Laurent Perrinet (Researcher, CNRS) is a computational neuroscientist specialized in large scale neural network models of low-level vision, perception and action, currently at the "Institut de Neurosciences de la Timone" (France), a joint research unit (CNRS / Aix-Marseille Université). He co-authored more than 35 articles in computational neuroscience and computer vision. He graduated from the aeronautics engineering school SUPAERO, in Toulouse (France) with a signal processing and stochastic modeling degree. He received a PhD. in Cognitive Science in 2003 on the mathematical analysis on temporal spike coding in multi-scale and adaptive representation of natural scenes. His research program is focusing in bridging the complex dynamics of realistic, large-scale models of spiking neurons with functional models of low-level vision. In particular, as part of the FACETS and BrainScaleS consortia, he has developed experimental protocols in collaboration with neurophysiologists to characterize the response of population of neurons. Recently, he extended models of visual processing in the framework of predictive processing in collaboration with the team of Karl Friston at the University College of London. This method aims at characterizing the processing of dynamical flow of information as an active inference process. His current challenge is to translate, or compile in computer terminology, this mathematical formalism with the event-based nature of neural information with the aim of pushing forward the frontiers of Artificial Intelligence systems.

Dissertation Topic: How to decipher the visual spike code? Study of the parallel, asynchronous and sparse dynamics in ultra-rapid visual processing.

Specialization in stochastic models for signal and image processing and particularly by means of artificial neural networks.

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