Mark M. Churchland
Affiliations: | Stanford University, Palo Alto, CA |
Area:
Motor CortexGoogle:
"Mark Churchland"Mean distance: 13.6 (cluster 17) | S | N | B | C | P |
Parents
Sign in to add mentor| Stephen G. Lisberger | grad student | 2001 | UCSF | |
| (Reconstructing visual speed: Behavior, perception, models, and the neural basis of an illusion of increased speed.) | ||||
| Krishna V. Shenoy | post-doc | 2001-2011 | Stanford | |
Children
Sign in to add trainee| Elom A Amematsro | grad student | Columbia | |
| Abigail A Russo | grad student | 2013- | |
| Francisco Sacadura | grad student | 2021- | Columbia |
| Karen E. Schroeder | post-doc | Columbia | |
| Cory R. Hussar | post-doc | 2012- | Columbia |
Collaborators
Sign in to add collaborator| Brian D. DePasquale | collaborator | ||
| Matthew T. Kaufman | collaborator | 2006- | Stanford |
| David Ferster | collaborator | 2010- | Northwestern |
BETA: Related publications
See more...
Publications
|
You can help our author matching system! If you notice any publications incorrectly attributed to this author, please sign in and mark matches as correct or incorrect. |
| Zimnik AJ, Ames KC, An X, et al. (2024) Identifying Interpretable Latent Factors with Sparse Component Analysis. Biorxiv : the Preprint Server For Biology |
| Windolf C, Yu H, Paulk AC, et al. (2023) DREDge: robust motion correction for high-density extracellular recordings across species. Biorxiv : the Preprint Server For Biology |
| Zhang Y, He T, Boussard J, et al. (2023) Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes. Biorxiv : the Preprint Server For Biology |
| Trautmann EM, Hesse JK, Stine GM, et al. (2023) Large-scale high-density brain-wide neural recording in nonhuman primates. Biorxiv : the Preprint Server For Biology |
| Churchland MM, Nuyujukian P. (2023) Krishna V. Shenoy (1968-2023). Nature Neuroscience |
| DePasquale B, Sussillo D, Abbott LF, et al. (2023) The centrality of population-level factors to network computation is demonstrated by a versatile approach for training spiking networks. Neuron |
| Marshall NJ, Glaser JI, Trautmann EM, et al. (2022) Flexible neural control of motor units. Nature Neuroscience |
| Saxena S, Russo AA, Cunningham J, et al. (2022) Motor cortex activity across movement speeds is predicted by network-level strategies for generating muscle activity. Elife. 11 |
| Schroeder KE, Perkins SM, Wang Q, et al. (2021) Cortical control of virtual self-motion using task-specific subspaces. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience |
| Zimnik AJ, Churchland MM. (2021) Independent generation of sequence elements by motor cortex. Nature Neuroscience. 24: 412-424 |