Topic: probabilistic priors

The view of the world through the lens of our senses is noisy and incomplete. Confronted with this uncertainty, the brain relies on knowledge about the natural world and the current task context to perceive and to act. The framework of Bayesian inference successfully formalizes this knowledge in terms of probabilistic priors, and perception/action as probabilistic inference. However, our understanding of how such priors are obtained, represented and used by the brain remains limited. Here we build upon the recent success in machine learning of “diffusion models” as a means of learning and usin