of processes
mpirank = mpicomm.Get_rank() # Number of my process
mpiname = MPI.Get_processor_name() # Name of my node
# The 0th process is the boss who collates/receives all data from workers
boss = 0
print 'Process '+str(mpirank)+' on '+mpiname+'.'

if PLOT_EXTRAS and mpisize>11:
    print "You want to plot Vm-s of mitrals, singles and granules for",mpisize,"processes."
    print "To avoid you the embarrassment of lots of figures, I'm aborting."
    sys.exit(1)


def onespike2synapse(network,compartmentobj,synName,synNum):
    ## connectSynapse is in moose.utils in MOOSE beta 1.4 -- deepcopies a new synapse
    ## so, if synapse exists, don't try to deepcopy over it again.
    if moose.context.exists(compartmentobj.path+'/'+synName):
        synapse = moose.SynChan(compartmentobj.path+'/'+synName)
    else:
        synapse = connectSynapse(network.context, compartmentobj, synName, 1.0)
    ## put in i also, because randomly a synapse might get connected twice.
    spiketable = moose.TimeTable(synapse.path+'/tt_'+str(synNum))
    #### SynChan's synapse MsgDest takes time as its argument.
    ## Thus spiketable should contain a list of spike times.
    spiketable.connect("event", synapse,"synapse")
    ## Presently only method 4 i.e. loading from file is supported for TimeTable.
    ## Hence the need to write a single value in a file.
    fn = 'temp_spikefile_mitspikeinit.txt'
    f = open(fn,'w')
    f.write(str(SETTLETIME+uniform(0,4e-3))) ## 4ms jitter in nerve shock
    f.close()
    spiketable.filename = fn
    os.remove(fn)


def setup_stim(network):
    ## network.populationDict = { 'populationname1':(cellname,{instanceid1:moosecell, ... }) , ... }
    ## network.projectionDict = { 'projectionname1':(source,target,[(syn_name1,pre_seg_path,post_seg_path),...]) , ... }

    ## We want a single sharp current injection at SETTLETIME, so cannot use soma.inject
    
    ## Only connect to num_tuft_comps_shock_mit number of tuft compartments
    mitcell = network.mitralTable[mitralmainidx] ## mitralTable is same as populationDict['mitrals'][1]
    for segi,seginfo in enumerate((network.cellSegmentDict['mitral'].values())[0:num_tuft_comps_shock_mit]):
        ## segment info - see MorphML_reader.py
        ## cellSegmentDict = { segid1 : [ segname,(proximalx,proximaly,proximalz),
        ##    (distalx,distaly,distalz),diameter,length,[potential_syn1, ... ] ] , ... }
        ## seginfo[5] is a list of potential synapses at this segment
        if 'ORN_mitral' in seginfo[5]:
            ## wrap this segment, seginfo[0] is segment name
            tuftcomp = moose.Compartment(mitcell.path+'/'+seginfo[0])
            onespike2synapse(network,tuftcomp,'ORN_mitral',segi)
            ### compartment, name_extn, start_time, duration, current (all SI)
            #setup_iclamp(tuftcomp, '_nerveshock'+str(segi),\
            #    SETTLETIME, 1e-3, tuft_shock_inject)
    
    ## Only connect to num_tuft_comps_shock_pg number of PG compartments
    for pgcell in network.populationDict['PGs'][1].values():
        for segi,seginfo in enumerate((network.cellSegmentDict['PG'].values())): # 3 compartments
            ## cellSegmentDict = { segid1 : [??, [ segname,(proximalx,proximaly,proximalz),
            ##    (distalx,distaly,distalz),diameter,length,[potential_syn1, ... ] ] ] , ... }
            ## seginfo[5] is a list of potential synapses at this segment
            if 'ORN_PG' in seginfo[5]: # 2 compartments
                for i in range(num_tuft_comps_shock_pg/2):
                    ## wrap this segment, seginfo[0] is segment name
                    tuftcomp = moose.Compartment(pgcell.path+'/'+seginfo[0])
                    onespike2synapse(network,tuftcomp,'ORN_PG',i*2+segi)
                    ### compartment, name_extn, start_time, duration, current (all SI)
                    #setup_iclamp(tuftcomp, '_nerveshock'+str(i*2+segi),\
                    #    SETTLETIME, 1e-3, tuft_shock_inject)
                    
    print "Set up simultaneous synapses on mainidx mitral and each PG:",\
        num_tuft_comps_shock_mit,num_tuft_comps_shock_pg


def run_inhibition(network, tables):
    resetSim(network.context, SIMDT, PLOTDT) # from moose_utils.py sets clocks and resets
    network.context.step(RUNTIME)
    ## get mitral A's firing rate


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

if __name__ == "__main__":
    uniquestr = 'spikeinit_'
    ## 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.
    ## no need for 'PG' below as 'ORN_PG' and 'SA_PG' are not needed,
    ## and 'PG_mitral', 'mitral_PG' connections to/from mits0&1 are kept automatically.
    includeProjections = ['granule_baseline']
    tweaks = build_tweaks( CLUB_MITRALS, NO_SPINE_INH,\
        NO_SINGLES, NO_JOINTS, NO_MULTIS, NO_PGS, ONLY_TWO_MITS,\
        includeProjections, (mitralmainidx,mitralsidekickidx) )
    ## send mpirank to put in ORN filenames / gran baseline temp files
    ## so they do not clash between mpi processes
    ## also, unique str, so that temp files of morphs, pulses, etc do not overlap
    network = OBNetwork(OBNet_file, synchan_activation_correction,\
        tweaks, mpirank, uniquestr, granfilebase, spiketable=SPIKETABLE)
    #printNetTree() # from moose_utils.py

    ## setup a separate Vm table, since the one setup by OBNetwork() above stores only spikes.
    mitAsoma_Vm = setupTable('mitAsomaVm',network.mitralTable[mitralmainidx].soma,'Vm')

    ## monitor Vm at the base of the tuft
    tuftbase_seg = moose.Compartment(network.mitralTable[mitralmainidx].path+\
        #'/Seg0_tuftden_19_23') # for migliore and shepherd 2007 cell
        '/Seg0_prim_dend_5_20') # tuft base
    tuftBaseTable_Vm = setupTable('mitdendTable',tuftbase_seg,'Vm')

    ## monitor Vm in the tuft compartment
    tuftcomp = moose.Compartment(network.mitralTable[mitralmainidx].path+'/Seg0_glom_81_102')
    tuftTable_Vm = setupTable('mitTuftTable',tuftcomp,'Vm')

    setup_stim(network)
    SPIKETABLE = False
    ## if not SPIKETABLE: record the Vm-s of a few interneurons
    ## else: record spiketimes of all interneurons
    tables = setupTables(network, NO_PGS, NO_SINGLES, NO_JOINTS, NO_MULTIS,
        args={'mitrals':(mitralmainidx,)}, spikes=SPIKETABLE)
    run_inhibition(network, tables)

    timevec_unbinned = arange(0.0,RUNTIME+1e-12,PLOTDT)
    ## Paper figure: mitA's soma, tuft base and tuft
    fig2 = figure(figsize=(columnwidth/2.0,linfig_height/2.0),dpi=1200,facecolor='w') # 'none' is transparent
    ax = fig2.add_subplot(111)
    plot(timevec_unbinned*1000,array(mitAsoma_Vm)*1000,'-r', label='soma',\
        linewidth=plot_linewidth, linestyle='-') # ms and mV
    plot(timevec_unbinned*1000,array(tuftBaseTable_Vm)*1000,'-b', label='base',\
        linewidth=plot_linewidth, dashes=(0.5,0.5)) # ms and mV
    plot(timevec_unbinned*1000,array(tuftTable_Vm)*1000,'-k', label='tuft',\
        linewidth=plot_linewidth, dashes=(1.5,0.5)) # ms and mV
    beautify_plot(ax,x0min=False,drawxaxis=True,drawyaxis=True,\
        xticks=[SETTLETIME*1000,SETTLETIME*1000+20],yticks=[-75,0,40])
    #add_scalebar(ax,matchx=False,matchy=False,hidex=True,hidey=False,\
    #    sizex=50,labelx='50 ms',sizey=20,labely='20 mV',label_fontsize=5,\
    #    bbox_to_anchor=[0.7,0.6],bbox_transform=ax.transAxes)
    ax.set_xlim(SETTLETIME*1000,SETTLETIME*1000+20)
    ax.set_xticklabels(['0','20'])
    ax.set_ylim(-75,40)
    axes_labels(ax,"time (ms)","Vm (mV)",fontsize=label_fontsize,xpad=-3,ypad=-4)
    if 'SAVEFIG' in sys.argv:
        fig2.tight_layout()
        fig2.savefig('../figures/connectivity/cells/mitral_spikeinit_'+\
            str(num_tuft_comps_shock_mit)+'ORNmits.png',dpi=fig2.dpi)
        fig2.savefig('../figures/connectivity/cells/mitral_spikeinit_'+\
            str(num_tuft_comps_shock_mit)+'ORNmits.svg',dpi=fig2.dpi)
        ## Paper figure done

    if PLOT_EXTRAS:
        timevec = arange(0.0,RUNTIME+2*PLOTDT+1e-12,PLOTDT)
        titlestr = ''
        #figure()
        #title('red:mitA, green:mitB, '+titlestr)
        #plot(timevec, mitA, 'r,')
        #plot(timevec, mitB, 'g,')
        plot_extras(timevec, tables, NO_PGS, NO_SINGLES, NO_JOINTS, NO_MULTIS, titlestr)
    else:
        spikestables = \
            print_extras_activity(tables, NO_PGS, NO_SINGLES, NO_JOINTS, NO_MULTIS, '')
            
    show()

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