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]
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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 L5/6 pyramidal GLU 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 L5/6 pyramidal GLU 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;
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HaySegev2014
models
readme.txt
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K_Tst.mod *
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microcircuit.hoc
                            
:Reference :Colbert and Pan 2002

NEURON	{
	SUFFIX NaTa_t
	USEION na READ ena WRITE ina
	RANGE gNaTa_tbar, gNaTa_t, ina
}

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

PARAMETER	{
	gNaTa_tbar = 0.00001 (S/cm2)
}

ASSIGNED	{
	v	(mV)
	ena	(mV)
	ina	(mA/cm2)
	gNaTa_t	(S/cm2)
	mInf
	mTau
	mAlpha
	mBeta
	hInf
	hTau
	hAlpha
	hBeta
}

STATE	{
	m
	h
}

BREAKPOINT	{
	SOLVE states METHOD cnexp
	gNaTa_t = gNaTa_tbar*m*m*m*h
	ina = gNaTa_t*(v-ena)
}

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

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

PROCEDURE rates(){
  LOCAL qt
  qt = 2.3^((34-21)/10)
	
  UNITSOFF
    if(v == -38){
    	v = v+0.0001
    }
		mAlpha = (0.182 * (v- -38))/(1-(exp(-(v- -38)/6)))
		mBeta  = (0.124 * (-v -38))/(1-(exp(-(-v -38)/6)))
		mTau = (1/(mAlpha + mBeta))/qt
		mInf = mAlpha/(mAlpha + mBeta)

    if(v == -66){
      v = v + 0.0001
    }

		hAlpha = (-0.015 * (v- -66))/(1-(exp((v- -66)/6)))
		hBeta  = (-0.015 * (-v -66))/(1-(exp((-v -66)/6)))
		hTau = (1/(hAlpha + hBeta))/qt
		hInf = hAlpha/(hAlpha + hBeta)
	UNITSON
}