Noise-robust realization of Turing-complete cellular automata by using neural networks with pattern representation

Makito Oku*, Kazuyuki Aihara

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

A modularly-structured neural network model is considered. Each module, which we call a 'cell', consists of two parts: a Hopfield neural network model and a multilayered perceptron. An array of such cells is used to simulate the Rule 110 cellular automaton with high accuracy even when all the units of neural networks are replaced by stochastic binary ones. We also find that noise not only degrades but also facilitates computation if the outputs of multilayered perceptrons are below the threshold required to update the states of the cells, which is a stochastic resonance in computation.

Original languageEnglish
Pages (from-to)4859-4863
Number of pages5
JournalPhysics Letters, Section A: General, Atomic and Solid State Physics
Volume374
Issue number48
DOIs
StatePublished - 2010/11/01

ASJC Scopus subject areas

  • General Physics and Astronomy

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