K.V. Shenoy

This study identifies a population code in primate premotor cortex that encodes dynamic choice formation, revealing an attractor mechanism for decision computation.

The paper introduces Neuropixels 1.0-NHP, a high-density electrode array for large-scale neural recording in nonhuman primates, enabling recording from over 3000 single neurons.

The paper shows that neural activity in human premotor cortex combines finger movements through pseudo-linear summation, with deviations that improve decoding performance.

This paper identifies dynamical motifs in recurrent neural networks that enable flexible multitask computation through modular reuse across different tasks.

This paper shows that recurrent neural networks (RNNs) can be adapted for real-time neuroprosthetic control by modifying their training data's temporal structure, enabling better performance than standard linear methods.

This study investigates how methylphenidate affects motor cortical neural activity and behavior in Rhesus monkeys, finding dose-dependent changes in reaction time, variability, and movement speed.

This paper demonstrates a high-performance speech neuroprosthesis that achieves low word error rates and high decoding speeds using intracortical recordings from a person with ALS.

This paper introduces a hidden Markov model for unsupervised recalibration of intracortical brain-computer interfaces, showing improved performance over long-term use.

Optogenetic and electrical perturbations in primate motor cortex during reaching show that movement-related neural activity is governed by a self-contained, low-dimensional dynamical subspace causally linked to reach kinematics.

This study uses a dynamical systems approach to show that initial neural states in PMd predict decision-related dynamics and behavior in a reaction time task.

This paper describes a new high-density Neuropixels probe enabling single-cell resolution recordings from over 200 cortical neurons in humans during surgery, identifying eight distinct single unit classes.

This study investigates how motor learning alters preparatory neural activity in monkeys, finding both task-specific and uniform shifts that may support motor memory retention.

This paper introduces WaveMAP, a method using non-linear dimensionality reduction and graph clustering to identify cell types in premotor cortex, revealing new physiological and functional diversity.

This paper demonstrates an optical brain-computer interface (oBCI) using dendritic calcium signals from rhesus macaques during motor tasks, achieving stable decoding of movement direction.

This paper demonstrates a brain-computer interface that decodes imagined handwriting movements into text at high speed and accuracy, closing the gap between BCI and able-bodied typing rates.

This study investigates how haptic feedback affects motor cortical control and iBCI performance in a tetraplegic participant.

The paper decodes real-time decision variables from motor cortex activity and shows their link to choice behavior and decision dynamics.

This study examines how force and grasp are represented in motor cortex of tetraplegic individuals, finding that grasp affects force decoding and that grasp is more accurately classified than force.

The paper introduces a high-fidelity musculoskeletal model of reaching movements that reproduces Fitts law without signal-dependent noise, suggesting the speed-accuracy tradeoff arises from motor planning variability and optimal control theory.

This study decodes 39 English phonemes from neural signals in the dorsal precentral gyrus using intracortical electrode arrays, achieving 33.9% classification accuracy with a recurrent neural network.

The paper investigates the representation of body movements in the motor cortex of tetraplegic individuals, finding a modular code that could improve brain-computer interfaces.

The paper records neural activity in the motor cortex of people with paralysis during speech and non-speaking movements, showing that neurons modulate during speech and revealing common neural dynamics between speech and arm movements.

This paper introduces a time warping framework to uncover precise spike-time patterns in neural recordings across different systems, revealing both expected and unexpected firing dynamics.

The paper shows that neural dynamics can be accurately estimated without spike sorting by using multi-unit threshold crossings, enabling new analyses and electrode array design.

The paper shows that decision-related activity in macaque PMd is linked to action selection, not perceptual processing, when specific stimulus-response associations are known.

This study investigates beta band activity in the premotor cortex of macaques during a visual reaction time task, finding frequency-dependent correlations with reaction time and laminar depth dependence.

The paper shows that model selection in neural data can be unreliable due to brittleness, where small data changes affect model rankings, and suggests ways to address this issue.

This paper introduces LFADS, a deep learning method to infer single-trial neural population dynamics from spiking data, demonstrating improved accuracy in predicting behavior and extracting dynamics across datasets.

This paper introduces tensor components analysis to uncover low-dimensional neural dynamics across multiple timescales in diverse datasets.

This study examines how decision variables are represented in neural populations of PMd and M1 during a motor task, showing differences in timing and strength between areas and replicating findings with a computational model.