Evolving neural models of path integration

Vickerstaff, R J and Di Paolo, E A (2005) Evolving neural models of path integration. Journal of Experimental Biology, 208. pp. 3349-3366. ISSN 0022-0949

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Item Type: Article
Additional Information: Originality: Uses evolutionary algorithms to synthesize minimal dynamical networks for path-integration behaviour. Introduces new kinds of plastic continuous-time recurrent neural networks; analyses resulting models and links them to improved versions of mathematical models demonstrating their implementability at the neuronal level. Rigour: uses improved fitness function criteria for incremental evolution; deploys dynamical systems analytical tools and compares results with real data on desert ant path integration. Significance: demonstration of how an embodied system may afford quite compact and simple home vector navigation. Shows the significance of compass sensor profiles in facilitating navigation, shows how the effects of neural activation decay can be compensated for. First publication of an evolved model in this experimental journal.
Schools and Departments: School of Engineering and Informatics > Informatics
Depositing User: Ezequiel Alejandro Di Paolo
Date Deposited: 06 Feb 2012 18:29
Last Modified: 27 Mar 2012 08:21
URI: http://sro.sussex.ac.uk/id/eprint/16650
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