Genetic Algorithm for Optimization and Specification of a Neuron Model

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