Efficient Universal Computing Architectures for Decoding Neural Activity

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...

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Bibliographic Details
Main Authors: Rapoport, Benjamin I., Turicchia, Lorenzo, Wattanapanitch, Woradorn, Davidson, Thomas J., Sarpeshkar, Rahul
Other Authors: Harvard University--MIT Division of Health Sciences and Technology
Format: Article
Language:en_US
Published: Public Library of Science 2012
Online Access:http://hdl.handle.net/1721.1/74634
https://orcid.org/0000-0003-0384-3786

Internet

http://hdl.handle.net/1721.1/74634
https://orcid.org/0000-0003-0384-3786

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