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 = -83.681797
Rm_axosomatic = 21298.371768
forsec axosomatic_list cm = 2.371953
spinefactor = 0.548509
soma gbar_nat = 447.254408
soma gbar_kfast = 43.784070
soma gbar_kslow = 190.331780
soma gbar_nap = 0.852072
soma gbar_km = 11.049652
basal gbar_ih = 3.121003
tuft gbar_ih = 40.673635
tuft gbar_nat = 0.408584
decay_kfast = 82.070143
decay_kslow = 65.182920
hillock gbar_nat = 8171.378905
iseg gbar_nat = 19582.667257
iseg vshift2_nat = -5.357797

Ra_apical = 332.92
apical Ra = Ra_apical
tuft gbar_sca = 2.81
tuft vshift_sca = 2.35
tuft gbar_kca = 9.55

recalculate_passive_properties()
recalculate_channel_densities()