Biological computing using perfusion anodophile biofilm electrodes (PABE)
Biological computing using perfusion anodophile biofilm electrodes (PABE)
This paper presents a theoretical approach to biological computing, using biofilm electrodes by illustrating a simplified Pavlovian learning model. The theory behind this approach was based on empirical data produced from a prototype version of these units, which illustrated high stability. The implementation of this system into the Pavlovian learning model, is one example and possibly a first step in illustrating, and at the same time discovering its potential as a computing processor.
artificial intelligence, biological computing, neurone-like, transistor-like, unit and connected assemblies, Pavlovian association learning
23-32
Greenman, John
eb3d9b82-7cac-4442-9301-f34884ae4a16
Ieropoulos, Ioannis
6c580270-3e08-430a-9f49-7fbe869daf13
Melhuish, Chris
c52dcc8b-1e36-425e-80df-9d05d2b21893
2008
Greenman, John
eb3d9b82-7cac-4442-9301-f34884ae4a16
Ieropoulos, Ioannis
6c580270-3e08-430a-9f49-7fbe869daf13
Melhuish, Chris
c52dcc8b-1e36-425e-80df-9d05d2b21893
Greenman, John, Ieropoulos, Ioannis and Melhuish, Chris
(2008)
Biological computing using perfusion anodophile biofilm electrodes (PABE).
International Journal of Unconventional Computing, 4 (1), .
Abstract
This paper presents a theoretical approach to biological computing, using biofilm electrodes by illustrating a simplified Pavlovian learning model. The theory behind this approach was based on empirical data produced from a prototype version of these units, which illustrated high stability. The implementation of this system into the Pavlovian learning model, is one example and possibly a first step in illustrating, and at the same time discovering its potential as a computing processor.
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Published date: 2008
Keywords:
artificial intelligence, biological computing, neurone-like, transistor-like, unit and connected assemblies, Pavlovian association learning
Identifiers
Local EPrints ID: 454775
URI: http://eprints.soton.ac.uk/id/eprint/454775
ISSN: 1548-7199
PURE UUID: 94053d87-0456-4f2e-a2b5-2e6b390c8949
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Date deposited: 23 Feb 2022 17:35
Last modified: 17 Mar 2024 04:10
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Contributors
Author:
John Greenman
Author:
Chris Melhuish
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