Genetic algorithm for optimization and specification of a neuron model

  • Authors:

  • W. C. Gerken;L. K. Purvis;R. J. Butera

  • Affiliations:

  • Laboratory for Neuroengineering, Georgia Institute of Technology, Atlanta, GA, USA and School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA;Laboratory for Neuroengineering, Georgia Institute of Technology, Atlanta, GA, USA and School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA;Laboratory for Neuroengineering, Georgia Institute of Technology, Atlanta, GA, USA and School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA and Wallace ...

  • Venue:
  • Neurocomputing
  • Year:
  • 2006

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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-state variable, two-state 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 within the population. Eventually, the GA converges to a population containing only ML-type firing models, that is, models with an instantaneous inward and single-state 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.