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]
Citations  Citation Browser
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;
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olfactory-bulb-gilra-bhalla
channels
neuron_channels
CaHVA_Chan.xml
CaL_Chan.xml
CaLChannel.py
CaPool.py
CaTChannel.py
channelConstants.py
granuleDefaults.py
Ih_cb.xml
KAChannel.py
KAChannelMS.py
KCaA.dat
KCaA_PG.dat
KCaB.dat
KCaB_PG.dat
KCaChannel.py
KCaChannel_PG.py
KCaMPIChannel.py
KCaMPIChannel_PG.py
KDRChannelMS.py
kfast_k.inf *
kfast_k.tau *
kfast_n.inf *
kfast_n.tau *
KFastChannel.py
KMChannel.py
kslow_k.inf *
kslow_k.tau *
kslow_n.inf *
kslow_n.tau *
KSlowChannel.py
load_channels.py
MOOSEChannelTest.py
NaChannel.py
NaGranChannel.py
NaMitChannelMS.py
tabchannels.dat *
TCa_d.xml
                            
#!/usr/bin/env python
# -*- coding: utf-8 -*-

### This program plots a channel's state variables / hinf, htau etc. as a function of voltage.

mechanism_names = [
'Na_rat_ms','KDR_ms','KA_ms', # migliore and shepherd
'TCa_d','Ih_cb', # PG cell of Cleland and Sethupathy uses channels from various places
'Na_mit_usb','K2_mit_usb','K_mit_usb','LCa3_mit_usb','KA_bsg_yka']
#'Gran_CaHVA_98','cal'] # extra channels from neuroConstruct examples
mechanism_vars = [
['minf','mtau','hinf','htau'],
['minf','mtau'],
['minf','mtau','hinf','htau'],
['minf','mtau','hinf','htau'],
['linf','ltau'],
['minf','mtau','hinf','htau'],
['ninf','ntau','kinf','ktau'],
['ninf','ntau','kinf','ktau'],
['sinf','stau','rinf','rtau'],
['pinf','ptau','qinf','qtau']
#['minf','mtau','hinf','htau'],
#['minf','mtau']
]

# index of the mechanism to test in above arrays.
test_mechanism_index = 8

import sys
import math

# 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)

sys.path.append('..')
from mooseConstants import *

from load_channels import *

from pylab import *
                        
if __name__ == "__main__":
    load_channels()
    idx = test_mechanism_index
    for varidx in range(len(mechanism_vars[idx])/2): # loop over each inf and tau i.e. two elements of mechanism_vars[idx] at a time
        var = ['x','y','z'][varidx]
        gate = moose.HHGate('/library/'+mechanism_names[idx]+'/'+var+'Gate')
        VMIN = gate.A.xmin
        VMAX = gate.A.xmax
        NDIVS = gate.A.xdivs # will use same VMIN, VMAX and NDIVS for A and B tables.
        dv = (VMAX-VMIN)/NDIVS
        vrange = [VMIN+i*dv for i in range(NDIVS+1)]
        figure()
        plot(vrange,[gate.A[i]/gate.B[i] for i in range(NDIVS+1)],'b-,') # Assume A and B have corresponding number of entries
        title('state variable '+mechanism_vars[idx][2*varidx]+' of '+mechanism_names[idx]+' vs Voltage (V)')
        figure()
        plot(vrange,[1.0/gate.B[i] for i in range(NDIVS+1)],'b-,')
        title('state variable '+mechanism_vars[idx][2*varidx+1]+' of '+mechanism_names[idx]+' vs Voltage (V)')
    show()