Journal article 504 views
Reaction–diffusion chemistry implementation of associative memory neural network
International Journal of Parallel, Emergent and Distributed Systems, Volume: 32, Issue: 1, Pages: 74 - 94
Swansea University Author: James Stovold
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DOI (Published version): 10.1080/17445760.2016.1155579
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 matri...
| Published in: | International Journal of Parallel, Emergent and Distributed Systems |
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| ISSN: | 1744-5760 1744-5779 |
| Published: |
Taylor & Francis
2017
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa39956 |
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| 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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| Keywords: |
Associative memory, artificial neural network, correlation matrix memory, reaction–diffusion chemistry, unconventional computing |
| College: |
Faculty of Science and Engineering |
| Issue: |
1 |
| Start Page: |
74 |
| End Page: |
94 |