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Peter Dayan

Neuroscience Symposium
Willuhn Group

The Neuroscience Symposia are organized weekly by the Netherlands Institute for Neuroscience. The presentations are given by researchers from the institute or by guest speakers. The title and content of the symposium is usually made known in the week prior to the presentation.

The symposium will be held online. A zoom link will be provided by email.

Guest speaker Ingo Willuhn

4:00 pm – Replay and Preplay in Human Planning

Peter Dayan read Mathematics at Cambridge, studied for his PhD with David Willshaw in Edinburgh, and did postdocs with Terry Sejnowski at the Salk Institute and Geoff Hinton in Toronto. He was an assistant professor in the Department of Brain and Cognitive Sciences at MIT, and was a founding faculty member of the Gatsby Computational Neuroscience Unit at UCL, which he then ran for 15 years. He is currently a director at the Max Planck Institute for Biological Cybernetics and a Professor at the University of Tübingen. His interests include affective decision making and neural reinforcement learning.

Abstract
Animals and humans replay neural patterns encoding trajectories through their environment, both whilst they solve decision-making tasks and during rest. This has been considered part of a general model inversion strategy. We have been using magnetoencephalography to detect replay in human subjects as they perform decision-making tasks. Having described the basic phenomenon, I will discuss a task in which we found that replay differed significantly between subjects who flexibly adjusted their choices to changes in temporal, spatial and reward structure and those who were slower to adapt to change. The former group predominantly replayed comparatively less good trajectories during task performance, and subsequently avoided these inefficient choices. The latter replayed comparatively preferred, but suboptimal, trajectories during rest periods between task epochs. We suggest that online and offline replay both contribute to planning, but each is associated with distinct model-based and model-free decision strategies.