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Symmetric discrete universal neural networks

✍ Scribed by Eric Goles; Martín Matamala

Publisher
Elsevier Science
Year
1996
Tongue
English
Weight
697 KB
Volume
168
Category
Article
ISSN
0304-3975

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✦ Synopsis

Given the class of symmetric discrete weight neural networks with finite state set (0, l}, we prove that there exist iteration modes under these networks which allow to simulate in linear space arbitrary neural networks (non-necessarily symmetric). As a particular result we prove that an arbitrary symmetric neural network can be simulated by a symmetric one iterated sequentially, with some negative diagonal weights. Further, considering only the synchronous update we prove that symmetric neural networks with one refractory state are able to simulate arbitrary neural networks. 'Partially supported by Fondecyt 1940520(E.G) and 1950569(M.M), ECOS (E.G, M.M) and CEE-CIl *CT92-0046.

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