2017 KAIST Computational Psychiatry Seminar Series
Computational insight into neural information processing of learning and decision making
KAIST 바이오및뇌공학과에서는 2017년 교육/연구 국제화 관련 사업의 일환으로 개최하는
2017 KAIST Computational Psychiatry Seminar Series 네 번째 세미나를 다음과 같이 개최합니다.
Pain and aversive learning : from computational neuroscience to clinical neuroengineering
Prof. Ben Seymour
(University of Cambridge, UK; CiNet, Japan)
His research interests lie in the area of aversive learning and pain processing. He is the author of highly-cited papers (total citations>15000, h-index=43), including 4 in Nature, 4 in Science, 5 in Neuron, 2 in Nature (Rev) Neuroscience.
When : 11.16(Thu.) 16:30-18:00
Where : #207 (Auditorium), Yang Boon Soon building (E16-1),KAIST
Pain and aversive learning: from computational neuroscience to clinical neuroengineering.
Chronic pain is the leading global cause of disability, and it is widely accepted that central (brain) maladaptive processes play a fundamental role in the chronification of pain after injury. However, rather than there being a single neural pathway or risk factor underlying chronification, it is likely that a host of underlying mechanisms interact to produce the complex neurobehavioural phenotype that leads to chronic pain. But in most people pain is good, providing a highly effective system for teaching us to avoid harm throughout life. Understanding and treating chronic pain relies on first understanding how the good pain system work. I will present an engineering approach to the problem, and set out how we can develop a quantitative account of the individual learning operations (computations) involved in the healthy pain system. Based on this, it is possible to construct an integrated 'systems-level' model of pain as a complex biological system. Ultimately, this allows us to develop a bioengineering-based treatment approach that specifically targets brain-based learning mechanisms.
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