Artificial Neural Networks Based on Brain Circuits Behaviour and Genetic Algorithms

  • Ana Porto
  • Alejandro Pazos
  • Alfonso Araque
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3512)


Once the behaviour of particular brain circuits has been analyzed, we have added up some of these patterns to Artificial Neural Networks; thus a new hybrid learning method has emerged. In order to find the best solution to a given problem, this method combines the use of Genetic Algorithms with particular changes to connection weights based in the behaviour observed in the brain circuits analyzed. The design and implementation of this combination is shown in feed-forward multilayer artificial neural networks, specifically created to solve a simple problem. We also illustrate the benefits obtained with these new nets from a comparison with previous results achieved by the optimal Artificial Neural Networks used so far for solving the same problem.


Genetic Algorithm Artificial Neural Network Mean Square Error Connection Weight Brain Circuit 
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 2005

Authors and Affiliations

  • Ana Porto
    • 1
  • Alejandro Pazos
    • 1
  • Alfonso Araque
    • 2
  1. 1.Department of Information and Communications TechnologiesUniversity of A CoruñaA CoruñaSpain
  2. 2.Laboratorio de Electrofisiología, Instituto de Neurobiología Ramón y CajalCSICMadridSpain

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