First Steps in Evolving Path Integration in Simulation

  • Robert Vickerstaff
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2801)


Path integration is a widely used method of navigation in nature whereby an animal continuously tracks its location by integrating its motion over the course of a journey. Many mathematical models of this process exist, as do at least two hand designed neural network models. Two one dimensional distance measuring tasks are here presented as a simplified analogy of path integration and as a first step towards producing a neuron-based model of full path integration constructed entirely by artificial evolution. Simulated agents are evolved capable of measuring the distance they have travelled along a one dimensional space. The resulting neural mechanisms are analysed and discussed, along with the prospects of producing a full model using the same methodology.


Path Integration Signal Neuron Speed Sensor Food Sensor Reverse Motor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Collett, M., Collett, T.S.: How do insects use path integration for their navigation? Biol. Cybern. 83, 245–259 (2000)CrossRefGoogle Scholar
  2. 2.
    Wehner, R., Gallizzi, K., Frei, C., Vesely, M.: Calibration processes in desert ant navigation: vector courses and systematic search. J. Comp. Physiol. A 188, 683–693 (2002)CrossRefGoogle Scholar
  3. 3.
    Wehner, R., Michel, B., Antonsen, P.: Visual navigation in insects: coupling of egocentric and geocentric information. The Journal of Experimental Biology 199, 129–140 (1996)Google Scholar
  4. 4.
    Benhamou, S., Seguinot, V.: How to Find one’s Way in the Labyrinth of Path Integration Models. J. theor. Biol. 174, 463–466 (1995)CrossRefGoogle Scholar
  5. 5.
    Maurer, R., Seguinot, V.: What is Modelling For? A Critical Review of the Models of Path Integration. J. theor. Biol. 175, 457–475 (1995)CrossRefGoogle Scholar
  6. 6.
    Hartmann, G., Wehner, R.: The ant’s path integration system: a neural architecture. Biol. Cybern. 73, 482–497 (1995)Google Scholar
  7. 7.
    Wittmann, T., Schwegler, H.: Path integration - a network model. Biol. Cybern. 73, 569–575 (1995)zbMATHCrossRefGoogle Scholar
  8. 8.
    Beer, R.D.: Toward the evolution of dynamical neural networks for minimally cognitive behavior. In: Maes, P., Mataric, M., Meyer, J., Pollack, J., Wilson, S. (eds.) From animals to animats 4: Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, pp. 421–429. MIT Press, Cambridge (1996)Google Scholar
  9. 9.
    Goldberg, D.E.: Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  10. 10.
    Egrov, A.V., Hamam, B.N., Fransen, E., Hassel, M.E., Alonso, A.A.: Graded persistent activity in entorhinal cortex neurons. Nature 420, 173–178 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Robert Vickerstaff
    • 1
  1. 1.Centre for Computational Neuroscience and Robotics, School of Biological SciencesUniversity of SussexBrightonUK

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