Laurent PERRINET, neuroscientist at the “Institut de Neurosciences de la Timone" will give a talk on Friday, September 8th at 09h30 in room Pierre Cotton of our lab on Event-based vision.
Abstract : Event-based cameras mimic the way biological retinas process visual information : each pixel independently reports brightness changes as asynchronous temporal events. This departs from conventional cameras that capture static frames at fixed intervals. I will first discuss how the retina detects light intensity changes and communicates this to the brain. Compared to traditional cameras, the event-based paradigm enables new vision applications with high-speed, low latency and energy-efficiency. I will highlight recent works applying event cameras to tasks such as visual odometry, motion detection or gesture recognition. The goal is to demonstrate the advantages for computer vision that emulate biological principles.
Biography : Laurent Perrinet 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 40 articles in computational neuroscience and computer vision. He graduated from the aeronautics engineering school SUPAERO, in Toulouse (France) with a signal processing and applied mathematics degree. He received a PhD in Cognitive Science in 2003 on the mathematical analysis of temporal spike coding of images by using a 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 within the NeOpTo team 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.
Contact : Laurent Perrinet - https://laurentperrinet.github.io
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