Publications
[Preprints]
[Journal Papers]
[Conference Papers]
[Invited Talks]
[Conference Talks]
[Abstracts ]
* denotes equal contribution as first authors† denotes equal contribution as senior authors
Preprints
A theory of brain-computer interface learning via low-dimensional control.J.A. Menendez, J.A. Hennig, M.D. Golub, E.R. Oby, P.T. Sadtler, A.P. Batista, S.M. Chase, B.M. Yu, and P.E. Latham.
bioRxiv, 2024.
Journal Papers
Learning leaves a memory trace in motor cortex.D.M. Losey*, J.A. Hennig*, E.R. Oby*, M.D. Golub, P.T. Sadtler, K.M. Quick, S.I. Ryu, E.C. Tyler-Kabara, A.P. Batista†, B.M. Yu†, and S.M. Chase†.
Current Biology, 34(7):1519-1531, 2024.
[ pdf ] [ bioRxiv version ]
Cortical preparatory activity indexes learned motor memories.
X. Sun*, D.J. O’Shea*, M.D. Golub, E.M. Trautmann, S. Vyas, S.I. Ryu, and K.V. Shenoy.
Nature, 602:274–279, 2022.
[ Blog Post ] [ pdf ] [ bioRxiv version ]
Learning is shaped by abrupt changes in neural engagement.
J.A. Hennig, E.R. Oby, M.D. Golub, L.A. Bahureksa, P.T. Sadtler, K.M. Quick, S.I. Ryu, E.C. Tyler-Kabara, A.P. Batista†, S.M. Chase†, and B.M. Yu†.
Nature Neuroscience, 24(5):727-736, 2021.
Computation through neural population dynamics.
S. Vyas, M.D. Golub, D. Sussillo, and K.V. Shenoy.
Annual Reviews Neuroscience, 43:249-275, 2020.
New neural activity patterns emerge with long-term learning.
E.R. Oby, M.D. Golub, J.A. Hennig, A.D. Degenhart, E.C. Tyler-Kabara, B.M. Yu†, S.M. Chase†, and A.P. Batista†.
Proceedings of the National Academy of Sciences, 116(30):15210-15215, 2019.
[ Supp. mats. ]
FixedPointFinder: A Tensorflow toolbox for identifying and characterizing fixed points in recurrent neural networks.
M.D. Golub and D. Sussillo.
Journal of Open Source Software, 3(31):1003, 2018.
[ Python code ]
Constraints on neural redundancy.
J.A. Hennig, M.D. Golub, P.J. Lund, P.T. Sadtler, E.R. Oby, K.M. Quick, E.R. Oby, S.I. Ryu, E.C. Tyler-Kabara, A.P. Batista†, S.M. Chase†, and B.M. Yu†.
eLife, 7:e36774, 2018.
[ Spotlight, Trends in Cognitive Sciences ]
Computation through cortical dynamics.
L.N. Driscoll, M.D. Golub, and D. Sussillo.
Neuron (Preview), 98(5):873-875, 2018.
Learning by neural reassociation.
M.D. Golub, P.T. Sadtler, K.M. Quick, E.R. Oby, S.I. Ryu, E.C. Tyler-Kabara, A.P. Batista, S.M. Chase†, and B.M. Yu†.
Nature Neuroscience, 21(4):607-616, 2018.
[ News and views ] [ Supp. mats. ] [ Matlab code ]
Brain-computer interfaces for dissecting cognitive processes underlying sensorimotor control.
M.D. Golub, S.M. Chase, A.P. Batista, and B.M. Yu.
Current Opinion in Neurobiology, 37:53-58, 2016.
Internal models for interpreting neural population activity during sensorimotor control.
M.D. Golub, B.M. Yu†, and S.M. Chase†.
eLife, 4:e10015, 2015.
[ Matlab code ]
Neural constraints on learning.
P.T. Sadtler, K.M. Quick, M.D. Golub, S.M. Chase, S.I. Ryu, E.C. Tyler-Kabara, B.M. Yu†, and A.P. Batista†.
Nature, 512:423-426, 2014.
Motor cortical control of movement speed with implications for brain-machine interface control.
M.D. Golub, B.M. Yu, A.B. Schwartz, and S.M. Chase.
J Neurophysiology, 11:411-429, 2014.
Conference Papers
Active learning of neural population dynamics using two-photon holographic optogenetics.A. Wagenmaker*, L. Mi*, M. Rozsa, M.S. Bull, K. Svoboda, K. Daie†, M.D. Golub†, and K. Jamieson†.
Advances in Neural Information Processing, 2024 (in press).
Universality and individuality in neural dynamics across large populations of recurrent networks.
N. Maheswaranathan*, A. Williams*, M.D. Golub, S. Ganguli, and D. Sussillo.
Advances in Neural Information Processing, 15603-15615, 2019. Spotlight Talk.
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics.
N. Maheswaranathan*, A. Williams*, M.D. Golub, S. Ganguli, and D. Sussillo.
Advances in Neural Information Processing, 15670-15679, 2019.
Learning an internal dynamics model from control demonstration.
M.D. Golub, S.M. Chase†, and B.M. Yu†.
International Conference on Machine Learning, 28(1):606-614, 2013. Talk.
Internal models engaged by brain-computer interface control.
M.D. Golub, S.M. Chase†, and B.M. Yu†.
IEEE Engineering in Medicine and Biology Society, 1327-1330, 2012. Talk.
Invited Talks
Active learning of neural population dynamics.van Vreeswijk Theoretical Neuroscience Seminar, online, 2025.
Identifying computations from neural population dynamics.
Advances and challenges in AI/ML for neurotechnologies, University of Washington. Seattle, WA, 2022.
Reverse engineering computations in the brain.
Allen Institute for Neural Dynamics, External Seminar. Seattle, WA, 2022.
University of Washington, Computer Science & Engineering. Seattle, WA, 2022.
University of California Berkeley, EECS & Neuroscience. Berkeley, CA, 2022.
Princeton University, Neuroscience. Princeton, NJ, 2022.
University of Chicago, Statistics & Data Science. Chicago, IL, 2022.
Yale University, Neuroscience. New Haven, CT, 2022.
University of California Los Angeles, Neuroscience. Los Angeles, CA, 2022.
Conference Talks
Separation of preparatory neural states when learning multiple arm-movement dynamics.X. Sun, D.J. O'Shea, M.D. Golub, E.M. Trautmann, S.I. Ryu, and K.V. Shenoy.
Computational and Systems Neuroscience (COSYNE), 2020.
Evidence of a memory trace in motor cortex after short-term learning.
D.M. Losey, J.A. Hennig, E.R. Oby, M.D. Golub, P.T. Sadtler, K.M. Quick, S.I. Ryu, E.C. Tyler-Kabara, A.P. Batista†, B.M. Yu†, and S.M. Chase†.
Computational and Systems Neuroscience (COSYNE), 2020.
Joint neural-behavioral models of perceptual decision making.
M.D. Golub.
Simons West Coast Postdoc Meeting, 2019.
Constraining recurrent neural networks to find dynamical solutions that look like the brain.
M.D. Golub.
Computational and Systems Neuroscience (COSYNE)} workshop: Data, dynamics and computation: using data-driven methods to ground mechanistic theory, 2019.
Within-manifold changes in population activity during learning.
M.D. Golub.
Simons workshop: Manifold Discovery in Neural Data, 2018.
Systematic changes of neural population activity during curl force field adaptation.
X. Sun, D.J. O'Shea, T. Fisher, M.D. Golub, S.I. Ryu, and K.V. Shenoy.
Advances in Motor Learning and Motor Control, 2018.
Learning can generate new patterns of neural population activity.
E.R. Oby, M.D. Golub, J.A. Hennig, A. Degenhart, E.C. Tyler-Kabara, B.M. Yu, S.M. Chase, and A.P. Batista.
Computational and Systems Neuroscience (COSYNE), 2018.
Population-level changes in neural activity during learning.
M.D. Golub, P.T. Sadtler, K.M. Quick, S.I. Ryu, E.C. Tyler-Kabara, A.P. Batista, S.M. Chase†, and B.M. Yu†.
Society for Neuroscience (SfN) Nanosymposium: Learning to reach, 2016.
Changes in neural population activity during learning.
M.D. Golub, P.T. Sadtler, K.M. Quick, S.I. Ryu, E.C. Tyler-Kabara, A.P. Batista, S.M. Chase†, and B.M. Yu†.
Computational and Systems Neuroscience (COSYNE) Workshop: Sensorimotor Learning Through Multidimensional Spaces, 2016.
Neural constraints on learning.
P.T. Sadtler, K.M. Quick, M.D. Golub, S.I. Ryu, B.M. Yu†, and A.P. Batista†.
Computational and Systems Neuroscience (COSYNE), 2014.
Neural co-modulation structure underlies BCI learning.
P.T. Sadtler, K.M. Quick, M.D. Golub, S.I. Ryu, B.M. Yu†, and A.P. Batista†.
International BCI Meeting, 2013.
Internal models engaged by brain-computer interface control.
M.D. Golub, B.M. Yu†, and S.M. Chase†.
Translational and Computational Motor Control, 2012.
Enhanced stability of cursor stopping in brain-computer interfaces.
M.D. Golub, B.M. Yu, A.B. Schwartz, and S.M. Chase.
IEEE Engineering in Medicine and Biology (EMBS), 2012.
Internal model estimation for closed-loop brain-computer interfaces.
M.D. Golub, S.M. Chase†, and B.M. Yu†.
Statistical Analysis of Neural Data (SAND) 6, 2012.
Abstracts
Overcoming non-identifiability issues in brain-wide communication models.B. Liu, J. Sacks, and M.D. Golub.
Computational and Systems Neuroscience (COSYNE), 2025 (upcoming).
Activity exploration influences learning speeds in models of brain-computer interfaces.
S. Mihalas, M. Bull, J. Sacks, M. Rozsa, C. Wang, K. Svoboda , M.D. Golub, K. Aitken, and K. Daie.
Computational and Systems Neuroscience (COSYNE), 2025 (upcoming).
Three-factor gradient-ascent approximation explains local-circuit plasticity during BCI learning.
K. Aitken, M. Rozsa, M. Bull, C. Wang, P. Humphreys, M. Eckstein, K. Stachenfeld, Z. Kurth-Nelson, L. Kinsey, M. Kulkarni, M. Botvinick, C. Clopath, T. Lillicrap, M.D. Golub, K. Svoboda, S. Mihalas, and K. Daie.
Computational and Systems Neuroscience (COSYNE), 2025 (upcoming).
Network models for distinguishing population-level learning mechanisms.
J. Sacks, E.R. Oby, J.A. Hennig, A.D. Degenhart, P.T. Sadtler, K.M. Quick, S.I. Ryu, E.C. Tyler-Kabara, S.M. Chase, B.M. Yu, A.P. Batista, and M.D. Golub.
Computational and Systems Neuroscience (COSYNE), 2024. Poster no. II-007.
Fast, sparse, and local learning in motor cortex.
M.S. Bull, M. Rozsa, L. Mi, P. Humphreys, M. Eckstein, K.L. Stachenfeld, Z. Kurth-Nelson, T. Lillicrap, C. Clopath, M.M. Botvinick, K. Svoboda, K. Daie†, and M.D. Golub†
Computational and Systems Neuroscience (COSYNE), 2024. Poster no. III-021.
Local circuit plasticity in motor cortex during learning
M.S. Bull, M. Rozsa, F. Lagzi, L. Mi, P. Humphreys, M. Eckstein, Z. Kurth-Nelson, K.L. Stachenfeld, T. Lillicrap, C. Clopath, M.M. Botvinick, K. Svoboda, K. Daie†, and M.D. Golub†
Society for Neuroscience (SfN), 2023. Poster no. 279.17.
Learning is shaped by an abrupt change in neural engagement.
J.A. Hennig, E.R. Oby, M.D. Golub, L.A. Bahureksa, P.T. Sadtler, K.M. Quick, S.I. Ryu, E.C. Tyler-Kabara, A.P. Batista†, S.M. Chase†, and B.M Yu BM†.
Computational and Systems Neuroscience (COSYNE), 2021. Virtual.
A motor cortical model of brain-machine interface learning, fast and slow.
J. Menendez, J.A. Hennig, M.D. Golub, E.R. Oby, A.P. Batista, S.M. Chase, B.M. Yu, and P. Latham.
Computational and Systems Neuroscience (COSYNE), 2020. Poster no. III-93.
Joint modeling of neural population dynamics and behavior in single-trial perceptual decision making
M.D. Golub, C. Chandrasekaran, W.T. Newsome, K.V. Shenoy, and D. Sussillo.
Society for Neuroscience (SfN), 2019. Poster no. 404.10.
Changes in neural population activity underlying the learning of novel arm dynamics
X. Sun, D.J. O'Shea, E.M. Trautmann, M.D. Golub, S. Vyas, T.G. Fisher, S. Ryu, and K.V. Shenoy.
Society for Neuroscience (SfN), 2019. Poster no. 582.02.
Evidence of a memory trace in motor cortex after short term learning
D.M. Losey, J.A. Hennig, E.R. Oby, M.D. Golub, P.T. Sadtler, K.M. Quick, S. Ryu, E.C. Tyler-Kabara, A.P. Batista†, B.M. Yu†, and S.M. Chase†.
Society for Neuroscience (SfN), 2019. Poster no. 670.25.
Joint neural-behavioral models of perceptual decision making.
M.D. Golub, C. Chandrasekaran, W.T. Newsome, K.V. Shenoy, and D. Sussillo.
Computational and Systems Neuroscience (COSYNE), 2019. Poster no. III-46.
Extracting universal algorithmic principles from large populations of recurrent networks.
N. Maheswaranathan, A. Williams, M.D. Golub, S. Ganguli, and D. Sussillo.
Computational and Systems Neuroscience (COSYNE), 2019. Poster no. III-51.
Systematic changes of neural population activity during curl force field adaptation and generalization.
X. Sun, D.J. O'Shea, E. Trautmann, M.D. Golub, T. Fisher, S.I. Ryu, and K.V. Shenoy.
Computational and Systems Neuroscience (COSYNE), 2019. Poster no. I-56.
Systematic changes of neural population activity during curl force field adaptation.
X. Sun, D.J. O'Shea, T. Fisher, M.D. Golub, S.I. Ryu, and K.V. Shenoy.
Society for Neuroscience (SfN), 2018. Poster no. 493.12.
Learning by neural reassociation.
M.D. Golub, P.T. Sadtler, K.M. Quick, S.I. Ryu, E.C. Tyler-Kabara, A.P. Batista, S.M. Chase†, and B.M. Yu†.
Computational and Systems Neuroscience (COSYNE), 2017. Poster no. II-1.
Predicting neural activity in behaviorally-irrelevant dimensions.
J.A. Hennig, M.D. Golub, P.J. Lund, P.T. Sadtler, K.M. Quick, S.I. Ryu, E.C. Tyler-Kabara, A.P. Batista, S.M. Chase†, and B.M. Yu†.
Computational and Systems Neuroscience (COSYNE), 2017. Poster no. II-14.
Predicting neural activity in behaviorally-irrelevant dimensions.
J.A. Hennig, M.D. Golub, P.J. Lund, P.T. Sadtler, K.M. Quick, S.I. Ryu, E.C. Tyler-Kabara, A.P. Batista, S.M. Chase†, and B.M. Yu†.
Society for Neuroscience (SfN), 2016. Poster no. 247.10.
Learning engages both high- and low-covariance modes of neural population activity.
M.D. Golub, P.T. Sadtler, K.M. Quick, S.I. Ryu, E.C. Tyler-Kabara, A.P. Batista, S.M. Chase†, and B.M. Yu†.
Computational and Systems Neuroscience (COSYNE), 2016. Poster no. II-25.
Internal models for interpreting neural population activity during sensorimotor control.
M.D. Golub, B.M. Yu†, and S.M. Chase†.
MIT McGovern Institute Symposium: Internal Models of Sensorimotor & Cognitive Function, 2015.
Internal models for interpreting neural population activity during sensorimotor control.
M.D. Golub, B.M. Yu†, and S.M. Chase†.
Computational and Systems Neuroscience (COSYNE), 2015. Poster no. I-39.
Neural co-modulation constrains motor learning.
P.T. Sadtler, K.M. Quick, M.D. Golub, S.I. Ryu, B.M. Yu†, and A.P. Batista†.
Society for Neuroscience (SfN), 2013. Poster no. 833.03.
Internal models engaged by brain-computer interface control.
M.D. Golub, B.M. Yu†, and S.M. Chase†.
Society for Neuroscience (SfN), 2012. Poster no. 275.10.
Internal model estimation for feedback control in brain-computer interfaces.
M.D. Golub, S.M. Chase†, and B.M. Yu†.
HHMI Machine Learning, Statistical Inference, and Neuroscience, 2012.
Internal model estimation for feedback control in brain-computer interfaces.
M.D. Golub, S.M. Chase†, and B.M. Yu†.
Computational and Systems Neuroscience (COSYNE), 2012. Poster no. I-8.
Improving cursor stops in closed-loop brain-computer interfaces by leveraging trajectory curvature.
M.D. Golub, B.M. Yu, A.B. Schwartz, and S.M. Chase.
Society for Neuroscience (SfN), 2011. Poster no. 142.18.