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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reducedhaymodel
single_cell
models
README.html
Ca_HVA.mod *
Ca_LVAst.mod *
CaDynamics_E2.mod *
epsp.mod *
Ih.mod *
Im.mod *
K_Pst.mod *
K_Tst.mod *
Nap_Et2.mod *
NaTa_t.mod *
SK_E2.mod *
SKv3_1.mod *
fullhay_run_1.dat
fullhay_run_2.dat
fullhay_run_3.dat
fullhay_run_3a.dat
mosinit.hoc
run_ctrl_vgraph.ses
runmodel.hoc
runmodel.py
screenshot.png
                            
:Reference : :		Adams et al. 1982 - M-currents and other potassium currents in bullfrog sympathetic neurones
:Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21

NEURON	{
	SUFFIX Im
	USEION k READ ek WRITE ik
	RANGE gImbar, gIm, ik, offma, sloma, tauma, offmb, slomb, taumb
}

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

PARAMETER	{
	gImbar = 0.00001 (S/cm2) 
        offma = -35 (mV)
        sloma = 10 (mV)
        tauma = 303.0303 (ms)
        offmb = -35 (mV)
        slomb = 10 (mV)
        taumb = 303.0303 (ms)
}

ASSIGNED	{
	v	(mV)
	ek	(mV)
	ik	(mA/cm2)
	gIm	(S/cm2)
	mInf
	mTau
	mAlpha
	mBeta
}

STATE	{ 
	m
}

BREAKPOINT	{
	SOLVE states METHOD cnexp
	gIm = gImbar*m
	ik = gIm*(v-ek)
}

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

INITIAL{
	rates()
	m = mInf
}

PROCEDURE rates(){
  LOCAL qt
  qt = 2.3^((34-21)/10)

	UNITSOFF
		mAlpha = exp(-(offma-v)/sloma)/tauma
		mBeta = exp((offmb-v)/slomb)/taumb
		mInf = mAlpha/(mAlpha + mBeta)
		mTau = (1/(mAlpha + mBeta))/qt
	UNITSON
}

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