Layer V pyramidal cell model with reduced morphology (Mäki-Marttunen et al 2018)

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Accession:187474
" ... In this work, we develop and apply an automated, stepwise method for fitting a neuron model to data with fine spatial resolution, such as that achievable with voltage sensitive dyes (VSDs) and Ca2+ imaging. ... We apply our method to simulated data from layer 5 pyramidal cells (L5PCs) and construct a model with reduced neuronal morphology. We connect the reduced-morphology neurons into a network and validate against simulated data from a high-resolution L5PC network model. ..."
References:
1 . Hay E, Hill S, Schürmann F, Markram H, Segev I (2011) Models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active properties. PLoS Comput Biol 7:e1002107 [PubMed]
2 . Hay E, Segev I (2015) Dendritic Excitability and Gain Control in Recurrent Cortical Microcircuits. Cereb Cortex 25:3561-71 [PubMed]
3 . Mäki-Marttunen T, Halnes G, Devor A, Metzner C, Dale AM, Andreassen OA, Einevoll GT (2018) A stepwise neuron model fitting procedure designed for recordings with high spatial resolution: Application to layer 5 pyramidal cells. J Neurosci Methods 293:264-283 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
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 Calcium; I A, slow;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; NEURON (web link to model); Python; NeuroML;
Model Concept(s):
Implementer(s): Maki-Marttunen, Tuomo [tuomo.maki-marttunen at tut.fi]; Metzner, Christoph [c.metzner at herts.ac.uk];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I M; I h; I K,Ca; I Calcium; I A, slow;
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:Comment : mtau deduced from text (said to be 6 times faster than for NaTa)
:Comment : so I used the equations from NaT and multiplied by 6
:Reference : Modeled according to kinetics derived from Magistretti & Alonso 1999
:Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21

NEURON	{
	SUFFIX Nap_Et2
	USEION na READ ena WRITE ina
	RANGE gNap_Et2bar, gNap_Et2, ina, offm, slom, offma, offmb, sloma, slomb, tauma, taumb, taummax, offh, sloh, offha, offhb, sloha, slohb, tauha, tauhb, tauhmax
}

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

PARAMETER	{
	gNap_Et2bar = 0.00001 (S/cm2)
	offm = -52.6 (mV)
	slom = 4.6 (mV)
	offma = -38 (mV)
	offmb = -38 (mV)
	sloma = 6.0 (mV)
	slomb = 6.0 (mV)
	tauma = 5.49451
	taumb = 8.06452
	taummax = 6.0 (ms)
	offh = -48.8 (mV)
	sloh = 10.0 (mV)
	offha = -17 (mV)
	offhb = -64.4 (mV)
	sloha = 4.63 (mV)
	slohb = 2.63 (mV)
	tauha = 347222.2
	tauhb = 144092.2
	tauhmax = 1.0 (ms)
}

ASSIGNED	{
	v	(mV)
	ena	(mV)
	ina	(mA/cm2)
	gNap_Et2	(S/cm2)
	mInf
	mTau
	mAlpha
	mBeta
	hInf
	hTau
	hAlpha
	hBeta
}

STATE	{
	m
	h
}

BREAKPOINT	{
	SOLVE states METHOD cnexp
	gNap_Et2 = gNap_Et2bar*m*m*m*h
	ina = gNap_Et2*(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
	mInf = 1.0/(1+exp((offm-v)/slom))
        if(v == offma){
    	    v = v+0.0001
        }
        if(v == offmb){
    	    v = v+0.0001
        }
	mAlpha = -(offma-v)/(1-(exp((offma-v)/sloma)))/tauma
	mBeta  = (offmb-v)/(1-(exp(-(offmb-v)/slomb)))/taumb
	mTau = taummax*(1/(mAlpha + mBeta))/qt

  	if(v == offha){
   	    v = v + 0.0001
  	}
        if(v == offhb){
            v = v+0.0001
        }

	hInf = 1.0/(1+exp(-(offh-v)/sloh))
        hAlpha = (offha-v) / (1 - exp(-(offha-v)/sloha))/tauha
        hBeta = -(offhb-v) / (1 - exp((offhb-v)/slohb))/tauhb
	hTau = tauhmax*(1/(hAlpha + hBeta))/qt
	UNITSON
}

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