Lateral dendrodenditic inhibition in the Olfactory Bulb (David et al. 2008)

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Accession:116094
Mitral cells, the principal output neurons of the olfactory bulb, receive direct synaptic activation from primary sensory neurons. Shunting inhibitory inputs delivered by granule cell interneurons onto mitral cell lateral dendrites are believed to influence spike timing and underlie coordinated field potential oscillations. Lateral dendritic shunt conductances delayed spiking to a degree dependent on both their electrotonic distance and phase of onset. Recurrent inhibition significantly narrowed the distribution of mitral cell spike times, illustrating a tendency towards coordinated synchronous activity. This result suggests an essential role for early mechanisms of temporal coordination in olfaction. The model was adapted from Davison et al, 2003, but include additional noise mechanisms, long lateral dendrite, and specific synaptic point processes.
Reference:
1 . David F, Linster C, Cleland TA (2008) Lateral dendritic shunt inhibition can regularize mitral cell spike patterning. J Comput Neurosci 25:25-38 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Olfactory bulb;
Cell Type(s): Olfactory bulb main mitral cell; Olfactory bulb main interneuron granule MC cell;
Channel(s): I Na,t; I L high threshold; I A; I K; I K,Ca;
Gap Junctions:
Receptor(s): GabaA; AMPA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Temporal Pattern Generation; Synchronization; Simplified Models; Active Dendrites; Olfaction;
Implementer(s):
Search NeuronDB for information about:  Olfactory bulb main mitral cell; Olfactory bulb main interneuron granule MC cell; GabaA; AMPA; I Na,t; I L high threshold; I A; I K; I K,Ca;
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DendroDendriticInhibition
LongDendrite
cadecay.mod *
kA.mod *
kca.mod *
kfasttab.mod *
kM.mod *
kslowtab.mod *
lcafixed.mod *
nafast.mod *
nmdanet.mod *
shuntInhib.mod *
stim2.mod
bulb.hoc
experiment_fig1.hoc
experiment_fig2ace.hoc
fig1bde.dat
fig1bde.m
fig1bde.ses
fig1fg.m
fig1fg.ses
fig2ace.m
fig2ace.ses
granule.tem
init.hoc
input.hoc
mathslib.hoc *
mitral.tem
mosinit.hoc *
parameters_fig1.hoc
parameters_fig2ace.hoc
tabchannels.dat *
tabchannels.hoc
                            
TITLE Calcium dependent potassium channel
: KCa channel with parameters from US Bhalla and JM Bower,
: J. Neurophysiol. 69:1948-1983 (1993)
: Andrew Davison, The Babraham Institute, 1998.

NEURON {
	SUFFIX kca
	USEION k READ ek WRITE ik
	USEION ca READ cai
	RANGE gkbar, ik, Yconcdep, Yvdep
	GLOBAL Yalpha, Ybeta
}

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

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

PARAMETER {
	v (mV)
	dt (ms)
	gkbar= 0.120 (mho/cm2) <0,1e9>
	ek = -80 (mV)
	Ybeta = 0.05 (/ms)
	cai (mM) := 1e-5 (mM)
}


STATE {
	Y
}

ASSIGNED {
	ik (mA/cm2)
	Yalpha   (/ms)
	Yvdep    
	Yconcdep (/ms)
}

INITIAL {
	rate(v,cai)
	Y = Yalpha/(Yalpha + Ybeta)
}

BREAKPOINT {
	SOLVE state METHOD cnexp
	ik = gkbar*Y*(v - ek)
}

DERIVATIVE state {
	rate(v,cai)
	Y' = Yalpha*(1-Y) - Ybeta*Y
}

PROCEDURE rate(v(mV),cai(mM)) {
	vdep(v)
	concdep(cai)
	Yalpha = Yvdep*Yconcdep
}

PROCEDURE vdep(v(mV)) {
	TABLE Yvdep FROM -100 TO 100 WITH 100
	Yvdep = exp((v*1(/mV)+70)/27)
}

PROCEDURE concdep(cai(mM)) {
	TABLE Yconcdep FROM 0 TO 0.01 WITH 1000
	if (cai < 0.01) {
		Yconcdep = 500(/ms)*( 0.015-cai*1(/mM) )/( exp((0.015-cai*1(/mM))/0.0013) -1 )
	} else {
		Yconcdep = 500(/ms)*0.005/( exp(0.005/0.0013) -1 )
	}
}

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