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
README.txt
cadecay.mod *
hpg.mod *
kA.mod
kamt.mod *
kca3.mod *
kdrmt.mod *
kfasttab.mod
kslowtab.mod
lcafixed.mod
nafast.mod
naxn.mod *
TCa_d.mod *
kfast_k.inf *
kfast_k.tau *
kfast_n.inf *
kfast_n.tau *
kslow_k.inf *
kslow_k.tau *
kslow_n.inf *
kslow_n.tau *
mit_memb.hoc
NeuronSimulatorChannelTest.py
                            
#!/usr/bin/env python

### 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'] # Bhalla and Bower 1993 mitral cell
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']
]

mechanism_vars_global_or_ranges = ['global','global','global','global','global','global','global','global','global','global']

# index of the mechanism to test in above arrays.
test_mechanism_index = 2

import sys
import math

try:
    from neuron import *
except ImportError:
    print "ERROR: Could not import neuron. Please add the directory containing neuron.py in your PYTHONPATH"
    import sys
    sys.exit(1)

from pylab import *

sys.path.append('../..')
from globalConstants import * # for CELSIUS

h.load_file("stdlib.hoc")
h.load_file("stdrun.hoc") # for h.stop ,etc.
h.load_file("mit_memb.hoc")

VMIN = -100.0 #mV
VMAX = 100.0 # mV
VSTEPS = 400
DT = 0.001 # ms
# USING PHYSIOLOGICAL UNITS AS NEURON USES THEM
TSTOP =  DT*VSTEPS # ms

class ChannelTest:

    def __init__(self, mechanism_name, plotvars):
        h('create soma')
        self.soma = h.soma
        self.soma.insert(mechanism_name)
        print "Inserted mechanism ",mechanism_name,"in soma"
        
        self.vclamp = h.SEClamp(0.5, sec=self.soma) # single electrode clamp in soma
        # Ensure that electrode impedance is 0, thus a perfect voltage clamp!
        # But setting to exactly zero seems not to work (Perhaps divide by zero internally returning NaN-s)
        self.vclamp.rs = 1e-10
        self.vclamp.dur1 = TSTOP
        
        self.vclampV = h.Vector(arange(VMIN,VMAX,(VMAX-VMIN)/VSTEPS)) # mV
        self.vclampV.play(self.vclamp._ref_amp1,DT)
        self.somavecV = h.Vector()
        self.vect = h.Vector()
        self.somavecV.record(self.soma(0.5)._ref_v)
        #self.somavecV.record(self.vclamp._ref_vc)
        self.vect.record(h._ref_t)
        self.vararray = []
        for i,var in enumerate(plotvars):
            #self.vararray.append(h.Vector())
            #self.vararray[-1].record(h.ref(self.soma(0.5).Na_rat_ms.gmax)) # works for gmax but not for minf, etc.
            h('objref vec'+str(i))
            h('vec'+str(i)+' = new Vector()')
            self.vararray.append(eval('h.vec'+str(i)))
            
            # USE ONE OR THE OTHER OPTIONS BELOW:
            if mechanism_vars_global_or_ranges[test_mechanism_index] == 'global':
                # if the var say minf in naxn.mod has been declared global / parameter, it is not accessed as a section / assigned variable but as &minf_Na_rat_ms
                # As long as there is only one compartment having Na_rat_ms, I suppose minf_Na_rat_ms will have the value returned for that compartment's voltage.
                h('vec'+str(i)+'.record(&'+var+'_'+mechanism_name+')')
            else:
                # if the var say minf in naxn.mod has been declared as a range, it is accessed as a section variable i.e. as &soma.minf_Na_rat_ms(0.5)
                h('vec'+str(i)+'.record(&soma.'+var+'_'+mechanism_name+'(0.5))')
            
    def run(self):
        h.celsius = CELSIUS # nrnivmodl ignored my settings in .mod file! default is 6.3! Also ena, etc are also ignored in the .mod file!
        h.finitialize(VMIN) # Set all section voltages to VMIN, so that state variables do not start from say v=-65mV.
        h.frecord_init() # initialize the recording vectors.
        h.tstop = TSTOP
        h.dt = DT
        h.run()
                
if __name__ == "__main__":
    idx = test_mechanism_index
    ct = ChannelTest(mechanism_names[idx],mechanism_vars[idx])
    ct.run()
    plot(ct.vect,ct.somavecV,'+-r')
    title('soma V (mV) vs time (ms)')
    for i,var in enumerate(ct.vararray):
        figure()
        plot(ct.somavecV,var,',-b')
        title('state variable '+mechanism_vars[idx][i]+' of '+mechanism_names[idx]+' vs Voltage (mV)')
    show()