August 2009
Volume 9, Issue 8
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Vision Sciences Society Annual Meeting Abstract  |   August 2009
Inferring monkey ocular following responses from V1 population dynamics using a probabilistic model of motion integration
Author Affiliations
  • Perrinet Laurent
    INCM,UMR 6193 CNRS-Université de la Méditerranée 31, chemin Joseph Aiguier 13402 Marseille cedex, France
  • Reynaud Alexandre
    INCM,UMR 6193 CNRS-Université de la Méditerranée 31, chemin Joseph Aiguier 13402 Marseille cedex, France
  • Chavane Frédéric
    INCM,UMR 6193 CNRS-Université de la Méditerranée 31, chemin Joseph Aiguier 13402 Marseille cedex, France
Journal of Vision August 2009, Vol.9, 745. doi:10.1167/9.8.745
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      Perrinet Laurent, Reynaud Alexandre, Chavane Frédéric, Masson Guillaume; Inferring monkey ocular following responses from V1 population dynamics using a probabilistic model of motion integration. Journal of Vision 2009;9(8):745. doi: 10.1167/9.8.745.

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      © 2016 Association for Research in Vision and Ophthalmology.

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Abstract
 

Short presentation of a large moving pattern elicits an ocular following response that exhibits many of the properties attributed to low-level motion processing such as spatial and temporal integration, contrast gain control and divisive interaction between competing motions. Similar mechanisms have been demonstrated in V1 cortical activity in response to center-surround gratings patterns measured with real-time optical imaging in awake monkeys (see poster of Reynaud et al., VSS09). Based on a previously developed Bayesian framework, we have developed an optimal statistical decoder of such an observed cortical population activity as recorded by optical imaging. This model aims at characterizing the statistical dependance between early neuronal activity and ocular responses and its performance was analyzed by comparing this neuronal read-out and the actual motor responses on a trial-by-trial basis. First, we show that relative performance of the behavioral contrast response function is similar to the best estimate obtained from the neural activity. In particular, we show that the latency of ocular response increases with low contrast conditions as well as with noisier instances of the behavioral task as decoded by the model. Then, we investigate the temporal dynamics of both neuronal and motor responses and show how motion information as represented by the model is integrated in space to improve population decoding over time. Lastly, we explore how a surrounding velocity non congruous with the central excitation information shunts the ocular response and how it is topographically represented in the cortical activity.

 
Laurent, P. Alexandre, R. Frédéric, C. Guillaume, M. (2009). Inferring monkey ocular following responses from V1 population dynamics using a probabilistic model of motion integration [Abstract]. Journal of Vision, 9(8):745, 745a, http://journalofvision.org/9/8/745/, doi:10.1167/9.8.745. [CrossRef]
Footnotes
 This work was supported by EC IP project FP6-015879, “FACETS”.
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