Abstract
It has been known for a short time that a class of recurrent neural networks has universal computational abilities. These networks can be viewed as iterated piecewise-linear maps in a high-dimensional space. In this paper, we show that similar systems in dimension two are also capable of universal computations. On the contrary, it is necessary to resort to more complex systems (e.g., iterated piecewise-monotone maps) in order to retain this capability in dimension one.
Cite
CITATION STYLE
Cosnard, M., Garzon, M., & Koiran, P. (1993). Computability properties of low-dimensional dynamical systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 665 LNCS, pp. 365–373). Springer Verlag. https://doi.org/10.1007/3-540-56503-5_37
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