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
Learning the statistics of pain: computational and neural mechanisms
Mancini F, Zhang S & Seymour B (2021)
Learning the statistics of pain: computational and neural mechanisms
Mancini F, Zhang S & Seymour B (2021)
A multi-site, multi-disorder resting-state magnetic resonance image database.
Tanaka SC, Yamashita A, Yahata N, Itahashi T, Lisi G et al. (2021), Scientific data, 8(1), 227
Pain Control by Co-adaptive Learning in a Brain-Machine Interface.
Zhang S, Yoshida W, Mano H, Yanagisawa T, Mancini F et al. (2020), Current biology : CB, 30(20), 3935-3944.e7
Publisher Correction: Primary functional brain connections associated with melancholic major depressive disorder and modulation by antidepressants.
Ichikawa N, Lisi G, Yahata N, Okada G, Takamura M et al. (2020), Scientific reports, 10(1), 17650
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
Learning the statistics of pain: computational and neural mechanisms
Mancini F, Zhang S & Seymour B (2021)
Learning the statistics of pain: computational and neural mechanisms
Mancini F, Zhang S & Seymour B (2021)
A multi-site, multi-disorder resting-state magnetic resonance image database.
Tanaka SC, Yamashita A, Yahata N, Itahashi T, Lisi G et al. (2021), Scientific data, 8(1), 227
Pain Control by Co-adaptive Learning in a Brain-Machine Interface.
Zhang S, Yoshida W, Mano H, Yanagisawa T, Mancini F et al. (2020), Current biology : CB, 30(20), 3935-3944.e7
Publisher Correction: Primary functional brain connections associated with melancholic major depressive disorder and modulation by antidepressants.
Ichikawa N, Lisi G, Yahata N, Okada G, Takamura M et al. (2020), Scientific reports, 10(1), 17650
Publications
Most Recent Publications
Learning the statistics of pain: computational and neural mechanisms
Mancini F, Zhang S & Seymour B (2021)
Learning the statistics of pain: computational and neural mechanisms
Mancini F, Zhang S & Seymour B (2021)
A multi-site, multi-disorder resting-state magnetic resonance image database.
Tanaka SC, Yamashita A, Yahata N, Itahashi T, Lisi G et al. (2021), Scientific data, 8(1), 227
Pain Control by Co-adaptive Learning in a Brain-Machine Interface.
Zhang S, Yoshida W, Mano H, Yanagisawa T, Mancini F et al. (2020), Current biology : CB, 30(20), 3935-3944.e7
Publisher Correction: Primary functional brain connections associated with melancholic major depressive disorder and modulation by antidepressants.
Ichikawa N, Lisi G, Yahata N, Okada G, Takamura M et al. (2020), Scientific reports, 10(1), 17650
