University of Cambridge > Talks.cam > Cambridge Neuroscience Interdisciplinary Seminars > Learning in pain: probabilistic inference and (mal)adaptive control.

Learning in pain: probabilistic inference and (mal)adaptive control.

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If you have a question about this talk, please contact Dr Dervila Glynn.

Theme: Adaptive Brain Computations

Pain is a major clinical problem affecting 1 in 5 people in the world. There are unresolved questions that urgently require answers to treat pain effectively, a crucial one being how the feeling of pain arises from brain activity. Computational models of pain consider how the brain processes noxious information and allow mapping neural circuits and networks to cognition and behaviour. To date, they have generally have assumed two largely independent processes: perceptual and/or predictive inference, typically modelled as an approximate Bayesian process, and action control, typically modelled as a reinforcement learning process. However, inference and control are intertwined in complex ways, challenging the clarity of this distinction. I will discuss how they may comprise a parallel hierarchical architecture that combines pain inference, information-seeking, and adaptive value-based control. Finally, I will discuss whether and how these learning processes might contribute to chronic pain.

Flavia Mancini is a neuroscientist leading the NoxLab within the Computational and Biological Learning (CBL) research unit at the Department of Engineering, University of Cambridge. Flavia combines behavioural, computational, and neuroimaging tools to understand pain perception and behaviour in humans. She trained at the University of Milan and University College London. Currently, she is a MRC Career Development Award fellow.

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This talk is part of the Cambridge Neuroscience Interdisciplinary Seminars series.

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