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SUMMARY:High-Resolution Brain Machine Interfaces using Flexible Silicon El
 ectronics
DTSTART:20190618T121500
DTEND:20190618T131500
DTSTAMP:20260407T045532Z
UID:614eea2e787bcf8893642be1e0d936caa3771a16aa7c40c41962462e
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof Jonathan Viventi\, Duke University\, Durham\, USA.\nRight
  now\, all of the tools that interface with our brains face a fundamental 
 trade-off. We can either sample with low resolution\, over large areas of 
 the brain\, or we can sample with fine resolution\, over very small areas 
 of the brain. This doesn’t fit with the way our brains are structured. W
 ith over 12 million neurons in each square cm of brain surface\, we need t
 o sample with high resolution over large areas in order to understand the 
 way the brain works. The limitation is wiring. Every contact we put in the
  brain requires an individual wire and we can’t fit more than about 100 
 wires inside our heads. Using the same electronics that enable a digital c
 amera to have millions of pixels without millions of wires\, we can move s
 ome of the signal processing right to the sensors\, allowing us to overcom
 e the wiring bottleneck. The challenge is that traditional electronics are
  rigid and brittle. They are not compatible with the soft\, curved surface
 s of the brain. The solution is to make electronics that are flexible. Thi
 nk of a piece of 2x4 lumber and a sheet of paper\, they’re both made out
  of the same material\, but have dramatically different physical propertie
 s. Leveraging that idea\, we can make electronics that are extremely flexi
 ble\, by making them very thin. Using these flexible electronics\, I have 
 developed high-density electrode arrays with thousands of electrodes that 
 do not require thousands of external wires.\nThis technology has enabled e
 xtremely flexible arrays of 1\,024 electrodes and soon\, thousands of mult
 iplexed and amplified sensors spaced as closely as 25 µm apart\, which ar
 e connected using just a few wires.  These devices yield an unprecedented
  level of spatial and temporal micro-electrocorticographic (µECoG) resolu
 tion for recording and stimulating distributed neural networks.  I will p
 resent the development of this technology and data from in vivo recordings
 .  I will also discuss how we are translating this technology for both re
 search and human clinical use. \n\nBio\nJonathan Viventi is an Assistant 
 Professor of Biomedical Engineering at Duke University. Dr. Viventi earned
  his Ph.D. in Bioengineering from the University of Pennsylvania and his M
 .Eng. and B.S.E. degrees in Electrical Engineering from Princeton Universi
 ty. Dr. Viventi's research applies innovations in flexible electronics\, l
 ow power analog circuits\, and machine learning to create new technology f
 or interfacing with the brain at a much finer scale and with broader cover
 age than previously possible. He creates new tools for neuroscience resear
 ch and technology to diagnose and treat neurological disorders\, such as e
 pilepsy. Using these tools\, he collaborates with neuroscientists and clin
 icians to explore the fundamental properties of brain networks in both hea
 lth and disease. His research program works closely with industry\, includ
 ing filing six patents and several licensing agreements. His work has been
  featured as cover articles in Science Translational Medicine and Nature M
 aterials\, and has also appeared in Nature Neuroscience\, the Journal of N
 europhysiology\, and Brain. For these achievements\, Dr. Viventi was selec
 ted to the 2014 MIT Technology Review “Top 35 Innovators Under 35” lis
 t\, the 2014 Popular Science “Brilliant 10” list and an NSF CAREER Awa
 rd.\n\n\nVideoconference: https://epfl.zoom.us/j/374216444\n\nStreamed to:
  Campus Biotech\, H8 Auditorium D
LOCATION:ME D2 1124 https://plan.epfl.ch/?room==MED%202%201124
STATUS:CONFIRMED
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