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SUMMARY:Tracking neural plasticity for brain machine interfaces
DTSTART:20141027T140000
DTEND:20141027T150000
DTSTAMP:20260407T002626Z
UID:001823eb2ebb95eaea4e240b25802ca9d298913f9187a2475a3aec76
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof Yiwen Wang\, Zhejiang University\, Hangzhou\, China.\nNe
 uroplasticity plays an important role in behavior learning and adaptation 
 to changes in the environment\, ranging from cellular level to large scale
  cortical remapping. In brain machine interfaces (BMI)\, neural signals ar
 e translated into control commands on the prosthetic devices directly. BMI
  users may benefit by adapting the neural activities during learning to ac
 hieve more complicated movement tasks. However\, the time-variant tuning o
 f the decoder introduces the nonstationary issue to the signal processing 
 of the neural activity\, which results in the decay of the decoding perfor
 mance over time. We are interested in observing and modeling the plasticit
 y of neural tuning in a computational manner\, which consequently contribu
 tes to the stable performance of brain machine interfaces. Collecting the 
 multi-channel neural spike trains from the primary motor cortex of a prima
 te\, we decode the slowly-changing neural tuning during the kinematic esti
 mation by dual Monte Carlo adaptive point process filtering. An alternativ
 e approach\, a reinforcement learning based technique is developed to inte
 rpret the time-variant neural activities\; this technique efficiently expl
 ores the high dimensional neural-state action space. Results show that the
  adaptive decoders could follow the nonstationary neural signals with more
  stable performance over multiple days.\nBio: Yiwen Wang received the B.S.
  and M.S. degrees from University of Science and Technology of China in 20
 01 and 2004 respectively. She received the Ph.D. degree from University of
  Florida\, USA in 2008. She then joined the Department of Electronics and 
 Computer Engineering as a Research Associate at the Hong Kong University o
 f Science and Technology\, Kowloon\, Hong Kong. In 2010\, she joined the f
 aculty of Qiushi Academy for Advanced Studies\, Zhejiang University\, Hang
 zhou\, China. She is currently an Associate Professor there. Her research
  interests are in neural decoding of brain-machine interfaces\, adaptive s
 ignal processing\, computational neuroscience\, and neuromorphic engineeri
 ng.
LOCATION:SV1717a http://plan.epfl.ch/?room=SV%201717A
STATUS:CONFIRMED
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