Laurent Perrinet is researcher in Computational Neuroscience at the "Institut de Neurosciences de la Timone" (France), a joint research unit (CNRS / Aix-Marseille Université). He graduated from the aeronautics engineering school SUPAERO, in Toulouse (France) with a signal processing and stochastic modelization degree. He received a PhD. in Cognitive Science in 2003. The thesis was directed by Manuel Samuelides and co-directed by Simon Thorpe. It emphasized on mathematical analysis on temporal spike coding and specifically rank-order coding. This work was extended on multi-scale and adaptive representation of natural static scenes. His research program is now focusing in bridging the complex dynamics of realistic models of large-scale models of spiking neurons with functional models of low-level vision. 1) We study the macroscopic effect of microstructures (di-synaptic thalamo-cortical contacts, patchy connections) by implementing realistic models of the primary visual cortex. These results are confronted to experimental optical imaging data collected conjointly in the lab. 2) We model low-level vision function (tracking or catching an object) by using Bayesian-type probabilistic representations of information. These models introduce non-linearities (contrast gain control, center-surround interactions, adaptation) which are emerging naturally from the simple operations on probabilities. Current challenge is to be able to translate, or compile in computer terminology, such functional models into neural architectures that would exhibit similar dynamics.

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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