What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Neuroscience
- Statistics
His scientific interests lie mostly in Neuroscience, Brain–computer interface, Motor cortex, Premotor cortex and Electrophysiology.
In his papers, he integrates diverse fields, such as Neuroscience and Dynamics.
His Brain–computer interface study combines topics in areas such as Paralysis, Neuroplasticity, Decoding methods and Neural Prosthesis.
The Motor cortex study which covers Electromyography that intersects with Cortical neurons and Brain mapping.
His Premotor cortex research is multidisciplinary, incorporating perspectives in Motor planning, Motor control, Biological neural network, Primary motor cortex and Multielectrode array.
His Electrophysiology study combines topics from a wide range of disciplines, such as Representation and Cog.
His most cited work include:
- Context-dependent computation by recurrent dynamics in prefrontal cortex (905 citations)
- Neural population dynamics during reaching (827 citations)
- Stimulus onset quenches neural variability: a widespread cortical phenomenon (761 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Brain–computer interface, Neuroscience, Artificial intelligence, Motor cortex and Decoding methods.
His research in Brain–computer interface intersects with topics in Neural Prosthesis, Cursor, Human–computer interaction, Simulation and Kalman filter.
His study in Premotor cortex extends to Neuroscience with its themes.
As a part of the same scientific family, Krishna V. Shenoy mostly works in the field of Artificial intelligence, focusing on Neurophysiology and, on occasion, Neuroprosthetics.
The concepts of his Motor cortex study are interwoven with issues in Cognitive psychology, Neural activity and Electromyography.
In his research, Signal and Local field potential is intimately related to Speech recognition, which falls under the overarching field of Decoding methods.
He most often published in these fields:
- Brain–computer interface (31.79%)
- Neuroscience (31.43%)
- Artificial intelligence (27.50%)
What were the highlights of his more recent work (between 2018-2021)?
- Motor cortex (21.07%)
- Brain–computer interface (31.79%)
- Neuroscience (31.43%)
In recent papers he was focusing on the following fields of study:
Krishna V. Shenoy focuses on Motor cortex, Brain–computer interface, Neuroscience, Artificial intelligence and Decoding methods.
The Motor cortex study combines topics in areas such as Paralysis, Neural activity, Local field potential and Representation.
His Brain–computer interface research integrates issues from Wireless, Computer hardware, Communication channel, Sensory system and Neural decoding.
Many of his studies on Neuroscience apply to Premotor cortex as well.
His studies in Artificial intelligence integrate themes in fields like Machine learning, Computer vision and Pattern recognition.
The study incorporates disciplines such as Speech recognition and Categorical variable in addition to Decoding methods.
Between 2018 and 2021, his most popular works were:
- Accurate Estimation of Neural Population Dynamics without Spike Sorting. (77 citations)
- Computation Through Neural Population Dynamics. (43 citations)
- Hand Knob Area of Premotor Cortex Represents the Whole Body in a Compositional Way. (24 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Neuroscience
- Statistics
His primary areas of investigation include Neuroscience, Brain–computer interface, Motor cortex, Artificial intelligence and Premotor cortex.
His work in Neuroscience is not limited to one particular discipline; it also encompasses Dynamical systems theory.
His work deals with themes such as Curse of dimensionality, Motor control, Task, Function and Algorithm, which intersect with Brain–computer interface.
His Motor cortex research incorporates elements of Models of neural computation and Homunculus.
His Artificial intelligence research includes themes of Network dynamics and Pattern recognition.
His Premotor cortex study incorporates themes from Neural activity, Perception, Checkerboard and Moment.
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