The role of action potential shape and parameter constraints in optimization of compartment models

Abstract

Automated parameter search methods are commonly used to optimize compartment model parameters. An important step in parameter fitting is selecting an objective function that represents key differences between model and experimental data. 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.