Eric M. Trautmann

This study shows that motor cortex activity is high-dimensional and flexible, with distinct neural dynamics for different subskills during a force-tracking task.

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.

DREDge improves motion correction in high-density extracellular recordings across species, enhancing reliability and reducing errors in electrophysiological data.

The paper directly observes the neural computations underlying a single decision by analyzing population activity in monkey lateral intraparietal cortex during motion direction decisions.

This paper introduces a spike sorting-free decoding method using density-based modeling to improve neural decoding performance without explicitly clustering spikes.

This study identifies a neural mechanism in the primate superior colliculus that terminates decision-making by applying a threshold to lateral intraparietal cortex activity.

This paper introduces DREDge, an algorithm for robust online drift estimation in electrophysiology data, improving scalability and temporal resolution for human and mouse recordings.

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 shows that motor units are flexibly controlled by the cortex, challenging the traditional view of common drive and rigid recruitment.

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 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 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.

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.

Task complexity sets how many dimensions multineuronal activity occupies and when dynamical portraits recovered from a tiny fraction of neurons can be trusted; tested on monkey reaching recordings.