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 inhibition_tuftinput_repeats.py &> nohup_tuft.out < /dev/null &
## if running multiple of these, change tuft to tuft1, tuft2, etc above,
## change loop lists to result in largely non-overlapping runs or opposite order,
## change the ADIproc command to have tuft1, tuft2, etc unique str.

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

from lock_utils import *

IN_VIVO = True
directed = True
reverse_list = [False] # lat B inhibits central A (False), vs A inhibits B (True)
frac_directed = 0.01#0.03#0.05 # for activity dependent inhibition, only geometric connectivity
## PROXIMAL_CONNECTION is not set by below proximal in networkConstantsMinimal.py,
## set it manually in networkConstants.py too + related USE_SECDEND_DECAY, strongsynfactorexclateral:
## -- see my labnote of 6th Aug, 2013.
proximal = True
NONLINEAR_ORNS = False
## UPDATE: I'm using odorinh=False to just give half the input when odorinh=True.
## Hence, for this half-comparison, ensure that odor bgnd to granules is same as air bgnd.
ODORINH = True#False # if ODORINH, use odor granule bgnd and 10Hz ORN to mitB, else air bgnd (35Hz) and 5Hz.
if ODORINH: mitB_freq = 10.0 # Hz # odor ORN input to tuft of mitB to cause lat inh
else: mitB_freq = 5.0#3.0 # Hz # air ORN input to tuft of mitB to cause lat inh
ASYM_TEST = False#True # Here unlike activdep inh, ASYM_TEST puts same ORN frate into B as A, rather than const.

mit_distance_list = [50.0,200.0,400.0,600.0,800.0,1000.0,1200.0,1400.0,1600.0,1800.0,1850.0,1900.0]
netseeds = [100.0,200.0,300.0,400.0,500.0,600.0,700.0,800.0,900.0,1000.0]

## inh_options = [ (no_singles,no_joints,no_PGs), ... ]
inh_options = [ (False,False,False) ]

ADI = []
for reverse in reverse_list:
    ADIdists = []
    for mit_distance in mit_distance_list:
        ADIfilenames = []
        for netseed in netseeds:
            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 portalocker didn't work properly with multiple files
                print "Acquiring Lock for ADI."
                sys.stdout.flush()
                #mylock('locksimfile.txt','ADI\n')
                lock_file = portalock_open('locksimfile.txt')
                print "Locked files for ADI."
                sys.stdout.flush()

                gen_file = open('../generators/stimuliConstantsMinimal.py','w') # blank file created
                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 = 1000.0#441.0#212.0#191.0\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 = '+str(mit_distance)+' # microns\n')
                gen_file.write('## use thresholded erf() on ORN firing rate?\n')
                gen_file.write('NONLINEAR_ORNS = '+str(NONLINEAR_ORNS)+'\n')
                gen_file.write('scaledWidth = 0.2 # s # width of scaled pulses\n')
                gen_file.close()

                net_file = open('../networks/networkConstantsMinimal.py','w') # blank file created    
                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 = 2\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()

                OBNet_file = '../netfiles/syn_conn_array_10000_singlesclubbed100_jointsclubbed1'\
                    '_numgloms2_seed'+str(netseed)+"_mitdist"+str(mit_distance)
                if directed:
                    OBNet_file += '_directed'+str(frac_directed)
                    if proximal: OBNet_file += '_proximal'
                    else: OBNet_file += '_distal'
                OBNet_file += '_2GLOMS'
                if not IN_VIVO: OBNet_file += '_INVITRO.xml'
                else: OBNet_file += '.xml'
                if not os.path.exists(OBNet_file):
                    print "Generating netfile",OBNet_file
                    gen_command = 'python2.6 '+cwd+'/../generators/generate_neuroML.py 2GLOMS'
                    if not IN_VIVO:
                        gen_command += ' INVITRO'
                    subprocess.check_call(gen_command,shell=True)
                else:
                    print "Netfile",OBNet_file,"already exists."

                simset_file = open('simset_activinhibition_minimal.py','w') # blank file created
                simset_file.write('## This file is programmatically generated.\n')
                simset_file.write('\n')
                simset_file.write('netseedstr = "'+str(netseed)+'"\n')
                simset_file.write('mitdistance = '+str(mit_distance)+' # microns\n')
                simset_file.write('mitdistancestr = "_mitdist'+str(mit_distance)+'" # microns\n')
                simset_file.write('\n')
                ## inh = (no_singles,no_joints,no_PGs)
                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[2])+'\n')
                simset_file.write('\n')
                simset_file.write('## When testing ADI (ASYM_TEST = False),'\
                    ' fixed current in mitB to generate 80Hz. 1mM Mg++.\n')
                simset_file.write('## When testing asymmetry in inhibition (ASYM_TEST=True),'\
                    ' same currents in mitA and mitB, and 0.2mM Mg++.\n')
                simset_file.write('ASYM_TEST = '+str(ASYM_TEST)+'\n')
                simset_file.write('## reverse roles of mitA and mitB in activity dependent inhibition\n')
                simset_file.write('REVERSED_ADI = '+str(reverse)+'\n')
                simset_file.write('IN_VIVO = '+str(IN_VIVO)+'\n')
                simset_file.write('## tuftinput: if ODORINH, use higher inputs to ORNs, higher gran bgnd not used.\n')
                simset_file.write('ODORINH = '+str(ODORINH)+'\n')
                simset_file.write('oninject_ext = '+str(mitB_freq)+' # Hz \n')
                simset_file.close()

                ## activdep_inhibition.py checks if the output files exists
                ## if there is already an output file, it quits.
                ## NOSHOW is for not showing plots, adi/adi2 is uniquestr
                ## for running multiple parallel activdep_inhibition_repeats.py.
                ADIproc = subprocess.Popen('mpiexec -machinefile ~/hostfile -n 41'\
                    ' ~/Python-2.6.4/bin/python2.6 inhibition_tuftinput.py NOSHOW tuft1',\
                    shell=True,stdout=subprocess.PIPE)
                while True:
                    next_line = ADIproc.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 ADI."
                    if 'Wrote' in next_line:
                        ADIfilename = next_line.split()[1]
                        ADIfilenames.append(ADIfilename)
                        break
                print ADIproc.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 ADI after quit."
        ADIdists.append(ADIfilenames)
    ADI.append(ADIdists)

print ADI
if IN_VIVO: invivo_str = '_invivo'
else: invivo_str = ''
if ODORINH: odorinh_str = '_odorinh'
else: odorinh_str = '_airinh'
fullfilename = '../results/tuftADI/tuftADI'+invivo_str+odorinh_str+'.pickle'
fullfile = open(fullfilename,'w')
pickle.dump(ADI, fullfile)
fullfile.close()
print "Wrote",fullfilename

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