Saurabh Vyas

This study develops a behavioral paradigm and computational model to show that strategy discovery in motor learning involves systematic exploration followed by an 'aha' moment, outperforming alternative learning models.

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.

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.

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

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.

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.

This paper demonstrates reversible, temporally specific optogenetic inhibition of motor neurons and muscle activity in mice using a light-activated chloride channel.

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