Neural Network Models of Chemotaxis in the Nematode Caenorhabditis Elegans

Part of Advances in Neural Information Processing Systems 9 (NIPS 1996)

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Thomas Ferrée, Ben Marcotte, Shawn Lockery


We train recurrent networks to control chemotaxis in a computer model of the nematode C. elegans. The model presented is based closely on the body mechanics, behavioral analyses, neuroanatomy and neurophysiology of C. elegans, each imposing constraints rel(cid:173) evant for information processing. Simulated worms moving au(cid:173) tonomously in simulated chemical environments display a variety of chemotaxis strategies similar to those of biological worms.