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Evolving neural models of path integration

journal contribution
posted on 2023-06-07, 20:12 authored by R J Vickerstaff, Ezequiel Di Paolo
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History

Publication status

  • Published

Journal

Journal of Experimental Biology

ISSN

0022-0949

External DOI

Volume

208

Page range

3349-3366

Pages

18.0

Department affiliated with

  • Informatics Publications

Notes

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.

Full text available

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Peer reviewed?

  • Yes

Legacy Posted Date

2012-02-06

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