Efficient Universal Computing Architectures for Decoding Neural Activity
The ability to decode neural activity into meaningful control signals for prosthetic devices is critical to the development of clinically useful brain- machine interfaces (BMIs). Such systems require input from tens to hundreds of brain-implanted recording electrodes in order to deliver robust and a...
Main Authors: | Rapoport, Benjamin I. (Contributor), Turicchia, Lorenzo (Contributor), Wattanapanitch, Woradorn (Author), Davidson, Thomas J. (Author), Sarpeshkar, Rahul (Contributor) |
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Other Authors: | Harvard University- (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor) |
Format: | Article |
Language: | English |
Published: |
Public Library of Science,
2012-11-14T15:07:17Z.
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Subjects: | |
Online Access: | Get fulltext |
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