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]
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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 -*-

### This program plots a synapse's Ik and Gk on an event

synapse_names = ['mitral_granule_NMDA']

# index of the synapse to test in above arrays.
test_synapse_index = 0

import sys,os
sys.path.extend(["..","../synapses"])
from load_synapses import *
from moose_utils import *

# The PYTHONPATH should contain the location of moose.py and _moose.so
# files.  Putting ".." with the assumption that moose.py and _moose.so
# has been generated in ${MOOSE_SOURCE_DIRECTORY}/pymoose/ (as default
# pymoose build does) and this file is located in
# ${MOOSE_SOURCE_DIRECTORY}/pymoose/examples
try:
    import moose
except ImportError:
    print "ERROR: Could not import moose. Please add the directory containing moose.py in your PYTHONPATH"
    import sys
    sys.exit(1)

from pylab import *

SETTLETIME = 0.25 # s
RUNTIME = 1.0+SETTLETIME # s
SIMDT = 5e-6 #s
PLOTDT = 5e-6 #s
                        
if __name__ == "__main__":
    load_synapses(1.0)
    idx = test_synapse_index
    syn_name = synapse_names[idx]
    context = moose.PyMooseBase.getContext()

    soma = moose.Compartment('/soma')
    soma.length = 1e-6 # m
    soma.diameter = 1e-6 # m
    soma.Cm = 0.01 * 3.14159 * soma.length * soma.diameter

    synid = context.deepCopy(context.pathToId('/library/'+syn_name),soma.id,syn_name)
    syn = moose.SynChan(synid)
    #### connect the soma to the synapse
    if syn.getField('mgblock')=='True': # If NMDA synapse based on mgblock, connect to mgblock
        mgblock = moose.Mg_block(syn.path+'/mgblock')
        compartment_connection = mgblock
    else: # if SynChan or even NMDAChan, connect normally
        compartment_connection = syn

    soma.connect("channel", compartment_connection, "channel")
    tt = moose.TimeTable('/soma/tt')
    tt.connect("event", syn,"synapse")
    syn.setWeight(syn.numSynapses-1, 1.0)
    syn.setDelay(syn.numSynapses-1, 0)
    fn = 'temp_spikefile.txt'
    f = open(fn,'w')
    f.write(str(SETTLETIME))
    f.close()
    tt.filename = fn
    os.remove(fn)
    
    somaVm = setupTable('/soma/somaVm', soma, 'Vm')
    chanIk = setupTable('/soma/chanIk', compartment_connection, 'Ik')
    chanGk = setupTable('/soma/chanGk', compartment_connection, 'Gk')

    resetSim(context, SIMDT, PLOTDT)
    context.step(RUNTIME)

    tvec = arange(0.0,RUNTIME+1e-12,PLOTDT)
    figure()
    title('somaVm')
    plot(tvec,somaVm, 'r,')
    figure()
    title('Synapse Ik')
    plot(tvec,chanIk, 'g,')
    figure()
    title('Synapse Gk')
    plot(tvec,chanGk, 'b,')
    
    show()