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First published online August 18, 2005
Journal of Experimental Biology 208, 3349-3366 (2005)
Published by The Company of Biologists 2005
doi: 10.1242/jeb.01772

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Evolving neural models of path integration

R. J. Vickerstaff^* and E. A. Di Paolo

Centre for Computational Neuroscience and Robotics, School of Life Sciences, University of Sussex, Brighton, BN1 9QG, UK

^* Author for correspondence (e-mail: robertvi{at}sussex.ac.uk)

Accepted 29 June 2005

We use a genetic algorithm to evolve neural models of path integration, with particular emphasis on reproducing the homing behaviour of Cataglyphis fortis ants. This is done within the context of a complete model system, including an explicit representation of the animal's movements within its environment. We show that it is possible to produce a neural network without imposing a priori any particular system for the internal representation of the animal's home vector. The best evolved network obtained is analysed in detail and is found to resemble the bicomponent model of Mittelstaedt. Because of the presence of leaky integration, the model can reproduce the systematic navigation errors found in desert ants. The model also naturally mimics the searching behaviour that ants perform once they have reached their estimate of the nest location. The results support possible roles for leaky integration and cosine-shaped compass response functions in path integration.

Key words: path integration, Cataglyphis fortis, genetic algorithm, leaky integration

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