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;
//Load Bill Lytton's graphing package
strdef sfile, gfile, bfile
print "Loading ", sfile, ", ", gfile, ", ", bfile

load_file(sfile) // not needed if using emacs package SIMCTRL
load_file(gfile) // basic routines
load_file(bfile) // for trays

// prepare stimuli

// count APs

objref spiketimes, isi, apc, fr, tVec, vs

spiketimes = new Vector() 
isi = new Vector()
apc = new APCount(0.5)

objref tVec, vs
tVec = new Vector()
vs = new Vector()

// manipulate all graphs simultaneously