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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.
Papers
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Journal ArticleDOI
Stimulus onset quenches neural variability: a widespread cortical phenomenon
Mark M. Churchland,Byron M. Yu,Byron M. Yu,John P. Cunningham,Leo P. Sugrue,Leo P. Sugrue,Marlene R. Cohen,Marlene R. Cohen,Greg S. Corrado,Greg S. Corrado,William T. Newsome,William T. Newsome,Andrew M. Clark,Paymon Hosseini,Benjamin B. Scott,David C. Bradley,Matthew A. Smith,Adam Kohn,Adam Kohn,J. Anthony Movshon,Katherine M. Armstrong,Tirin Moore,Steve W. C. Chang,Lawrence H. Snyder,Stephen G. Lisberger,Nicholas J. Priebe,Ian M. Finn,David Ferster,Stephen I. Ryu,Gopal Santhanam,Maneesh Sahani,Krishna V. Shenoy +31 more
TL;DR: In this article, the authors measured neural variability in 13 extracellularly recorded datasets and one intra-cellularly recorded dataset from seven areas spanning the four cortical lobes in monkeys and cats and found that stimulus onset caused a decline in neural variability.
Journal ArticleDOI
Dimensionality reduction for large-scale neural recordings.
John P. Cunningham,Byron M. Yu +1 more
TL;DR: This review examines three important motivations for population studies: single-trial hypotheses requiring statistical power, hypotheses of population response structure and exploratory analyses of large data sets, and practical advice about selecting methods and interpreting their outputs.
Journal ArticleDOI
A high-performance brain–computer interface
TL;DR: The design and demonstration, using electrode arrays implanted in monkey dorsal premotor cortex, of a manyfold higher performance BCI than previously reported are presented, indicating that a fast and accurate key selection system, capable of operating with a range of keyboard sizes, is possible.
Journal ArticleDOI
Neural constraints on learning
Patrick T. Sadtler,Kristin M. Quick,Matthew D. Golub,Steven M. Chase,Stephen I. Ryu,Elizabeth C. Tyler-Kabara,Byron M. Yu,Aaron P. Batista +7 more
TL;DR: The results suggest that the existing structure of a network can shape learning, and offer a network-level explanation for the observation that the authors are more readily able to learn new skills when they are related to the skills that they already possess.
Journal ArticleDOI
Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity
Byron M. Yu,John P. Cunningham,Gopal Santhanam,Stephen I. Ryu,Krishna V. Shenoy,Maneesh Sahani +5 more
TL;DR: In this article, Gaussian process factor analysis (GPFA) was proposed to combine smoothing and dimensionality reduction operations in a common probabilistic framework, and applied to the activity of 61 neurons recorded simultaneously in macaque premotor and motor cortices.