S. Stavisky

发表

Neural Population Dynamics Underlying Motor Learning Transfer

K. Shenoy, P. Nuyujukian, S. Ryu, 2018, Neuron.

Neural ensemble dynamics in dorsal motor cortex during speech in people with paralysis

Francis R. Willett, Brian A. Murphy, Donald T. Avansino, 2019, eLife.

Neural Population Dynamics Underlying Motor Learning Transfer Highlights

K. Shenoy, P. Nuyujukian, S. Ryu, 2018 .

Trial-by-Trial Motor Cortical Correlates of a Rapidly Adapting Visuomotor Internal Model

Stephen I. Ryu, Krishna V. Shenoy, Sergey D. Stavisky, 2017, The Journal of Neuroscience.

Neural ensemble dynamics in dorsal motor cortex during speech in people with paralysis

Francis R. Willett, Krishna V. Shenoy, Jaimie M. Henderson, 2018, bioRxiv.

The need for calcium imaging in nonhuman primates: New motor neuroscience and brain-machine interfaces

Krishna V. Shenoy, Maneesh Sahani, Karl Deisseroth, 2017, Experimental Neurology.

IMAGING DISEASE MECHANISMS IN PEOPLE WITH ALS

S. Stavisky, A. Gleixner, J. McKeon, 2016 .

Making brain–machine interfaces robust to future neural variability

David Sussillo, K. Shenoy, S. Ryu, 2016, Nature Communications.

Decoding spoken English from intracortical electrode arrays in dorsal precentral gyrus

Francis R. Willett, Krishna V Shenoy, Sergey D Stavisky, 2020, Journal of neural engineering.

Decoding spoken English phonemes from intracortical electrode arrays in dorsal precentral gyrus

Francis R. Willett, Leigh R. Hochberg, Krishna V. Shenoy, 2020, bioRxiv.

Large-scale neural recordings with single neuron resolution using Neuropixels probes in human cortex

Sydney S. Cash, K. Shenoy, L. Hochberg, 2022, Nature Neuroscience.

Large-scale neural recordings with single-cell resolution in human cortex using high-density Neuropixels probes

Ziv M. Williams, K. Shenoy, S. Cash, 2021, bioRxiv.

Accurate Estimation of Neural Population Dynamics without Spike Sorting

Surya Ganguli, Stephen I. Ryu, Krishna V. Shenoy, 2017, Neuron.

The Neural Representation of Force across Grasp Types in Motor Cortex of Humans with Tetraplegia

Francis R. Willett, Leigh R. Hochberg, Krishna V. Shenoy, 2020, eNeuro.

Making brain–machine interfaces robust to future neural variability

David Sussillo, K. Shenoy, S. Ryu, 2016, Nature Communications.

Augmenting intracortical brain-machine interface with neurally driven error detectors

Krishna V Shenoy, Jonathan C Kao, Sergey D Stavisky, 2017, Journal of neural engineering.

Neural Point-and-Click Communication by a Person With Incomplete Locked-In Syndrome

Nicolas Y. Masse, Beata Jarosiewicz, Daniel Bacher, 2015, Neurorehabilitation and neural repair.

Inferring single-trial neural population dynamics using sequential auto-encoders

Chethan Pandarinath, David Sussillo, Matthew T. Kaufman, 2017, Nature Methods.

Information Systems Opportunities in Brain–Machine Interface Decoders

Krishna V. Shenoy, David Sussillo, Paul Nuyujukian, 2014, Proceedings of the IEEE.

A recurrent neural network for closed-loop intracortical brain–machine interface decoders

David Sussillo, Paul Nuyujukian, Jonathan C Kao, 2012, Journal of neural engineering.

Workshops of the Eighth International Brain-Computer Interface Meeting: BCIs: The Next Frontier.

James J. S. Norton, Aliceson Nicole Dusang, A. Nijholt, 2022, Brain computer interfaces.

Principled BCI Decoder Design and Parameter Selection Using a Feedback Control Model

Chethan Pandarinath, Beata Jarosiewicz, Krishna V Shenoy, 2019, Scientific Reports.

Rapid calibration of an intracortical brain–computer interface for people with tetraplegia

Chethan Pandarinath, Michael C. Burkhart, Tommy Hosman, 2018, Journal of neural engineering.

A high performing brain–machine interface driven by low-frequency local field potentials alone and together with spikes

Paul Nuyujukian, Krishna V Shenoy, Jonathan C Kao, 2015, bioRxiv.

Information Systems Opportunities in Brain-Machine Interface

Krishna V. Shenoy, David Sussillo, Paul Nuyujukian, 2014 .

Feasibility of Automatic Error Detect-and-Undo System in Human Intracortical Brain–Computer Interfaces

Chethan Pandarinath, Leigh R. Hochberg, Krishna V. Shenoy, 2018, IEEE Transactions on Biomedical Engineering.

System identification of brain-machine interface control using a cursor jump perturbation

Stephen I. Ryu, Krishna V. Shenoy, Sergey D. Stavisky, 2015, 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER).

Performance sustaining intracortical neural prostheses

Paul Nuyujukian, Krishna V Shenoy, Jonathan C Kao, 2014, Journal of neural engineering.

Francis R. Willett, Krishna V Shenoy, Sergey D Stavisky, 2020, Journal of neural engineering.

Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements

Christine H. Blabe, K. Shenoy, P. Nuyujukian, 2018, Scientific Reports.

Motor Cortical Visuomotor Feedback Activity Is Initially Isolated from Downstream Targets in Output-Null Neural State Space Dimensions

Stephen I. Ryu, Krishna V. Shenoy, Sergey D. Stavisky, 2017, Neuron.

Neural Representation of Observed, Imagined, and Attempted Grasping Force in Motor Cortex of Individuals with Chronic Tetraplegia

Douglas C. Crowder, K. Shenoy, S. Cash, 2020, Scientific Reports.

Continuous Control of the DLR Light-Weight Robot III by a Human with Tetraplegia Using the BrainGate2 Neural Interface System

John D. Simeral, Sami Haddadin, Patrick van der Smagt, 2010, ISER.

Power-saving design opportunities for wireless intracortical brain–computer interfaces

Boris Murmann, Leigh R. Hochberg, Krishna V. Shenoy, 2020, Nature Biomedical Engineering.

The Neuropixels probe: A CMOS based integrated microsystems platform for neuroscience and brain-computer interfaces

S. Severi, K. V. Shenoy, J. Putzeys, 2019, 2019 IEEE International Electron Devices Meeting (IEDM).

Decoding Speech from Intracortical Multielectrode Arrays in Dorsal “Arm/Hand Areas” of Human Motor Cortex

Francis R. Willett, Leigh R. Hochberg, Krishna V. Shenoy, 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Power-saving design opportunities for wireless intracortical brain–computer interfaces

K. Shenoy, L. Hochberg, J. Henderson, 2020, Nature Biomedical Engineering.

Principled BCI Decoder Design and Parameter Selection Using a Feedback Control Model

Christine H. Blabe, K. Shenoy, L. Hochberg, 2019, Scientific Reports.

Auto-deleting brain machine interface: Error detection using spiking neural activity in the motor cortex

Stephen I. Ryu, Krishna V. Shenoy, Sergey D. Stavisky, 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Hybrid decoding of both spikes and low-frequency local field potentials for brain-machine interfaces

Stephen I. Ryu, Krishna V. Shenoy, Paul Nuyujukian, 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Investigating the role of firing-rate normalization and dimensionality reduction in brain-machine interface robustness

Surya Ganguli, Stephen I. Ryu, Krishna V. Shenoy, 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

etecti on U sing Spiking Neural A ctivity in T he Motor Cortex

K. Shenoy, S. Ryu, S. Stavisky, 2015 .

Modified Neuropixels probes for recording human neurophysiology in the operating room

L. Hochberg, B. Dutta, R. Hardstone, 2023, Nature Protocols.

Accurate Estimation of Neural Population Dynamics without Spike Sorting

Matthew T. Kaufman, K. Shenoy, S. Ganguli, 2017, Neuron.

Reprint of “Non-causal spike filtering improves decoding of movement intention for intracortical BCIs”

Nicolas Y. Masse, John D. Simeral, Beata Jarosiewicz, 2015, Journal of Neuroscience Methods.

Hybrid decoding of both spikes and low-frequency local field potentials for brain-machine interfaces

K. Shenoy, P. Nuyujukian, S. Ryu, 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Non-causal spike filtering improves decoding of movement intention for intracortical BCIs

Nicolas Y. Masse, John D. Simeral, Beata Jarosiewicz, 2014, Journal of Neuroscience Methods.

A recurrent neural network for closed-loop intracortical brain–machine interface decoders

Joline M. Fan, David Sussillo, K. Shenoy, 2012, Journal of neural engineering.

A Recurrent Neural Network for Closed-loop Intracortical Brain–machine Interface Decoders

David Sussillo, P. Nuyujukian, S. Ryu, 2011 .

BRAND: A platform for closed-loop experiments with deep network models

Nicholas S. Card, Kevin L. Bodkin, L. Hochberg, 2023, bioRxiv.

Neural Representation of Observed, Imagined, and Attempted Grasping Force in Motor Cortex of Individuals with Chronic Tetraplegia

Francis R. Willett, Leigh R. Hochberg, Krishna V. Shenoy, 2020, Scientific Reports.