Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
The NEURON simulation environment
Neural Computation
Efficient fitting of conductance-based model neurons from somatic current clamp
Journal of Computational Neuroscience
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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.