Motion-based prediction with neuromorphic hardware

Abstract

We stand at a point in history where our phones have become smart but lack a feature which prevails in most forms of living intelligence: vision. The ability to see is indeed an essential facet of intelligence which is developed in an autonomous manner even in young human infants. I will focus here on a particular problem: how do we estimate motion in a visual image? I will explain why for this problem, it is crucial to understand how the visual system might overcome temporal delays and will demonstrate at different levels of description, from probabilistic models to neuromorphic hardware, a surprising solution: The visual system models the world and uses the eye to probe this model.

Date
Oct 7, 2015 1:00 PM
Location
Saint-Paul de Vence
Laurent U Perrinet
Laurent U Perrinet
Researcher in Computational Neuroscience

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

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