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Natural Computing
Erschienen in: Natural Computing 4/2013

01.12.2013

Biochemical connectionism

verfasst von: Michael A. Lones, Alexander P. Turner, Luis A. Fuente, Susan Stepney, Leo S. D. Caves, Andy M. Tyrrell

Erschienen in: Natural Computing | Ausgabe 4/2013

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Abstract

In this paper, we discuss computational architectures that are motivated by connectionist patterns that occur in biochemical networks, and speculate about how this biochemical approach to connectionism might complement conventional neural approaches. In particular, we focus on three features of biochemical networks that make them distinct from neural networks: their diverse, complex nodal processes, their emergent organisation, and the dynamical behaviours that result from higher-order, self-modifying processes. We also consider the growing use of evolutionary algorithms in the design of connectionist systems, noting how this enables us to explore a wider range of connectionist architectures, and how the close relationship between biochemical networks and biological evolution can guide us in this endeavour.
Vorheriger Artikel Consideration of mobile DNA: new forms of artificial genetic regulatory networks
Nächster Artikel Geiringer theorems: from population genetics to computational intelligence, memory evolutive systems and Hebbian learning

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Metadaten
Titel
Biochemical connectionism
verfasst von
Michael A. Lones
Alexander P. Turner
Luis A. Fuente
Susan Stepney
Leo S. D. Caves
Andy M. Tyrrell
Publikationsdatum
01.12.2013
Verlag
Springer Netherlands
Erschienen in
Natural Computing / Ausgabe 4/2013
Print ISSN: 1567-7818
Elektronische ISSN: 1572-9796
DOI
https://doi.org/10.1007/s11047-013-9400-y

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