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;
# -*- coding: utf-8 -*-
# ALL SI UNITS
# milliMolar is same as mol/m^3

## USAGE: nohup python2.6 odor_morphs_repeats.py &> nohup_morphs.out < /dev/null &
## if running multiple of these, change morphs to morphs1, morphs2, etc above,
## change loop lists to result in largely non-overlapping runs or opposite order,
## change the morphproc command far below to have morphs1, morphs2, etc unique str.

import os,sys
import os.path
import pickle
import subprocess
cwd = os.getcwd() # current working directory

from lock_utils import *
from pylab import *

IN_VIVO = True
directed = True
frac_directed = 0.0
NONLINEAR_ORNS = False
NONLINEAR_TYPE = 'P' # P for primary glom non-linear, L for lateral gloms non-linear
## inh_options = [ (no_singles,no_joints,no_lat,no_PGs,varyRMP), ... ]
## in order,below options are: all cells; no lat; no joints;
## varyRMP; varyRMP+nolat; varyRMP+nojoints; no PGs; no singles; only mitrals
#inh_options = [ \
#    (False,False,False,False,False), (False,False,True,False,False), (False,True,False,False,False),\
#    (False,False,False,False,True), (False,False,True,False,True), (False,True,False,False,True), \
#    (False,False,False,True,False), (True,False,False,False,False), (True,True,False,True,False)]
## Varying RMP does not give enough decorr, so don't have lots of varying RMP options.
## in order,below options are: all cells; no lat; no joints;
## varyRMP; no PGs; no singles + no joints; only mitrals
#inh_options = [ \
#    (False,False,False,False,False), (False,False,True,False,False), (False,True,False,False,False),\
#    (False,False,False,False,True), \
#    (False,False,False,True,False), (True,True,False,False,False), (True,True,False,True,False) ]
inh_options = [ (False,False,False,False,False) ]
FORCE_SALIENT = False#True
if FORCE_SALIENT:
    stim_seed_list = [-28]#,-10,-19,-28]#range(-1,-37,-1)#[-1,-2,-3,-4,-5,-6,-7,-8,-9]
else:
    stim_seed_list = [844.0]#arange(750.0,770.0)#[157.0,160.0,190.0,191.0,212.0,441.0]
net_seed_list = [100.0,200.0]#[100.0,200.0,300.0]

print "Starting automated morphs simulations ..."
morphs = []
## important to have largest num_gloms first,
## so that firerates file gets written first time for max num_gloms
## Not necessary: below, I check if an exemplary firefile for the last glom exists, else generate.
for num_gloms in [3]:
    morphs_stimseeds = []
    for stimseed in stim_seed_list:
        morphs_netseeds = []
        ## for the non-salient stimuli, change the network also each time
        if stimseed>0: net_seed_list = [stimseed]
        for netseed in net_seed_list:
            morphs_inh = []
            for inh in inh_options:

                files_locked = True     # files have started to be opened for this iteration
                                        # made False, once simulation is loaded and files closed
                ## a special lock file to keep track of locking,
                ## since python will not import below files if kept locked.
                print "Acquiring Lock for odor morphs."
                sys.stdout.flush()
                #mylock('locksimfile.txt','morphs\n')
                lock_file = portalock_open('locksimfile.txt')
                print "Locked files for odor morphs."
                sys.stdout.flush()

                ## generate the stimuli params file
                gen_file = open('../generators/stimuliConstantsMinimal.py','w')
                gen_file.write('## This file is programmatically generated.\n')
                gen_file.write('\n')
                gen_file.write('## used by generate_firerates.py\n')
                gen_file.write('stim_rate_seednum = '+str(stimseed)+'\n')
                gen_file.write('## used by generate_neuroml.py\n')
                gen_file.write('stim_net_seed = '+str(netseed)+'\n')
                gen_file.write('## distance between 2 mitrals for activity dependent inhibition\n')
                gen_file.write('mit_distance = 0 # microns ## irrelevant for odor responses\n')
                gen_file.write('## use thresholded erf() on ORN firing rate?\n')
                gen_file.write('NONLINEAR_ORNS = '+str(NONLINEAR_ORNS)+'\n')
                gen_file.write('NONLINEAR_TYPE = "'+ NONLINEAR_TYPE \
                    +'" # P for primary glom non-linear, L for lateral gloms non-linear\n')
                gen_file.write('scaledWidth = 0.2 # s # width of scaled pulses\n')
                gen_file.close()

                ## generate the network params file
                net_file = open('../networks/networkConstantsMinimal.py','w')
                net_file.write('## actual number of modelled gloms could be 10 (for odor testing)\n')
                net_file.write('## or 2 (for inhibition testing) decided during neuroml generation.\n')
                net_file.write('## can set number of modelled glom to whatever you like.\n')
                net_file.write('## Randomly half of them will lie on central glom\'s mit0 or mit1.\n')
                net_file.write('## First half will receive odor A. Rest will receive odor B.\n')
                net_file.write('NUM_GLOMS = '+str(num_gloms)+'\n')
                net_file.write('\n')
                net_file.write('## Whether FRAC_DIRECTED of mits_per_syns will be\n')
                net_file.write('## connected between pairs listed in DIRECTED_CONNS.\n')
                net_file.write('## Keep directed True for simulating odors,\n')
                net_file.write('## Even for ADI, choose two connected mitrals.\n')
                net_file.write('directed = '+str(directed)+'\n')
                net_file.write('\n')
                net_file.write('## ensure that FRAC_DIRECTED * num of mitrals directed < 1.\n')
                net_file.write('## For NUM_GLOMS=10, 20mits all connected to mit0, FRAC_DIRECTED < 0.05.\n')
                net_file.write('## Can set FRAC_DIRECTED to 0.0 keeping DIRECTED=True. This will ensure that\n')
                net_file.write('## other mits lat dends are over directed centralmit\'s soma, if PROXIMAL_CONNECTION = True\n')
                net_file.write('frac_directed = '+str(frac_directed))
                net_file.write(' # I think you need to set this to 0.05 to get reasonable phase separation?\n')
                net_file.close()

                ## generate the neuroML netfile, don't if exists
                OBNet_file = '../netfiles/syn_conn_array_10000'
                OBNet_file += '_singlesclubbed100_jointsclubbed1'+\
                    '_numgloms'+str(num_gloms)+'_seed'+str(netseed)
                if directed: OBNet_file += '_directed'+str(frac_directed)+'_proximal'
                OBNet_file += '.xml'
                if not os.path.exists(OBNet_file):
                    print "Generating netfile",OBNet_file
                    gen_command = 'python2.6 '+cwd+'/../generators/generate_neuroML.py'
                    subprocess.check_call(gen_command,shell=True)
                else:
                    print "Netfile",OBNet_file,"already exists."

                ## Generate the odor frates, don't if exists
                stim_frate_filename = '../generators/firerates/firerates_2sgm_'+str(stimseed)
                stim_frate_filename += '.pickle'
                if not os.path.exists(stim_frate_filename):
                    print "Generating firerate file",stim_frate_filename
                    gen_command = 'python2.6 '+cwd+'/../generators/generate_firerates_odors.py NOSHOW'
                    subprocess.check_call(gen_command,shell=True)
                else:
                    print "Firerate file",stim_frate_filename,"already exists."

                ## generate the odor morph firefiles (spiketimes),
                ## don't if an exemplary firefile exists
                stim_firefiles_dirname = '../firefiles/firefiles'+str(stimseed)
                if NONLINEAR_ORNS: stim_firefiles_dirname += '_NL'+NONLINEAR_TYPE
                if not os.path.exists(stim_firefiles_dirname):
                    print "Creating firefiles directory ",stim_firefiles_dirname
                    subprocess.check_call('mkdir '+stim_firefiles_dirname,shell=True)
                if not os.path.exists(stim_firefiles_dirname+\
                        '/firetimes_2sgm_glom_'+str(num_gloms-1)+\
                        '_odor_0.0_0.0_avgnum0.txt'):
                    print "Generating firefiles in",stim_firefiles_dirname
                    gen_command = 'python2.6 '+cwd+'/../generators/generate_firefiles_odors.py NOSHOW'
                    subprocess.check_call(gen_command,shell=True)                
                else:
                    print "Exemplary firefile in",stim_firefiles_dirname,"already exists."
                 
                ## no need to presently generate baseline anew: I instead need to shuffle for trials!
                ## generate baseline firefiles also anew with this stimseed*netseed combo.
                #print "Generating baseline firefiles."
                #gen_command = 'python2.6 '+cwd+'/../generators/generate_firefiles_gran_baseline.py'
                #subprocess.check_call(gen_command,shell=True)
                
                ## generate the odor simulation params file
                simset_file = open('simset_odor_minimal.py','w')
                simset_file.write('## This file is programmatically generated.\n')
                simset_file.write('\n')
                simset_file.write('netseedstr = "'+str(netseed)+'"\n')
                simset_file.write('rateseedstr = "'+str(stimseed)+'"\n')
                simset_file.write('\n')
                simset_file.write('OBNet_file = "'+OBNet_file+'"\n')
                simset_file.write('ORNpathseedstr = "'+stim_firefiles_dirname+'/"\n')
                ## inh = (no_singles,no_joints,no_lat,no_PGs,varyRMP)
                simset_file.write('NO_SINGLES = '+str(inh[0])+'\n')
                simset_file.write('## spine inhibition and singles are self-inh\n')
                simset_file.write('## toggle them on/off together\n')
                simset_file.write('NO_SPINE_INH = NO_SINGLES\n')
                simset_file.write('NO_JOINTS = '+str(inh[1])+'\n')
                simset_file.write('NO_MULTIS = NO_JOINTS\n')
                simset_file.write('NO_PGS = '+str(inh[3])+'\n')
                simset_file.write('NO_LATERAL = '+str(inh[2])+'\n')
                simset_file.write('\n')
                simset_file.write('VARY_MITS_RMP = '+str(inh[4])+'\n')
                simset_file.close()

                ## odor_morphs.py checks if the output files exists
                ## if there is already an output file, it quits.
                morphproc = subprocess.Popen('mpiexec -machinefile ~/hostfile -n 57'\
                    ' ~/Python-2.6.4/bin/python2.6 odor_morphs.py NOSHOW morphs1',\
                    shell=True,stdout=subprocess.PIPE)
                while True:
                    next_line = morphproc.stdout.readline()
                    if not next_line:
                        break
                    sys.stdout.write(next_line)
                    if files_locked and ('Loading' in next_line):
                        ## now that the simulation has loaded,
                        ## unlock files for the other process.
                        ## only if files are locked still,
                        ## else redundant since 'Loading' appears multiple times
                        #myunlock('locksimfile.txt')
                        portalocker.unlock(lock_file)
                        lock_file.close()
                        files_locked = False # files are closed now
                        print "Unlocked files for odor morphs."
                    if 'Wrote' in next_line:
                        morphfilename = next_line.split()[1]
                        morphs_inh.append(morphfilename)
                        break
                print morphproc.communicate()[0]
                ## unlock in case files are locked even after odor_morphs quits.
                if files_locked:
                    #myunlock('locksimfile.txt')
                    portalocker.unlock(lock_file)
                    lock_file.close()
                    print "UnLocked files for odor morphs after quit."
            morphs_netseeds.append(morphs_inh)
        morphs_stimseeds.append(morphs_netseeds)
    morphs.append(morphs_stimseeds)

print morphs
fullfilename = '../results/odor_morphs/morphs_random'
if NONLINEAR_ORNS: fullfilename += 'NL'+NONLINEAR_TYPE
fullfilename += '.pickle'
fullfile = open(fullfilename,'w')
pickle.dump(morphs, fullfile)
fullfile.close()
print "Wrote",fullfilename

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