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Evolution of Multisensory Integration in Large Neural Fields

  • Benjamin Inden
  • Yaochu Jin
  • Robert Haschke
  • Helge Ritter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7401)

Abstract

We show that by evolving neural fields it is possible to study the evolution of neural networks that perform multisensory integration of high dimensional input data. In particular, four simple tasks for the integration of visual and tactile input are introduced. Neural networks evolve that can use these senses in a cost-optimal way, enhance the accuracy of classifying noisy input images, or enhance spatial accuracy of perception. An evolved neural network is shown to display a kind of McGurk effect.

Keywords

Connection Weight Multisensory Integration Lateral Connection Connection Gene Evolutionary Robotic 
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 2012

Authors and Affiliations

  • Benjamin Inden
    • 1
  • Yaochu Jin
    • 2
  • Robert Haschke
    • 3
  • Helge Ritter
    • 3
  1. 1.Research Institute for Cognition and RoboticsBielefeld UniversityGermany
  2. 2.Department of ComputingUniversity of SurreyUK
  3. 3.Neuroinformatics GroupBielefeld UniversityGermany

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