Microcircuits of L5 thick tufted pyramidal cells (Hay & Segev 2015)

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Accession:156780
"... We simulated detailed conductance-based models of TTCs (Layer 5 thick tufted pyramidal cells) forming recurrent microcircuits that were interconnected as found experimentally; the network was embedded in a realistic background synaptic activity. ... Our findings indicate that dendritic nonlinearities are pivotal in controlling the gain and the computational functions of TTCs microcircuits, which serve as a dominant output source for the neocortex. "
Reference:
1 . Hay E, Segev I (2015) Dendritic Excitability and Gain Control in Recurrent Cortical Microcircuits. Cereb Cortex 25:3561-71 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Dendrite;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex V1 L6 pyramidal corticothalamic cell;
Channel(s): I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I M; I h; I K,Ca; I A, slow;
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA; Glutamate;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Dendritic Action Potentials; Active Dendrites; Detailed Neuronal Models; Laminar Connectivity; Orientation selectivity;
Implementer(s): Hay, Etay [etay.hay at mail.huji.ac.il];
Search NeuronDB for information about:  Neocortex V1 L6 pyramidal corticothalamic cell; GabaA; AMPA; NMDA; Glutamate; I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I M; I h; I K,Ca; I A, slow; Gaba; Glutamate;
/
HaySegev2014
models
readme.txt
Ca_HVA.mod *
Ca_LVAst.mod *
CaDynamics_E2.mod *
epsp.mod *
Ih.mod *
Im.mod *
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K_Tst.mod *
Nap_Et2.mod *
NaTa_t.mod *
NaTs2_t.mod *
ProbAMPANMDA2.mod
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SK_E2.mod *
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cell1.asc *
microcircuit.hoc
                            
:Comment :
:Reference : :		Reuveni, Friedman, Amitai, and Gutnick, J.Neurosci. 1993

NEURON	{
	SUFFIX Ca_HVA
	USEION ca READ eca WRITE ica
	RANGE gCa_HVAbar, gCa_HVA, ica 
}

UNITS	{
	(S) = (siemens)
	(mV) = (millivolt)
	(mA) = (milliamp)
}

PARAMETER	{
	gCa_HVAbar = 0.00001 (S/cm2) 
}

ASSIGNED	{
	v	(mV)
	eca	(mV)
	ica	(mA/cm2)
	gCa_HVA	(S/cm2)
	mInf
	mTau
	mAlpha
	mBeta
	hInf
	hTau
	hAlpha
	hBeta
}

STATE	{ 
	m
	h
}

BREAKPOINT	{
	SOLVE states METHOD cnexp
	gCa_HVA = gCa_HVAbar*m*m*h
	ica = gCa_HVA*(v-eca)
}

DERIVATIVE states	{
	rates()
	m' = (mInf-m)/mTau
	h' = (hInf-h)/hTau
}

INITIAL{
	rates()
	m = mInf
	h = hInf
}

PROCEDURE rates(){
	UNITSOFF
        if((v == -27) ){        
            v = v+0.0001
        }
		mAlpha =  (0.055*(-27-v))/(exp((-27-v)/3.8) - 1)        
		mBeta  =  (0.94*exp((-75-v)/17))
		mInf = mAlpha/(mAlpha + mBeta)
		mTau = 1/(mAlpha + mBeta)
		hAlpha =  (0.000457*exp((-13-v)/50))
		hBeta  =  (0.0065/(exp((-v-15)/28)+1))
		hInf = hAlpha/(hAlpha + hBeta)
		hTau = 1/(hAlpha + hBeta)
	UNITSON
}

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