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

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

from OBNetwork import *

from stimuliConstants import * # has SETTLETIME
from simset_inhibition import * # has REALRUNTIME
from sim_utils import * # has setup_tables(), plot_extras() and build_tweaks()
from data_utils import *

SETTLETIME = 500e-3 # increasing to 500ms from 250ms as PG cells spike I think right at the start.
RUNTIME = REALRUNTIME + SETTLETIME

# Whether to inject the current into tuft or soma, typically soma
TUFT_INJECT = False

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

## from node000:
## mpiexec -machinefile ~/hostfile -n 3 ~/Python-2.6.4/bin/python2.6 inhibition_recvslat.py
## or from node059:
## mpiexec -machinefile ~/hostfile_end5 -n 3 ~/Python-2.6.4/bin/python2.6 inhibition_recvslat.py

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

class lateral_granule_inhibition:
    
    def __init__(self):
        pass

    def setup_stim(self,network,rank):
        if SPIKEBLOCK:
            print "Blocking Na in mitral cells only"\
                " (don't have granules, else inconsistent) ..."
            for cell in network.mitralTable.values():
                #blockChannels(cell, ['Na','K2']) # only K2 i.e. Kfast, not all K
                blockChannels(cell, ['Na']) # only TTX, only Na blocked not K2
        iAinject = offInject
        if rank == 1:
            iBinject = 0.0
        else:
            iBinject = onInject
        ipulse_duration = 400e-3 # seconds ## for self and rec inh: Arevian et al
        #ipulse_duration = 125e-3 # seconds ## only for self inh: 40Hz for 125ms -- Friedman & Strowbridge 2000
        ## 1-1200pA for 400ms was used by Arevian et al to generate FvsI curves.
        ## I need to use much larger currents (inhibition folded into mitral model)
        if TUFT_INJECT:
            tuftseg = 'Seg0_glom_81_102'
            print "injecting into tuft segment",tuftseg
            compA = moose.Compartment(network.mitralTable[mitralidx].path+'/'+tuftseg)
            compB = moose.Compartment(network.mitralTable[mitralsidekickidx].path+'/'+tuftseg)
        else:
            compA = network.mitralTable[mitralidx].soma
            compB = network.mitralTable[mitralsidekickidx].soma
        iA = setup_iclamp(compA, '_mitralA', SETTLETIME, ipulse_duration, iAinject)
        ## 1-1200pA for 400ms was used by Arevian et al to generate FvsI curves.
        ## slightly stagger the start of current pulses in the two cells
        ## so that the mitrals do not continuously co-fire.
        iB = setup_iclamp(compB,'_mitralB', SETTLETIME-5e-3, ipulse_duration+5e-3, iBinject)
        ## To check backpropagating AP, check at various distances
        ## from the soma on the lateral dendrites.
        dend_seg = moose.Compartment(network.mitralTable[mitralidx].path+\
            #'/Seg0_sec_dendp4_0_254') # at 43.86 microns from soma
            #'/Seg0_sec_dendd3_0_204') # at 190.56 microns from soma
            '/Seg0_sec_dendd4_2_269') # at 1004.47 microns from soma
        self.mitAdendTable = setupTable('mitdendTable',dend_seg,'Vm')
        dend_seg = moose.Compartment(network.mitralTable[mitralsidekickidx].path+\
            #'/Seg0_sec_dendp4_0_254') # at 43.86 microns from soma
            #'/Seg0_sec_dendd3_0_204') # at 190.56 microns from soma
            '/Seg0_sec_dendd4_2_269') # at 1004.47 microns from soma
        self.mitBdendTable = setupTable('mitdendTable',dend_seg,'Vm')
        print "Glomerulus segment of mitral A = ", network.mitralTable[mitralidx].glom.path
        print "Glomerulus segment of mitral B = ", network.mitralTable[mitralsidekickidx].glom.path
        print 'Injecting mitral A with '+str(iAinject)+' and B with '+str(iBinject)+' at rank '+str(rank)

    def run_inhibition(self,network):
        resetSim(network.context, SIMDT, PLOTDT) # from moose_utils.py sets clocks and resets
        network.context.step(RUNTIME)
        return (array(network.mitralTable[mitralidx]._vmTableSoma),\
            array(network.mitralTable[mitralsidekickidx]._vmTableSoma))

if __name__ == "__main__":
    includeProjections = ['granule_baseline']

    if mpirank == boss:
        mit_responses = []
        for procnum in [1,2]:
            mitral_Vm_both = mpicomm.recv(source=procnum, tag=0)
            mit_responses.append(mitral_Vm_both)

        ## write results to a file
        outfilename = 'data_recvslat.pickle'
        f = open(outfilename,'w')
        pickle.dump(mit_responses, f)
        f.close()
        print "Wrote", outfilename

        timevec = arange(0.0,RUNTIME+1e-10,SIMDT)*1e3
        fig = figure(facecolor='w')
        ax = fig.add_subplot(111)
        title('mitral A',fontsize=30)
        #plot(plotBins(network.mitralTable[str(mitralidx)]._vmTableSoma),'r-,')
        #plot(plotSpikes(network.mitralTable[str(mitralidx)]._vmTableSoma),'r-,')
        ax.plot(timevec,mit_responses[0][0]*1e3,color=(0,0,0),linewidth=2,label='Recurrent')
        ax.plot(timevec,mit_responses[1][0]*1e3,color=(0,0,1),linewidth=2,label='Lateral')
        biglegend()
        axes_labels(ax,'time (ms)','Vm (ms)')

        fig = figure(facecolor='w')
        ax = fig.add_subplot(111)
        title('mitral B',fontsize=30)
        #plot(plotBins(network.mitralTable[str(mitralidx)]._vmTableSoma),'r-,')
        #plot(plotSpikes(network.mitralTable[str(mitralidx)]._vmTableSoma),'r-,')
        ax.plot(timevec,mit_responses[0][1]*1e3,color=(0,0,0),linewidth=2,label='Recurrent')
        ax.plot(timevec,mit_responses[1][1]*1e3,color=(0,0,1),linewidth=2,label='Lateral')
        biglegend()
        axes_labels(ax,'time (ms)','Vm (ms)')
        
        show()
    else:
        ## 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, (mitralidx,mitralsidekickidx) )
        network = OBNetwork(OBNet_file, synchan_activation_correction,\
            tweaks, mpirank, "recvslat", granfilebase, spiketable=False)
        #printNetTree() # from moose_utils.py

        sim =  lateral_granule_inhibition()
        sim.setup_stim(network, mpirank)
        mitral_responses_both = sim.run_inhibition(network)
        mpicomm.send( mitral_responses_both, dest=boss, tag=0 )

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