Krishna V. Shenoy
Cognitive Neuroscience · 2006–2021
A researcher in the Science corpus: 18 papers; trusts 51 researchers and is trusted by 40 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 234 citations into it; #1,092 of 10,852 by trust here, #403 by citations
- Cellular and Molecular Neuroscience — 130 citations into it; #902 of 2,998 by trust here, #330 by citations
- Artificial Intelligence — 93 citations into it; #13,724 of 35,345 by trust here, #16,166 by citations
- Electrical and Electronic Engineering — 53 citations into it; #10,002 of 16,539 by trust here, #6,070 by citations
- Biomedical Engineering — 33 citations into it
- Signal Processing — 21 citations into it
- Sensory Systems — 13 citations into it
- Statistical and Nonlinear Physics — 13 citations into it
Whom Krishna V. Shenoy trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Mark M. Churchland
- David Sussillo
- David John Cameron MacKay
- Christopher D. Harvey
- John P. Cunningham
- Geoffrey E. Hinton
- Alex Graves
- Diederik P. Kingma
- Simon Justin Julier
- Paul Cisek
Who trusts Krishna V. Shenoy overall
- Andrew Gordon Wilson
- John P. Cunningham
- Saurabh Vyas
- Logan Grosenick
- Nicolas Chapados
- Sue Ann Koay
- Rafael Yuste
- Guangyu Robert Yang
- György Buzsáki
- Jouni Hartikainen
Papers from Krishna V. Shenoy's perspective
Ordered by how strongly the researchers Krishna V. Shenoy trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Auto-Encoding Variational Bayes (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Fast Gaussian process methods for point process intensity estimation (2008) — 126 citations
- A high-performance brain–computer interface (2006) — 718 citations
- Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex (2011) — 350 citations
- A theory of multineuronal dimensionality, dynamics and measurement (2017) — 358 citations
- Single-Neuron Stability during Repeated Reaching in Macaque Premotor Cortex (2007) — 174 citations
- High-performance brain-to-text communication via handwriting (2021) — 871 citations
- Organizing recurrent network dynamics by task-computation to enable continual learning (2020) — 98 citations
- Computation Through Neural Population Dynamics (2020) — 657 citations
- Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis (2017) — 301 citations
- Inferring single-trial neural population dynamics using sequential auto-encoders (2017) — 800 citations
- High performance communication by people with paralysis using an intracortical brain-computer interface (2017) — 494 citations
- Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface (2015) — 336 citations
- Cortical control of arm movements: a dynamical systems perspective. (2013) — 810 citations
- Context-dependent computation by recurrent dynamics in prefrontal cortex (2013) — 1,875 citations
- Neural population dynamics during reaching (2012) — 1,632 citations
- Stimulus onset quenches neural variability: a widespread cortical phenomenon (2010) — 1,116 citations
- Expectation Propagation for Inference in Non-Linear Dynamical Models with Poisson Observations (2006) — 16 citations
- Functional, but Not Anatomical, Separation of "what" and "when" in Prefrontal Cortex Gaussian-process Factor Analysis for Low-dimensional Single-trial Analysis of Neural Population Activity