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Associative Memory in Reaction-Diffusion Chemistry

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Advances in Unconventional Computing

Part of the book series: Emergence, Complexity and Computation ((ECC,volume 23))

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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Authors and Affiliations

  1. Department of Computer Science, University of York, York, North Yorkshire, YO10 5GE, England

    James Stovold & Simon O’Keefe

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  1. James Stovold
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  2. Simon O’Keefe
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Correspondence to Simon O’Keefe .

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Editors and Affiliations

  1. Unconventional ComputingCentre, University of the Westof England Unconventional ComputingCentre, Bristol, United Kingdom

    Andrew Adamatzky

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Stovold, J., O’Keefe, S. (2017). Associative Memory in Reaction-Diffusion Chemistry. In: Adamatzky, A. (eds) Advances in Unconventional Computing. Emergence, Complexity and Computation, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-319-33921-4_6

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  • DOI: https://doi.org/10.1007/978-3-319-33921-4_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33920-7

  • Online ISBN: 978-3-319-33921-4

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