AP shape and parameter constraints in optimization of compartment models (Weaver and Wearne 2006)

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"... We construct an objective function that includes both time-aligned action potential shape error and errors in firing rate and firing regularity. We then implement a variant of simulated annealing that introduces a recentering algorithm to handle infeasible points outside the boundary constraints. We show how our objective function captures essential features of neuronal firing patterns, and why our boundary management technique is superior to previous approaches."
1 . Weaver CM, Wearne SL (2006) The role of action potential shape and parameter constraints in optimization of compartment models Neurocomputing 69:1053-1057
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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;
Brain Region(s)/Organism:
Cell Type(s): Vestibular neuron;
Channel(s): I Na,p; I Na,t; I A; I K,Ca;
Gap Junctions:
Simulation Environment: NEURON;
Model Concept(s): Parameter Fitting; Methods;
Implementer(s): Weaver, Christina [christina.weaver at fandm.edu];
Search NeuronDB for information about:  I Na,p; I Na,t; I A; I K,Ca;
objectvar save_window_, rvp_
objectvar scene_vector_[3]
objectvar ocbox_, ocbox_list_, scene_, scene_list_
{ocbox_list_ = new List()  scene_list_ = new List()}

//Begin MulRunFitter[0]
load_file("mulfit.hoc", "MulRunFitter")
ocbox_ = new MulRunFitter(1)
ranfac = 2
fspec = new File("MRFdemos.ses.ft1")
fdat = new File("MRFdemos.ses.fd1")
ocbox_.map("MulRunFitter[0]", 565, 278, 368.64, 256.32)
objref ocbox_
//End MulRunFitter[0]

objectvar scene_vector_[1]