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
cells
PG_nrn
readme.txt *
hpg.mod *
kamt.mod *
kdrmt.mod *
naxn.mod *
nmdanetOB.mod *
TCa_d.mod *
coeff-table.txt *
forfig3.hoc *
gc.hoc *
Level2.xml
mitral.hoc *
mitral_davison2007_neurml_L1.xml
mod_func.c
mosinit.hoc *
nrnmech.dll
PGsimple_aditya2010_neuroML_L1_L2.xml
PGTest_nrn.py
                            
begintemplate GC

public somagc, dend, synmt, priden, sampa, priden2

create somagc, priden2[3], dend[6], priden

objref synmt[3], sampa[3]

proc init() {

topol()
geometry()
segments()
memb()

}


proc topol() {local i
	connect priden(0), somagc(1)
	connect priden2[0](0), priden(.6)
	connect priden2[1](0), priden(.8)
	connect priden2[2](0), priden(.9)
	connect dend[0](0), dend[1](1)
	connect dend[1](0), priden2[0](0.5)
	connect dend[2](0), dend[3](1)
	connect dend[3](0), priden2[0](0.6)
	connect dend[4](0), dend[5](1)
	connect dend[5](0), priden2[0](0.4)
}

proc segments() {local i
	somagc.nseg= 1
	forsec "priden" nseg = 10
	forsec "dend" nseg = 1
}

proc geometry() {local i
	somagc { L = 8  diam = 8}
	dend[0] { L=1  diam=1}
	dend[2] { L=1  diam=1}
	dend[4] { L=1  diam=1}
	dend[1] { L=2  diam=.2}
	dend[3] { L=2  diam=.2}
	dend[5] { L=2  diam=.2}
	priden {L=150 diam=.5}
	for i=0, 2 {priden2[i] {L=100 diam=.4}}
	define_shape()
}
	


proc memb() {
	forall {
		insert pas 
		insert kamt gbar_kamt=0.008 ek=-90
		insert nax gbar_nax=0.02 sh_nax=15 ena=60
		Ra = 80
		cm = 1
		g_pas = 1/30000
		e_pas = -65
		ek = -90
	}

	forsec "soma" {
		gbar_nax=0.04
       		insert kdrmt gbar_kdrmt=0.006
		gbar_kamt = 0.004
		cm = 4
		g_pas = cm/30000
	}

	forsec "pri" {
		cm = 4
		g_pas = cm/30000
	}

dend[0] {
	synmt[0] = new nmdanet(1) 
	sampa[0] = new ExpSyn(1)
	sampa[0].e=0
	sampa[0].tau=3
	}

dend[2] {
	synmt[1] = new nmdanet(1) 
	sampa[1] = new ExpSyn(1)
	sampa[1].e=0
	sampa[1].tau=3
	}

dend[4] {
	synmt[2] = new nmdanet(1) 
	sampa[2] = new ExpSyn(1)
	sampa[2].e=0
	sampa[2].tau=3
	}
}

endtemplate GC