Propriétés émergentes d'un modèle de prédiction probabiliste utilisant un champ neural

Abstract

Sensory informations such as visual images are inherently variable. We use probabilistic models to describe how the low-level visual system could describe superposed and ambiguous information. This allows to describe the interactions of neighboring populations of neurons as inference rules that dynamically build up the overall description of the visual scene. We focus here on temporal prediction, that is by the transport of information based on an estimate of local motion in the image.

Date
Jul 2, 2011 12:00 AM
Event
Atelier Neurosciences Computationnelles, 2-3 Juillet 2011 Khemisset, Maroc

La finalité de cette manifestation est de permettre à nos chercheurs de se réunir en groupes de travail et en ateliers afin de découvrir la thématique des neurosciences et son interdisciplinarité. La manifestation se tient dans le cadre des activités du laboratoire LAMS, de ABC MATHINFO, du GDRI NeurO et du réseau méditerranéen NeuroMed. * related publication @ SPIE 2008 * see this more recent talk @ UCL, London

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Laurent U Perrinet
Researcher in Computational Neuroscience

My research interests include Machine Learning and computational neuroscience applied to Vision.

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