"... 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."
Model Type: Neuron or other electrically excitable cell
Cell Type(s): Vestibular neuron
Currents: I Na,p; I Na,t; I A; I K,Ca
Model Concept(s): Parameter Fitting; Methods
Simulation Environment: NEURON
Implementer(s): Weaver, Christina [christina.weaver at fandm.edu]
References:
Weaver CM, Wearne SL. (2006). The role of action potential shape and parameter constraints in optimization of compartment models Neurocomputing. 69