| Neural constraints on learning PT Sadtler, KM Quick, MD Golub, SM Chase, SI Ryu, EC Tyler-Kabara, ... Nature 512, 423-426, 2014 | 668 | 2014 |
| Computation through neural population dynamics S Vyas, MD Golub, D Sussillo, KV Shenoy Annual review of neuroscience 43 (1), 249-275, 2020 | 487 | 2020 |
| Learning by neural reassociation MD Golub, PT Sadtler, ER Oby, KM Quick, SI Ryu, EC Tyler-Kabara, ... Nature Neuroscience 21 (4), 607-616, 2018 | 225 | 2018 |
| New neural activity patterns emerge with long-term learning ER Oby, MD Golub, JA Hennig, AD Degenhart, EC Tyler-Kabara, BM Yu, ... Proceedings of the National Academy of Sciences 116 (30), 15210-15215, 2019 | 201 | 2019 |
| Universality and individuality in neural dynamics across large populations of recurrent networks N Maheswaranathan, A Williams, M Golub, S Ganguli, D Sussillo Advances in neural information processing systems 32, 2019 | 133 | 2019 |
| Brain–computer interfaces for dissecting cognitive processes underlying sensorimotor control MD Golub, SM Chase, AP Batista, BM Yu Current Opinion in Neurobiology 37, 53-58, 2016 | 113 | 2016 |
| Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics N Maheswaranathan, A Williams, M Golub, S Ganguli, D Sussillo Advances in neural information processing systems 32, 2019 | 78 | 2019 |
| Motor cortical control of movement speed with implications for brain-machine interface control MD Golub, BM Yu, AB Schwartz, SM Chase Journal of neurophysiology 112 (2), 411-429, 2014 | 74 | 2014 |
| Constraints on neural redundancy JA Hennig, MD Golub, PJ Lund, PT Sadtler, ER Oby, KM Quick, SI Ryu, ... eLife 7, e36774, 2018 | 66 | 2018 |
| Cortical preparatory activity indexes learned motor memories X Sun, DJ O’Shea, MD Golub, EM Trautmann, S Vyas, SI Ryu, KV Shenoy Nature 602 (7896), 274-279, 2022 | 63* | 2022 |
| Internal models for interpreting neural population activity during sensorimotor control MD Golub, BM Yu, SM Chase eLife 4, e10015, 2015 | 63 | 2015 |
| Learning is shaped by abrupt changes in neural engagement JA Hennig, ER Oby, MD Golub, LA Bahureksa, PT Sadtler, KM Quick, ... Nature Neuroscience 24 (5), 727-736, 2021 | 53 | 2021 |
| Learning an internal dynamics model from control demonstration MD Golub, SM Chase, BM Yu International Conference on Machine Learning (ICML) 28 (1), 606-614, 2013 | 42 | 2013 |
| FixedPointFinder: A Tensorflow toolbox for identifying and characterizing fixed points in recurrent neural networks MD Golub, D Sussillo Journal of Open Source Software 3 (31), 1003, 2018 | 38 | 2018 |
| Internal models engaged by brain-computer interface control MD Golub, M Yu, Byron, SM Chase IEEE Engineering in Medicine and Biology Society, 1327-1330, 2012 | 31 | 2012 |
| Computation through cortical dynamics LN Driscoll, MD Golub, D Sussillo Neuron 98 (5), 873-875, 2018 | 14 | 2018 |
| Learning leaves a memory trace in motor cortex DM Losey, JA Hennig, ER Oby, MD Golub, PT Sadtler, KM Quick, SI Ryu, ... Current Biology 34 (7), 1519-1531. e4, 2024 | 12* | 2024 |
| Line attractor dynamics in recurrent networks for sentiment classification N Maheswaranathan, AH Williams, MD Golub, S Ganguli, D Sussillo International Conference on Machine Learning (ICML) Workshop: Deep Phenomena, 2019 | 2 | 2019 |
| A theory of brain-computer interface learning via low-dimensional control JA Menendez, JA Hennig, MD Golub, ER Oby, PT Sadtler, AP Batista, ... bioRxiv, 2024.04. 18.589952, 2024 | | 2024 |
| Method of incremental training to create new patterns of physiological control signals ER Oby, AP Batista, SM Chase, AD Degenhart, MD Golub, PT Sadtler, ... US Patent App. 17/343,050, 2021 | | 2021 |
Steven M. Chase, Ph.D.Carnegie Mellon UniversityVerified email at cmu.edu
