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Byron M. Yu
Researcher at Carnegie Mellon University
Publications - 105
Citations - 9431
Byron M. Yu is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Population & Brain–computer interface. The author has an hindex of 36, co-authored 98 publications receiving 7703 citations. Previous affiliations of Byron M. Yu include University College London & University of California, Berkeley.
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Decoding of neural signals for movement control
TL;DR: In this paper, a brain machine interface for decoding neural signals for control of a machine is provided, which combines information from two classes of neural activity: plan and peri-movement.
Posted ContentDOI
Feedforward and feedback interactions between visual cortical areas use different population activity patterns
João D. Semedo,Anna I. Jasper,Amin Zandvakili,Amir Aschner,Christian K. Machens,Adam Kohn,Byron M. Yu +6 more
TL;DR: In this paper, the authors investigate the way in which feedforward and feedback signaling interact with one another and find that feedforward-dominated interactions are feedforwarddominated shortly after stimulus onset and feedback-dominated during spontaneous activity.
Posted ContentDOI
Learning is shaped by abrupt changes in neural engagement
Jay A. Hennig,Emily R. Oby,Matthew D. Golub,Lindsay A Bahureksa,Patrick T. Sadtler,Kristin M. Quick,Stephen I. Ryu,Elizabeth C. Tyler-Kabara,Aaron P. Batista,Steven M. Chase,Byron M. Yu +10 more
TL;DR: It is found that neural engagement interacted with learning, helping to explain why animals learned some task goals more quickly than others and how these changes impacted behavioral performance for different task goals.
Proceedings ArticleDOI
Increasing the Performance of Cortically-Controlled Prostheses
Krishna V. Shenoy,Gopal Santhanam,Stephen I. Ryu,Afsheen Afshar,Byron M. Yu,Vikash Gilja,Michael D. Linderman,Rachel S. Kalmar,John P. Cunningham,Caleb Kemere,Aaron P. Batista,Mark M. Churchland,Teresa H. Meng +12 more
TL;DR: Some of the recent experimental and computational work aimed at establishing a principled design methodology to increase electrode-based cortical prosthesis performance to near theoretical limits are reviewed and results should substantially increase the clinical viability of cortical prostheses.
Proceedings ArticleDOI
Influence of movement speed on plan activity in monkey pre-motor cortex and implications for high-performance neural prosthetic system design
TL;DR: Pre-motor cortex neural activity from a rhesus monkey trained to perform delayed-reaches to targets at two different speeds is recorded and movement speed information is found in action potential emission rates and local field potential power during a "plan" period preceding movement.