Olfactory bulb microcircuits model with dual-layer inhibition (Gilra & Bhalla 2015)

 Download zip file 
Help downloading and running models
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;
/
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
                            
TITLE LCa calcium channel with fixed reversal potential
: LCa channel with parameters from US Bhalla and JM Bower,
: J. Neurophysiol. 69:1948-1983 (1993)
: Adapted from /usr/local/neuron/demo/release/nachan.mod - squid
: by Andrew Davison, The Babraham Institute.
: 25-08-98

NEURON {
	SUFFIX LCa3_mit_usb
	USEION ca WRITE ica
	RANGE gcabar, ica
	GLOBAL sinf, rinf, stau, rtau
}

UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
	(molar) = (1/liter)
	(mM) = (millimolar)
}


INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}

CONSTANT { eca = 70 (mV) }

PARAMETER {
	v (mV)
	dt (ms)
	gcabar	= 0.120 (mho/cm2) <0,1e9>
:	eca = 70 (mV)
}

STATE {
	r s
}

ASSIGNED {
	ica (mA/cm2)
	sinf
	rinf
	stau (ms)
	rtau (ms)
}

INITIAL {
	rates(v)
	s = sinf
	r = rinf
}

BREAKPOINT {
	SOLVE states METHOD cnexp
	ica = gcabar*s*r*(v - eca)
}

DERIVATIVE states {
	rates(v)
	s' = (sinf - s)/stau
	r' = (rinf - r)/rtau
}

FUNCTION alp(v(mV),i) (/ms) {
	if (i==0) {
		alp = 7.5(/ms)/(1 + exp((-v *1(/mV) + 13)/7))
	}else if (i==1){
		alp = 0.0068(/ms)/(1 + exp((v *1(/mV) + 30)/12))
	}
}

FUNCTION bet(v(mV),i)(/ms) {
	if (i==0) {
		bet = 1.65(/ms)/(1 + exp((v *1(/mV) - 14)/4))
	}else if (i==1){
		bet = 0.06(/ms)/(1 + exp(-v* 1(/mV)/11))
	}
}

PROCEDURE rates(v(mV)) {LOCAL a, b
	TABLE sinf, rinf, stau, rtau FROM -100 TO 100 WITH 200
	a = alp(v,0)  b=bet(v,0)
	stau = 1/(a + b)
	sinf = a/(a + b)
	a = alp(v,1)  b=bet(v,1)
	rtau = 1/(a + b)
	rinf = a/(a + b)
}


Loading data, please wait...