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
                            
: Dynamics that track inside calcium concentration
: modified from Destexhe et al. 1994

NEURON	{
	SUFFIX CaDynamics_E2
	USEION ca READ ica WRITE cai
	RANGE decay, gamma, minCai, depth
}

UNITS	{
	(mV) = (millivolt)
	(mA) = (milliamp)
	FARADAY = (faraday) (coulombs)
	(molar) = (1/liter)
	(mM) = (millimolar)
	(um)	= (micron)
}

PARAMETER	{
	gamma = 0.05 : percent of free calcium (not buffered)
	decay = 80 (ms) : rate of removal of calcium
	depth = 0.1 (um) : depth of shell
	minCai = 1e-4 (mM)
}

ASSIGNED	{ica (mA/cm2)}

STATE	{
	cai (mM)
	}

BREAKPOINT	{ SOLVE states METHOD cnexp }

DERIVATIVE states	{
	cai' = -(10000.0)*(ica*gamma/(2*FARADAY*depth)) - (cai - minCai)/decay
}

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