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Short Bio
Alexandre Pouget is a full Professor at the University of Geneva in the department of basic neurosciences. He received his undergraduate education at the École normale supérieure (Paris), before moving to the Salk Institute in 1988 to pursue a PhD in computational neuroscience in Terry Sejnowski's laboratory. After a postdoc at UCLA with John Schlag in 1994, he became a professor at Georgetown University in 1996, then at the University of Rochester in the Brain and Cognitive Science department in 1999 before moving to the University of Geneva in 2011. Pouget was awarded the Carnegie Prize in Brain and Mind Sciences in 2016. He is the author of more than 100 papers and the editor of one book. He co-founded the Computational and Systems Neuroscience conference in 2004 with Anthony Zador. In 2016, he co-founded the International Brain Laboratory with Zachary Mainen and Michael Hausser, the first international CERN-like collaboration in systems neuroscience. His research focuses on general theories of representation and computation in neural circuits with a strong emphasis on neural theories of probabilistic inference.
Abstract of Talk
The neural representations of prior information about the state of the world are poorly understood. To investigate this issue, we examined brain-wide Neuropixels recordings and widefield calcium imaging collected by the International Brain Laboratory. Mice were trained to indicate the location of a visual grating stimulus, which appeared on the left or right with prior probability alternating between 0.2 and 0.8 in blocks of variable length. We found that mice estimate this prior probability and thereby improve their decision accuracy. Furthermore, we report that this subjective prior is encoded in at least 20% to 30% of brain regions which, remarkably, span all levels of processing, from early sensory areas (LGd, VISp) to motor regions (MOs, MOp, GRN) and high level cortical regions (ACCd, ORBvl). This widespread representation of the prior is consistent with a neural model of Bayesian inference involving loops between areas, as opposed to a model in which the prior is incorporated only in decision making areas. This study offers the first brain-wide perspective on prior encoding at cellular resolution, underscoring the importance of using large scale recordings on a single standardized task.