Associative Memory in Reaction-Diffusion Chemistry

James Henry Stovold; Simon Edward Marius O'Keefe

Associative Memory in Reaction-Diffusion Chemistry

James Henry Stovold, Simon Edward Marius O'Keefe

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

Unconventional computing paradigms are typically very difficult to program. By implementing efficient parallel control architectures such as artificial neural networks, we show that it is possible to program unconventional paradigms with relative ease. The work presented implements correlation matrix memories (a form of artificial neural network based on associative memory) in reaction-diffusion chemistry, and shows that implementations of such artificial neural networks can be trained and act in a similar way to conventional implementations.

Original language	English
Title of host publication	Advances in Unconventional Computing
Subtitle of host publication	Volume 2: Prototypes, Models and Algorithms
Editors	Andrew Adamatzky
Publisher	Springer
Pages	141-165
Number of pages	25
Volume	2
Edition	1
ISBN (Electronic)	978-3-319-33921-4
ISBN (Print)	978-3-319-33920-7
Publication status	Published - 2017

Publication series

Name	Emergence, Complexity and Computation
Publisher	Springer Verlag Berlin
Volume	23
ISSN (Print)	2194-7287
ISSN (Electronic)	2194-7295

Cite this

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title = "Associative Memory in Reaction-Diffusion Chemistry",

abstract = "Unconventional computing paradigms are typically very difficult to program. By implementing efficient parallel control architectures such as artificial neural networks, we show that it is possible to program unconventional paradigms with relative ease. The work presented implements correlation matrix memories (a form of artificial neural network based on associative memory) in reaction-diffusion chemistry, and shows that implementations of such artificial neural networks can be trained and act in a similar way to conventional implementations.",

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Associative Memory in Reaction-Diffusion Chemistry. / Stovold, James Henry; O'Keefe, Simon Edward Marius.
Advances in Unconventional Computing: Volume 2: Prototypes, Models and Algorithms. ed. / Andrew Adamatzky. Vol. 2 1. ed. Springer, 2017. p. 141-165 (Emergence, Complexity and Computation; Vol. 23).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

TY - CHAP

T1 - Associative Memory in Reaction-Diffusion Chemistry

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AU - O'Keefe, Simon Edward Marius

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AB - Unconventional computing paradigms are typically very difficult to program. By implementing efficient parallel control architectures such as artificial neural networks, we show that it is possible to program unconventional paradigms with relative ease. The work presented implements correlation matrix memories (a form of artificial neural network based on associative memory) in reaction-diffusion chemistry, and shows that implementations of such artificial neural networks can be trained and act in a similar way to conventional implementations.

M3 - Chapter

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T3 - Emergence, Complexity and Computation

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EP - 165

BT - Advances in Unconventional Computing

A2 - Adamatzky, Andrew

PB - Springer

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