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Dynamic probabilistic inference in the brain


Anna Kutschireiter | Harvard Medical School, Cambridge, USA
Jan Drugowitsch | Harvard Medical School, Cambridge, USA


Every day, our brain needs to make sense of the rich, dynamical stream of sensory
inputs, and combine it with prior knowledge about its environment. Ample behavioral
evidence suggests that the brain’s processing of information conforms to the rules of
probabilistic inference. Most of this evidence came from static trial-by-trial experiments
that do not reflect the dynamic nature of our environment, leading to simplified and
rather restricted models of how our brains perform such inference. The aim of this
workshop is to look beyond such simplified, static models of inference, and ask how the
brain could perform the continuous-time dynamic inference required to operate in
natural environments. Such inference needs to span the range of synapses learning
environmental regularities, over the efficient and effective processing of dynamic and
continuously changing sensory inputs, to applying continuous-time control in order to
act upon the world’s inferred state. Recent experiments have started moving towards
more natural behaviors and as such provide the ideal benchmark to test the emerging
models against.

We will bring together researchers working on models of dynamical inference in the brain,
ranging from inference and learning on the level of synapses, single neurons and neuronal
networks to predictions of optimal strategies and behavior, as well as on experiments to test these predictions. More precisely, the goal is to provide a forum to discuss recent developments on all these levels and consider the implications of adding a dynamic component to the usually static inference. By spanning a wide range of research areas, the workshop should appeal to the broad audience attending the main conference.

Workshop schedule
session 1: choice of representation
session 2: synapse level. learning
session 3: behaviour & decision making
session 4: perceptual inference & efficient coding