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Using Probabilistic Dependencies Improves the Search of Conductance-Based Compartmental Neuron Models

  • Roberto Santana
  • Concha Bielza
  • Pedro Larrañaga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6023)

Abstract

Conductance-based compartmental neuron models are traditionally used to investigate the electrophysiological properties of neurons. These models require a number of parameters to be adjusted to biological experimental data and this question can be posed as an optimization problem. In this paper we investigate the behavior of different estimation of distribution algorithms (EDAs) for this problem. We focus on studying the influence that the interactions between the neuron model conductances have in the complexity of the optimization problem. We support evidence that the use of these interactions during the optimization process can improve the EDA behavior.

Keywords

Conductance-based compartmental neuron models estimation of distribution algorithm probabilistic models 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Roberto Santana
    • 1
  • Concha Bielza
    • 1
  • Pedro Larrañaga
    • 1
  1. 1.Departmento de Inteligencia ArtificialUniversidad Politécnica de MadridMadridSpain

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