Biography
Ben Seymour is a Wellcome Senior Clinical Fellow at Oxford University, working jointly at the Wellcome Centre for Integrative Neuroimaging and the Oxford Institute for Biomedical Engineering; and a visiting researcher at the Center for Information and Neural Networks (National Institute of Information and Communications Technology) in Osaka, and ATR labs (Kyoto).
Awards and Prizes
- Turing Fellow
- Fellow of the Royal Society of Arts
Most Recent Publications
Evidence for dopaminergic involvement in endogenous modulation of pain relief.
Desch S, Schweinhardt P, Seymour B, Flor H & Becker S (2023), Elife, 12
Confidence of probabilistic predictions modulates the cortical response to pain.
Mulders D, Seymour B, Mouraux A & Mancini F (2023), Proc Natl Acad Sci U S A, 120(4), e2212252120
Post-injury pain and behaviour: a control theory perspective
Seymour B, Crook RJ & Chen ZS (2023), NATURE REVIEWS NEUROSCIENCE
Computational and neural mechanisms of statistical pain learning.
Mancini F, Zhang S & Seymour B (2022), Nature communications, 13(1), 6613
Confidence of probabilistic predictions modulates the cortical response to pain
Mulders D, Seymour B, Mouraux A & Mancini F (2022)
Research Interests
The lab addresses the computational and systems neuroscience of pain. This research is part theoretical: building realistic models of neuronal information processes to understand processes of pain perception and behaviour, and part experimental: testing these theories using a range of experimental methodologies, especially fMRI. The research aims to develop new technology-based therapies for treating pain in clinical populations, and looks to develop broader applications of brain inspired ‘safe learning systems’ in AI and robotics.
Current Projects
• UKRI Neurotechnology Network for Chronic pain (2022-2025). With colleagues from Glasgow, Cardiff, Plymouth and Exeter, this project lays a foundation for the development of state-of-the-art clinical neuroengineering and neurotechnology for pain and associated symptoms of fatigue and disability.
• Versus/MRC The Role of Learning in Chronic Musculoskeletal Pain (2022-2025). With colleagues in Cambridge, this project develops digital technologies for understanding how maladaptive cognitive and motor learning play a role in the pathogenesis of chronic pain.
• Wellcome Neural Mechanisms of Endogenous Analgesia (2020-2025). This project aims to identify how the brain switches off pain in various situations, using computational models of pain, and neuroimaging.
• IITP/KAIST Developing Safe AI systems (2018-2022). With colleagues in KAIST (South Korea), this project explores how to design adaptive autonomous control systems that prioritize safety.
• Versus/NICT Pain, Action, Movement Network (2017-2022). With colleagues in Japan, this project builds a collaborative network for studying the role of movement in pain.
Research Groups
Related Academics
Most Recent Publications
Evidence for dopaminergic involvement in endogenous modulation of pain relief.
Desch S, Schweinhardt P, Seymour B, Flor H & Becker S (2023), Elife, 12
Confidence of probabilistic predictions modulates the cortical response to pain.
Mulders D, Seymour B, Mouraux A & Mancini F (2023), Proc Natl Acad Sci U S A, 120(4), e2212252120
Post-injury pain and behaviour: a control theory perspective
Seymour B, Crook RJ & Chen ZS (2023), NATURE REVIEWS NEUROSCIENCE
Computational and neural mechanisms of statistical pain learning.
Mancini F, Zhang S & Seymour B (2022), Nature communications, 13(1), 6613
Confidence of probabilistic predictions modulates the cortical response to pain
Mulders D, Seymour B, Mouraux A & Mancini F (2022)
Publications
Most Recent Publications
Evidence for dopaminergic involvement in endogenous modulation of pain relief.
Desch S, Schweinhardt P, Seymour B, Flor H & Becker S (2023), Elife, 12
Confidence of probabilistic predictions modulates the cortical response to pain.
Mulders D, Seymour B, Mouraux A & Mancini F (2023), Proc Natl Acad Sci U S A, 120(4), e2212252120
Post-injury pain and behaviour: a control theory perspective
Seymour B, Crook RJ & Chen ZS (2023), NATURE REVIEWS NEUROSCIENCE
Computational and neural mechanisms of statistical pain learning.
Mancini F, Zhang S & Seymour B (2022), Nature communications, 13(1), 6613
Confidence of probabilistic predictions modulates the cortical response to pain
Mulders D, Seymour B, Mouraux A & Mancini F (2022)
