Matthew D. Golub, Ph.D.
Affiliations: | 2009-2015 | Electrical & Computer Engineering | Carnegie Mellon University, Pittsburgh, PA |
| 2017-2022 | Electrical Engineering | Stanford University, Palo Alto, CA | |
| 2022- | Computer Science & Engineering | University of Washington, Seattle, Seattle, WA |
Area:
Machine learning for neuroscience; motor control, learning, decision making, brain-machine interfaces.Website:
https://homes.cs.washington.edu/~mgolub/Google:
"Matthew Golub"Mean distance: 15.51 (cluster 29) | S | N | B | C | P |
Parents
Sign in to add mentor| Steven M. Chase | grad student | 2009-2015 | Carnegie Mellon |
| Byron M. Yu | grad student | 2009-2015 | Carnegie Mellon |
| William T. Newsome | post-doc | 2017-2022 | Stanford |
| Krishna V. Shenoy | post-doc | 2017-2022 | Stanford |
| David Sussillo | post-doc | 2017-2022 | Stanford |
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Publications
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| Wagenmaker A, Mi L, Rozsa M, et al. (2024) Active learning of neural population dynamics using two-photon holographic optogenetics. Arxiv |
| Losey DM, Hennig JA, Oby ER, et al. (2024) Learning leaves a memory trace in motor cortex. Current Biology : Cb. 34: 1519-1531.e4 |
| Sun X, O'Shea DJ, Golub MD, et al. (2022) Cortical preparatory activity indexes learned motor memories. Nature |
| Hennig JA, Oby ER, Golub MD, et al. (2021) Learning is shaped by abrupt changes in neural engagement. Nature Neuroscience |
| Vyas S, Golub MD, Sussillo D, et al. (2020) Computation Through Neural Population Dynamics. Annual Review of Neuroscience. 43: 249-275 |
| Maheswaranathan N, Williams AH, Golub MD, et al. (2019) Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics. Advances in Neural Information Processing Systems. 32: 15696-15705 |
| Maheswaranathan N, Williams AH, Golub MD, et al. (2019) Universality and individuality in neural dynamics across large populations of recurrent networks. Advances in Neural Information Processing Systems. 2019: 15629-15641 |
| Oby ER, Golub MD, Hennig JA, et al. (2019) New neural activity patterns emerge with long-term learning. Proceedings of the National Academy of Sciences of the United States of America |
| Hennig JA, Golub MD, Lund PJ, et al. (2018) Constraints on neural redundancy. Elife. 7 |
| Golub MD, Sadtler PT, Oby ER, et al. (2018) Publisher Correction: Learning by neural reassociation. Nature Neuroscience |