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.
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Baier, N.U., De Feo, O. (2005). Deterministic Modelling of Randomness with Recurrent Artificial Neural Networks. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_41
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DOI: https://doi.org/10.1007/11550822_41
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