Genetic algorithm for optimization and specification of a neuron model

W. C. Gerken, L. K. Purvis, R. J. Butera

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

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.

Original languageEnglish
Pages (from-to)1039-1042
Number of pages4
JournalNeurocomputing
Volume69
Issue number10-12
DOIs
StatePublished - May 2006

Bibliographical note

Funding Information:
This work was supported by grants from The National Institutes of Health (R01-MH62057 and R01-NS046851).

Keywords

  • Genetic algorithm
  • Model specification
  • Morris-Lecar
  • Neuron model
  • Parameter optimization

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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