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Reservoir Computing as a Model for In-Materio Computing

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Advances in Unconventional Computing

Part of the book series: Emergence, Complexity and Computation ((ECC,volume 22))

Abstract

Research in substrate-based computing has shown that materials contain rich properties that can be exploited to solve computational problems. One such technique known as Evolution-in-materio uses evolutionary algorithms to manipulate material substrates for computation. However, in general, modelling the computational processes occurring in such systems is a difficult task and understanding what part of the embodied system is doing the computation is still fairly ill-defined. This chapter discusses the prospects of using Reservoir Computing as a model for in-materio computing, introducing new training techniques (taken from Reservoir Computing) that could overcome training difficulties found in the current Evolution-in-Materio technique.

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Acknowledgments

Matthew Dale is funded by a Defence Science and Technology Laboratory (DSTL) Ph.D. studentship.

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Authors and Affiliations

  1. Department of Computer Science, University of York, York, UK

    Matthew Dale & Susan Stepney

  2. Department of Electronics, University of York, York, UK

    Julian F. Miller

  3. York Centre for Complex Systems Analysis, University of York, York, UK

    Susan Stepney

Authors
  1. Matthew Dale
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  2. Julian F. Miller
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  3. Susan Stepney
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Correspondence to Matthew Dale .

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Editors and Affiliations

  1. Unconventional Computing Centre, University of the West of England Unconventional Computing Centre, Bristol, United Kingdom

    Andrew Adamatzky

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Dale, M., Miller, J.F., Stepney, S. (2017). Reservoir Computing as a Model for In-Materio Computing. In: Adamatzky, A. (eds) Advances in Unconventional Computing. Emergence, Complexity and Computation, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-319-33924-5_22

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  • DOI: https://doi.org/10.1007/978-3-319-33924-5_22

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