☰ Neurotree

Mark M. Churchland

Affiliations:

Stanford University, Palo Alto, CA

Area:

Motor Cortex

Google:

"Mark Churchland"

Mean distance: 13.6 (cluster 17)

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Parents

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

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

Brian D. DePasquale	collaborator
Matthew T. Kaufman	collaborator	2006-	Stanford
David Ferster	collaborator	2010-	Northwestern

BETA: Related publications

Publications

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Zhao Z, Yu H, Wisniewski DJ, et al. (2024) Formation of Anisotropic Conducting Interlayer for High-Resolution Epidermal Electromyography Using Mixed-Conducting Particulate Composite. Advanced Science (Weinheim, Baden-Wurttemberg, Germany). e2308014
Churchland MM, Shenoy KV. (2024) Preparatory activity and the expansive null-space. Nature Reviews. Neuroscience. 25: 213-236
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

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