Organisms modeling: The question of radial basis function networks

There exists usually a gap between bio-inspired computational techniques and what biologists can do with these techniques in their current researches. Although biology is the root of system-theory and artifical neural networks, computer scientists are tempted to build their own systems independently...

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Bibliographische Detailangaben
Hauptverfasser: Muzy, Alexandre, Massardier, Lauriane, Coquillard, Patrick
Format: Tagungsbericht
Sprache:eng
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Beschreibung
Zusammenfassung:There exists usually a gap between bio-inspired computational techniques and what biologists can do with these techniques in their current researches. Although biology is the root of system-theory and artifical neural networks, computer scientists are tempted to build their own systems independently of biological issues. This publication is a first-step re-evalution of an usual machine learning technique (radial basis funtion(RBF) networks) in the context of systems and biological reactive organisms.
ISSN:2271-2097
2431-7578
2271-2097
DOI:10.1051/itmconf/20140303002