Using Evolutionary Methods to Parameterize Neural Models: A Study of the Lamprey Central Pattern Generator

  • John Hallam
  • Auke Jan Ijspeert
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 109)


The neural controller of anguilliform swimming in lampreys is particularly well studied because of its relative robustness and simplicity. In this chapter we look at connectionist models of the controller — in which populations of similar neurons are represented by abstract, differential-equation-based models — and describe the use of evolutionary computation techniques for investigating the space of appropriate architectures and parameters for such models. An introduction to this style of modeling is followed by a presentation of the lamprey central pattern generator model, devised by Ekeberg, and its development by Ijspeert using genetic algorithms. Some results on the robustness to body variation in the modelled controllers will be described, and the value of this approach to neural modeling work will be discussed.


Central Pattern Generator Connectionist Model Connection Strength Biological Cybernetics Segmental Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • John Hallam
    • 1
  • Auke Jan Ijspeert
    • 2
  1. 1.Division of InformaticsUniversity of EdinburghScotland
  2. 2.Department of Computer ScienceUniversity of Southern CaliforniaLos AngelesUSA

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