# Expressive Power of Non-deterministic Evolving Recurrent Neural Networks in Terms of Their Attractor Dynamics

## Détails

ID Serval

serval:BIB_3FC9ED926E81

Type

**Article**: article d'un périodique ou d'un magazine.

Collection

Publications

Institution

Titre

Expressive Power of Non-deterministic Evolving Recurrent Neural Networks in Terms of Their Attractor Dynamics

Périodique

Unconventional Computation and Natural Computation

ISBN

9783319218182

9783319218199

9783319218199

ISSN

0302-9743

1611-3349

1611-3349

Statut éditorial

Publié

Date de publication

2015

Peer-reviewed

Oui

Volume

12

Numéro

1

Pages

144-156

Langue

anglais

Résumé

We provide a characterization of the expressive powers of several models of nondeterministic recurrent neural networks according to their attractor dynamics. More precisely, we consider two forms of nondeterministic neural networks. In the first case, nondeterminism is expressed as an external binary guess stream processed by means of an additional Boolean guess cell. In the second case, nondeterminism is expressed as a set of possible evolving patterns that the synaptic connections of the network might follow over the successive time steps. In these two contexts, ten models of nondeterministic neural networks are considered, according to the nature of their synaptic weights. Overall, we prove that the static rational-weighted neural networks of type 1 are computationally equivalent to nondeterministic Muller Turing machines. They recognize the class of all effectively analytic (Sigma(1)(1) lightface) sets. The nine other models of analog and/or evolving neural networks of types 1 and 2 are all computationally equivalent to each other, and strictly more powerful than nondeterministic Muller Turing machines. They recognize the class of all analytic (Sigma(1)(1) boldface) sets.

Mots-clé

Recurrent neural networks, Neural computation, Analog computation, Evolving systems, Attractors, Spatiotemporal patterns, Turing machines, Expressive power, Omega-languages, Borel sets, Analytic sets

Web of science

Création de la notice

10/05/2017 14:29

Dernière modification de la notice

20/08/2019 14:37