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ICANN 98 pp 1103-1108 | Cite as

Self-Localization by Hidden Representations

  • Michael Herrmann
  • Klaus Pawelzik
  • Theo Geisel
Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)

Abstract

We present a framework for generating representations of space in an autonomous agent which does not obtain any direct information about its location. Instead the algorithm relies exclusively on sensory input and internal estimations of actions. The activations within a neural network are propagated in time depending on internal estimations of actions. Sensory input connections are adapted according to a Hebbian learning rules derived from the prediction error on sensory inputs one step ahead. During exploration of the environment the respective cells develop location and direction selectivity even when relying on highly ambiguous stimuli.

Keywords

Mobile Robot Sensory Input Direction Selectivity Ambiguous Stimulus Consistent Orientation 
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 London 1998

Authors and Affiliations

  • Michael Herrmann
    • 1
  • Klaus Pawelzik
    • 1
  • Theo Geisel
    • 1
  1. 1.Max-Planck-Institut für StrömungsforschungGöttingenGermany

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