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SUMMARY:A learning-based approach to artificial proprioception
DTSTART:20150202T140000
DTEND:20150202T150000
DTSTAMP:20260505T021223Z
UID:5276e405246c3518162947434dfc746e5723a9aed56c3e30c80dd6cb
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Philip Sabes\, University of California San Francisco\,
  USA.\nProprioception—the sense of the body's position in space—is imp
 ortant to natural movement planning and execution and will likewise be nec
 essary for successful motor prostheses and brain–machine interfaces (BMI
 s).  I will present our recent work on the development of a learning-base
 d approach to delivering artificial proprioceptive feedback. \nThis work 
 is motivated by the theoretical observation that movement planning and con
 trol rely on information from multiple sensory modalities\, and that these
  signals are combined in a statistically optimal and highly adaptive manne
 r.  We have shown how a simple network model can learn to perform such mu
 ltisensory processing\, driven only by the common statistics of its inputs
 \, e.g.\, by spatiotemporal correlations between sensory modalities.  Whe
 n then demonstrated that the same principle can be used to train animals t
 o use an artificial sensory signal.  In particular\, we paired known visu
 al feedback with an initially unfamiliar (and non-biomimetic) multichannel
  intracortical microstimulation signal that provided continuous informatio
 n about hand position relative to an unseen target. After learning\, the a
 nimals were able to use this signal to guide naturalistic movements. Furth
 ermore\, they combined the artificial signal with vision to form an optima
 l estimate of hand position. These results demonstrate that a learning-bas
 ed approach can be used to provide a rich artificial sensory feedback sign
 al\, suggesting a new strategy for restoring proprioception to patients us
 ing BMIs\, as well as a powerful new tool for studying the adaptive mechan
 isms of sensory integration.\nBio: Dr. Philip Sabes is Professor of Physio
 logy at the University of California\, San Francisco. He is also the direc
 tor of the UCSF Swartz Center for Theoretical Neurobiology. Dr. Sabes' lab
 oratory works toward understanding how the brain controls movement\, and i
 n particular the role of sensory information and learning. His lab is also
  using this understanding to develop Brain Machine Interfaces to help peop
 le with severe sensory and motor loss\, such as spinal cord injury. Dr. Sa
 bes was a Marshall Scholar before earning his PhD in Brain and Cognitive S
 ciences at MIT. He was a Sloan Research Fellow and a McKnight Scholar. He 
 and two members of his lab were awarded the 2013 Annual BCI (Brain Compute
 r Interface) Research Award for their work on the development of artificia
 l somatosensory feedback. He currently holds the Jack D. and DeLoris Lange
  Endowed Chair in Cell Physiology.
LOCATION:SV1717a http://plan.epfl.ch/?room=SV%201717A
STATUS:CONFIRMED
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