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Associative Memory in Reaction-Diffusion Chemistry

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Associative Memory in Reaction-Diffusion Chemistry. / Stovold, James.
Advances in Unconventional Computing. Springer, 2016.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter

Harvard

Stovold, J 2016, Associative Memory in Reaction-Diffusion Chemistry. in Advances in Unconventional Computing. Springer. https://doi.org/10.1007/978-3-319-33921-4_6

APA

Stovold, J. (2016). Associative Memory in Reaction-Diffusion Chemistry. In Advances in Unconventional Computing Springer. https://doi.org/10.1007/978-3-319-33921-4_6

Vancouver

Stovold J. Associative Memory in Reaction-Diffusion Chemistry. In Advances in Unconventional Computing. Springer. 2016 doi: 10.1007/978-3-319-33921-4_6

Author

Stovold, James. / Associative Memory in Reaction-Diffusion Chemistry. Advances in Unconventional Computing. Springer, 2016.

Bibtex

@inbook{80813ae83aa848a5addf84d0c960ac1f,
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.",
author = "James Stovold",
year = "2016",
month = jul,
day = "27",
doi = "10.1007/978-3-319-33921-4_6",
language = "English",
booktitle = "Advances in Unconventional Computing",
publisher = "Springer",

}

RIS

TY - CHAP

T1 - Associative Memory in Reaction-Diffusion Chemistry

AU - Stovold, James

PY - 2016/7/27

Y1 - 2016/7/27

N2 - 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.

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.

U2 - 10.1007/978-3-319-33921-4_6

DO - 10.1007/978-3-319-33921-4_6

M3 - Chapter

BT - Advances in Unconventional Computing

PB - Springer

ER -