A set of reduced models of layer 5 pyramidal neurons (Bahl et al. 2012)

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These are the NEURON files for 10 different models of a reduced L5 pyramidal neuron. The parameters were obtained by automatically fitting the models to experimental data using a multi objective evolutionary search strategy. Details on the algorithm can be found at http://www.g-node.org/emoo and in Bahl et al. (2012).
1 . Bahl A, Stemmler MB, Herz AV, Roth A (2012) Automated optimization of a reduced layer 5 pyramidal cell model based on experimental data. J Neurosci Methods 210:22-34 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Dendrite;
Brain Region(s)/Organism:
Cell Type(s): Neocortex U1 L5B pyramidal pyramidal tract GLU cell;
Channel(s): I Na,p; I Na,t; I K; I M; I h; I K,Ca; I Calcium; I A, slow;
Gap Junctions:
Simulation Environment: NEURON;
Model Concept(s): Action Potential Initiation; Parameter Fitting; Simplified Models; Active Dendrites; Detailed Neuronal Models; Action Potentials; Methods; Calcium dynamics;
Implementer(s): Bahl, Armin [bahl at neuro.mpg.de];
Search NeuronDB for information about:  Neocortex U1 L5B pyramidal pyramidal tract GLU cell; I Na,p; I Na,t; I K; I M; I h; I K,Ca; I Calcium; I A, slow;

celsius = 37


// Define The Sections for Each Conductance

objref nat_list
objref kfast_list
objref kslow_list
objref ih_list

nat_list = new SectionList()
kslow_list = new SectionList()
kfast_list = new SectionList()
ih_list = new SectionList()

basal			ih_list.append()
apical			ih_list.append()
tuft			ih_list.append()

soma			nat_list.append()
hillock			nat_list.append()
iseg			nat_list.append()
apical			nat_list.append()
tuft 			nat_list.append()

soma			kfast_list.append()
apical			kfast_list.append()
tuft			kfast_list.append()

soma			kslow_list.append()
apical			kslow_list.append()
tuft			kslow_list.append()

// Insert the Channels in the Correct Section and Set the Reversal Potentials

forall {
	insert pas
	g_pas = 1./15000
	e_pas = -70
	cm = 1.0

forsec ih_list {
	insert ih
	ehd_ih = -47

forsec nat_list {
	insert nat
	ena = 55
	vshift_nat = 10

forsec kfast_list {
	insert kfast
	ek = -80

forsec kslow_list {
	insert kslow
	ek = -80

tuft {
	insert cad
	insert sca
	insert kca

	eca = 140

soma insert nap
soma insert km

Rm_axosomatic = 15000
spinefactor = 2.0

decay_kfast = 50.0
decay_kslow = 50.0

proc recalculate_passive_properties() {
	forsec axosomatic_list g_pas = 1./Rm_axosomatic
	forsec apicaltree_list g_pas = soma.g_pas*spinefactor 
	forsec apicaltree_list cm = soma.cm*spinefactor

proc recalculate_channel_densities() {
	// See Keren et al. 2009
	soma distance()
	forsec apicaltree_list {
	 for(x) gbar_kfast(x) = soma.gbar_kfast(0.5) * exp(-distance(x)/decay_kfast)
	 for(x) gbar_kslow(x) = soma.gbar_kslow(0.5) * exp(-distance(x)/decay_kslow)
	tuft mih = gbar_ih/distance(0)
	tuft mnat = (gbar_nat-soma.gbar_nat(0.5))/distance(0)
	apical for(x) gbar_nat(x) = mnat*distance(x) + soma.gbar_nat(0.5)
	apical for(x) gbar_ih(x) = mih*distance(x)


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