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
snmf
models
morphologies
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 *
drawfits_withmids_combfs.py
drawfits_withmids_combfs_nseg5.py
drawmorphs.py
drawobjective_evolution.py
emoo.py
mytools.py
originalrun.sav
pars_withmids_combfs_1a.sav
pars_withmids_combfs_1a_0a.sav
pars_withmids_combfs_1a_0a_5a.sav
pars_withmids_combfs_1a_0a_5a_5a.sav
pars_withmids_combfs_final.sav *
pars_withmids_combfs_fixed_final.sav
screenshot.png
snmf_protocols.py
snmf_target.py
snmf_withmids_combfs.py
                            
:Comment : LVA ca channel. Note: mtau is an approximation from the plots
:Reference : :		Avery and Johnston 1996, tau from Randall 1997
:Comment: shifted by -10 mv to correct for junction potential
:Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21

NEURON	{
	SUFFIX Ca_LVAst
	USEION ca READ eca WRITE ica
	RANGE gCa_LVAstbar, gCa_LVAst, ica, offma, offmt, offha, offht, sloma, slomt, sloha, sloht, taummin, taumdiff, tauhmin, tauhdiff
}

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

PARAMETER	{
	gCa_LVAstbar = 0.00001 (S/cm2)
	offma = -40.0 (mV)
	offmt = -35.0 (mV)
	offha = -90.0 (mV)
	offht = -50.0 (mV)
	sloma = 6.0 (mV)
	slomt = 5.0 (mV)
	sloha = 6.4 (mV)
	sloht = 7.0 (mV)
	taummin = 5.0 (ms)
	taumdiff = 20.0 (ms)
	tauhmin = 20.0 (ms)
	tauhdiff = 50.0 (ms)
}

ASSIGNED	{
	v	(mV)
	eca	(mV)
	ica	(mA/cm2)
	gCa_LVAst	(S/cm2)
	mInf
	mTau
	hInf
	hTau
}

STATE	{
	m
	h
}

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

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 = 1.0000/(1+ exp((offma-v)/sloma))
		mTau = (taummin + taumdiff/(1+exp(-(offmt-v)/slomt)))/qt
		hInf = 1.0000/(1+ exp(-(offha-v)/sloha))
		hTau = (tauhmin + tauhdiff/(1+exp(-(offht-v)/sloht)))/qt
	UNITSON
}

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