title: Genetic algorithm for optimization and specification of a neuron model.
abstract: We present a novel approach for neuron model specification using a Genetic Algorithm (GA) to develop simple firing neuron models consisting of a single compartment with one inward and one outward current. The GA not only chooses the model parameters, but also chooses the formulation of the ionic currents (i.e. single-variable, two-variable, instantaneous, or leak). The fitness function of the GA compares the frequency output of the GA generated models to an I-F curve of a nominal Morris-Lecar (ML) model. Initially, several different classes of models compete among the population. Eventually, the GA converges to a population containing only ML-type firing models with an instantaneous inward and single-variable outward current. Simulations where ML-type models are restricted from the population are also investigated. This GA approach allows the exploration of a universe of feasible model classes that is less constrained by model formulation assumptions than traditional parameter estimation approaches. While we use a simple model, this technique is scalable to much larger and more complex formulations.
authors.affiliation: Laboratory for Neuroengineering, Georgia Institute of Technology, Atlanta, GA; School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA.
authors.forename: W
authors.initials: W
authors.lastname: Gerken
authors.forename: L
authors.initials: L
authors.lastname: Purvis
authors.forename: R
authors.initials: R
authors.lastname: Butera