Daniel J. O’Shea

The paper develops a predictive theory for experimental design in neural population geometry inference, showing that more neurons allow accurate inference with fewer trials.

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.

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.

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.

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 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.

ERAASR removes electrical stimulation artifacts from multielectrode array recordings, improving the detection of spiking activity in neural data.