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Modeling spatial integration in the ocular following response using a probabilistic framework.

Laurent U Perrinet1, Guillaume S Masson

  • 1Institut de Neurosciences Cognitives de la Méditerranée (INCM-UMR 6193, CNRS) 31, ch Joseph Aiguier, 13402, Marseille Cedex 20, France. Laurent.Perrinet@incm.cnrs-mrs.fr

Journal of Physiology, Paris
|November 29, 2007
PubMed
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The primate visual system efficiently resolves motion perception uncertainties using Bayesian theory. This study models ocular following response (OFR) spatial integration, revealing how local motion cues are combined and modulated by surround stimuli.

Area of Science:

  • Neuroscience
  • Computational Vision
  • Sensory-motor Systems

Background:

  • The primate visual system excels at resolving motion perception uncertainties for sensorimotor transformations like ocular following response (OFR).
  • Bayesian theory provides a probabilistic framework for understanding optimal perception, previously applied successfully to model OFR with full-field gratings.
  • Recent OFR studies utilize disk and bipartite stimuli to investigate center-surround integration dynamics.

Purpose of the Study:

  • To extend the ideal observer model to simulate the spatial integration of local motion cues within a probabilistic framework.
  • To analyze the characteristics of spatial motion integration, including optimal stimulus size and surround modulation effects on OFR.
  • To account for observed contrast gain control and suppressive effects in behavioral data using an extended computational model.

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Main Methods:

  • Extended an ideal observer model based on Bayesian theory to incorporate spatial integration of local motion cues.
  • Employed analytical methods to test the hypothesis of independence for local motion measures.
  • Simulated and analyzed behavioral data from OFR experiments using center-surround stimuli.

Main Results:

  • The hypothesis of independent local motion measures adequately describes the spatial integration of motion signals.
  • The extended model successfully accounted for contrast gain control mechanisms in behavioral data for center-surround stimuli.
  • An additional inhibitory mechanism was required to explain the observed suppressive effects of the surround.

Conclusions:

  • The spatial integration of motion signals can be effectively modeled by assuming independence of local motion cues within a probabilistic framework.
  • The model captures key aspects of center-surround interactions in OFR, including contrast gain control.
  • Further inhibitory mechanisms are necessary to fully explain the complex suppressive influences of the stimulus surround on OFR.