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ICANN ’93 pp 90-95 | Cite as

A Self-organizing Neural Network for Learning A Body-centered Invariant Representation of 3-D Target Position

  • Daniel Bullock
  • Douglas Greve
  • Stephen Grossberg
  • Frank H. Guenther
Conference paper

Abstract

This paper describes a self-organizing neural network that learns a body-centered representation of 3-D target positions. This representation remains invariant under head and eye movements, and is a key component of sensory-motor systems for producing motor equivalent reaches to targets [1]. Learning requires no teacher, instead utilizing information gained from an action-perception cycle in which head movements are made while a stationary target is foveated. Because the spatial representations used relate closely to neck anatomy, the network learns very rapidly, converging after foveating only 200 targets.

Keywords

Head Movement Target Position Neck Anatomy Neck Angle Vestibular Ocular Reflex 
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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References

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Copyright information

© Springer-Verlag London Limited 1993

Authors and Affiliations

  • Daniel Bullock
    • 1
  • Douglas Greve
    • 1
  • Stephen Grossberg
    • 1
  • Frank H. Guenther
    • 1
  1. 1.Department of Cognitive and Neural SystemsBoston UniversityBostonUSA

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