Deterministic modelling of randomness with recurrent artificial neural networks

Authors:
Norman U. Baier;Oscar De Feo
Affiliations:
Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland;Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Year:
2005

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Abstract

It is shown that deterministic (chaotic) systems can be used to implicitly model the randomness of stochastic data, a question arising when addressing information processing in the brain according to the paradigm proposed by the EC APEREST project. More precisely, for a particular class of recurrent artificial neural networks, the identification procedure of stochastic signals leads to deterministic (chaotic) models which mimic the statistical/spectral properties of the original data.