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

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Accession:146026
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).
Reference:
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]
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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:
Receptor(s):
Gene(s):
Transmitter(s):
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;
load_file("stdrun.hoc")
load_file("../reduced_model.hoc")

forall e_pas = -84.998432
Rm_axosomatic = 15158.600592
forsec axosomatic_list cm = 2.708710
spinefactor = 0.500131
soma gbar_nat = 295.471939
soma gbar_kfast = 43.229568
soma gbar_kslow = 630.252495
soma gbar_nap = 3.524026
soma gbar_km = 11.911267
basal gbar_ih = 12.871974
tuft gbar_ih = 23.929821
tuft gbar_nat = 45.917053
decay_kfast = 65.606365
decay_kslow = 34.325297
hillock gbar_nat = 9451.076984
iseg gbar_nat = 17193.545673
iseg vshift2_nat = -8.920295

Ra_apical =  4.45008826e+02 
apical Ra = Ra_apical
tuft gbar_sca = 4.86650161e-01
tuft vshift_sca = 7.95615680e-01
tuft gbar_kca = 9.68514833e+00

recalculate_passive_properties()
recalculate_channel_densities()