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 independentl...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2014-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | http://dx.doi.org/10.1051/itmconf/20140303002 |
| Summary: | 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.
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| ISSN: | 2271-2097 |