Our goal is to uncover general principles of representation and computation in neural circuits. Our current research is focused primarily on exploring the neural basis of probabilistic inference. The main idea, inspired by the mathematician Laplace more than 2 centuries ago, is that neurons represent knowledge in the form of probability distributions and acquire new knowledge by following the rules of probabilistic inference. The advantages of this probabilistic approach are twofold: 1- it provides a powerful, sometimes optimal, approach to computation in the presence of uncertainty, as is almost always the case in natural world computation, and 2- the same computational principles can be applied to a wide range of problems and could provide a very general theory of neural computation across all species. We have applied or are currently exploring the implications of this theory in the context of odor recognition, navigation, motor control, decision making, multisensory integration, visual search, simple arithmetic and causal reasoning in humans. We also collaborate with several laboratories to test the experimental predictions of this general framework.
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