H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Engineering and Technology H-index 57 Citations 11,340 185 World Ranking 805 National Ranking 350
Neuroscience H-index 56 Citations 15,802 138 World Ranking 1715 National Ranking 827

Research.com Recognitions

Awards & Achievements

2016 - Fellow of the Indian National Academy of Engineering (INAE)

2002 - Fellow of Alfred P. Sloan Foundation

Overview

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Neural population dynamics during reaching

Mark M. Churchland;John P. Cunningham;John P. Cunningham;Matthew T. Kaufman;Justin D. Foster.
Nature (2012)

1049 Citations

Context-dependent computation by recurrent dynamics in prefrontal cortex

Valerio Mante;David Sussillo;Krishna V. Shenoy;William T. Newsome.
Nature (2013)

961 Citations

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)

895 Citations

A high-performance brain–computer interface

Gopal Santhanam;Stephen I. Ryu;Byron M. Yu;Afsheen Afshar.
Nature (2006)

784 Citations

Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity

Byron M Yu;John P Cunningham;Gopal Santhanam;Stephen I. Ryu.
neural information processing systems (2008)

523 Citations

Cortical control of arm movements: a dynamical systems perspective.

Krishna V. Shenoy;Maneesh Sahani;Mark M. Churchland.
Annual Review of Neuroscience (2013)

506 Citations

An optogenetic toolbox designed for primates

Ilka Diester;Matthew T Kaufman;Murtaza Mogri;Ramin Pashaie;Ramin Pashaie.
Nature Neuroscience (2011)

470 Citations

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)

439 Citations

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)

410 Citations

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)

401 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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