2013 - Fellow of Alfred P. Sloan Foundation
Mark M. Churchland mainly investigates Neuroscience, Motor cortex, Electromyography, Premotor cortex and Motor control. Many of his research projects under Neuroscience are closely connected to Dynamics and Control algorithm with Dynamics and Control algorithm, tying the diverse disciplines of science together. The concepts of his Neural variability study are interwoven with issues in Cerebral cortex, Motor planning and Cortex.
Mark M. Churchland has researched Motor cortex in several fields, including Nerve net and Electroencephalography. The various areas that Mark M. Churchland examines in his Premotor cortex study include Biological neural network and Primary motor cortex. His work investigates the relationship between Motor control and topics such as Cortical neurons that intersect with problems in Cognitive psychology and Developmental psychology.
Mark M. Churchland mainly focuses on Neuroscience, Motor cortex, Artificial intelligence, Premotor cortex and Neural activity. He works mostly in the field of Neuroscience, limiting it down to topics relating to Motion and, in certain cases, Communication, as a part of the same area of interest. His Motor cortex research incorporates elements of Cognitive psychology, Set and Electromyography, Muscle activity.
His Artificial intelligence research focuses on subjects like Machine learning, which are linked to Visualization. As part of one scientific family, Mark M. Churchland deals mainly with the area of Premotor cortex, narrowing it down to issues related to the Brain–computer interface, and often Neural Prosthesis. Mark M. Churchland studied Primary motor cortex and Motor control that intersect with Cortical neurons and Supplementary motor area.
His primary areas of study are Motor cortex, Neuroscience, Control theory, Primary motor cortex and Motor control. His studies in Motor cortex integrate themes in fields like Sequence, Robust control, Trajectory and Set. His Set research incorporates themes from Premotor cortex, Artificial intelligence and Pattern recognition.
His work in the fields of Neuroscience, such as Microstimulation, intersects with other areas such as Population response. His Motor control research is multidisciplinary, incorporating elements of Feed forward, Control theory and Motor learning. His Muscle activity research includes elements of Contralateral hemisphere and Neural activity.
Mark M. Churchland spends much of his time researching Primary motor cortex, Supplementary motor area, Motor cortex, Neuroscience and Motor control. His Supplementary motor area study is associated with SMA*. The study incorporates disciplines such as Cued speech, Microstimulation and Macaque in addition to SMA*.
His Motor cortex study typically links adjacent topics like Muscle activity. Motor control is a subfield of Control theory that Mark M. Churchland explores. His study of Population response brings together topics like Contralateral hemisphere, Rhesus macaque and Neural activity.
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Neural population dynamics during reaching
Mark M. Churchland;John P. Cunningham;John P. Cunningham;Matthew T. Kaufman;Justin D. Foster.
Stimulus onset quenches neural variability: a widespread cortical phenomenon
Mark M. Churchland;Byron M. Yu;Byron M. Yu;John P. Cunningham;Leo P. Sugrue;Leo P. Sugrue.
Nature Neuroscience (2010)
Cortical control of arm movements: a dynamical systems perspective.
Krishna V. Shenoy;Maneesh Sahani;Mark M. Churchland.
Annual Review of Neuroscience (2013)
Cortical activity in the null space: permitting preparation without movement
Matthew T Kaufman;Mark M Churchland;Stephen I Ryu;Krishna V Shenoy.
Nature Neuroscience (2014)
A high-performance neural prosthesis enabled by control algorithm design
Vikash Gilja;Paul Nuyujukian;Cindy A Chestek;John P Cunningham;John P Cunningham.
Nature Neuroscience (2012)
Neural Variability in Premotor Cortex Provides a Signature of Motor Preparation
Mark M. Churchland;Byron M. Yu;Stephen I. Ryu;Gopal Santhanam.
The Journal of Neuroscience (2006)
A Central Source of Movement Variability
Mark M. Churchland;Afsheen Afshar;Krishna V. Shenoy.
Cortical Preparatory Activity: Representation of Movement or First Cog in a Dynamical Machine?
Mark M. Churchland;John P. Cunningham;John P. Cunningham;Matthew T. Kaufman;Stephen I. Ryu;Stephen I. Ryu.
A neural network that finds a naturalistic solution for the production of muscle activity
David Sussillo;Mark M Churchland;Matthew T Kaufman;Krishna V Shenoy.
Nature Neuroscience (2015)
Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex
Cynthia A Chestek;Vikash Gilja;Paul Nuyujukian;Justin D Foster.
Journal of Neural Engineering (2011)
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