Evolving neural models of path integration

Robert John Vickerstaff, Ezequiel Di Paolo

Research output: Contribution to journalArticlepeer-review

50 Citations (SciVal)


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.
Original languageEnglish
Pages (from-to)3349-3366
Number of pages18
JournalJournal of Experimental Biology
Publication statusPublished - 01 Sept 2005


  • path integration
  • Cataglyphis fortis
  • genetic algorithm
  • leaky integration


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