Abstract
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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Porto, A., Pazos, A., Araque, A. (2005). Artificial Neural Networks Based on Brain Circuits Behaviour and Genetic Algorithms. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_13
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DOI: https://doi.org/10.1007/11494669_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
Online ISBN: 978-3-540-32106-4
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