Use of a genetic algorithm for neuron model specification

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

Research output: Contribution to conferencePaperpeer-review


We have used 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 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. However, 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 not allowed in 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
Number of pages3
StatePublished - 2005
Event2nd International IEEE EMBS Conference on Neural Engineering, 2005 - Arlington, VA, United States
Duration: Mar 16 2005Mar 19 2005


Conference2nd International IEEE EMBS Conference on Neural Engineering, 2005
Country/TerritoryUnited States
CityArlington, VA


  • Genetic Algorithm
  • Neuron model

ASJC Scopus subject areas

  • General Engineering


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