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.
An event ranging “From Mathematical Image Analysis to Neurogeometry of the Brain” LADISLAV TAUC & GDR MSPC NEUROSCIENCES CONFERENCE.