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Bayesian multisensory integration and cross-modal spatial links.

Sophie Deneve1, Alexandre Pouget

  • 1Gatsby Computational Neuroscience Unit, Alexandra House, 17 Queen Square, London WC1N 3AR, UK. sdeneve@gatsby.ucl.ac.edu

Journal of Physiology, Paris
|October 13, 2004
PubMed
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Multisensory integration combines information from different senses, like vision and audition. We propose a Bayesian framework with basis function networks to optimally integrate these cues, viewing it as a sensory dialogue.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Sensory Perception

Background:

  • Human perception integrates information from multiple senses (vision, audition, proprioception).
  • Sensory modalities utilize distinct frames of reference for object representation and localization.
  • The reliability of sensory cues varies dynamically with context.

Purpose of the Study:

  • To present a Bayesian framework for optimal multisensory cue integration.
  • To address the challenge of differing frames of reference in multisensory perception.
  • To propose a computational model for effective multisensory combination.

Main Methods:

  • Review of the Bayesian framework for cue integration.
  • Introduction of basis function networks for creating cross-modal spatial links.

Related Experiment Videos

  • Integration of basis function networks with the Bayesian framework.

    • Main Results:

    • The Bayesian framework offers an optimal solution for combining unreliable sensory cues.
    • Basis function networks effectively solve the frame of reference problem.
    • Combined Bayesian and basis function networks enable optimal multisensory integration.

    Conclusions:

    • Multisensory integration is best modeled as an interactive dialogue between senses.
    • This approach moves beyond the concept of convergence onto a single supra-modal area.
    • The proposed framework provides a unified model for multisensory perception.