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First Steps in Evolving Path Integration in Simulation

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

Abstract

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.

Keywords

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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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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