Organisms modeling: The question of radial basis function networks

Abstract : 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 systems-theory and artificial neural networks, computer scientists are tempted to build their own systems independently of biological issues. This publication is a first-step re-evaluation of an usual machine learning technique (radial basis function(RBF) networks) in the context of systems and biological reactive organisms.
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Communication dans un congrès
Workshop Activity-Based Modeling & Simulation (ACTIMS 2014), Jan 2014, Zürich, Switzerland. 3, pp.03002, ACTIMS 2014 – Activity-Based Modeling & Simulation 2014. <10.1051/itmconf/20140303002>
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https://hal.archives-ouvertes.fr/hal-01315184
Contributeur : Alexandre Muzy <>
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Dernière modification le : jeudi 2 février 2017 - 16:01:36
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Alexandre Muzy, Lauriane Massardier, Patrick Coquillard. Organisms modeling: The question of radial basis function networks. Workshop Activity-Based Modeling & Simulation (ACTIMS 2014), Jan 2014, Zürich, Switzerland. 3, pp.03002, ACTIMS 2014 – Activity-Based Modeling & Simulation 2014. <10.1051/itmconf/20140303002>. <hal-01315184>

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