Large-scale high-density brain-wide neural recording in nonhuman primates.

High-density silicon probes have transformed neuroscience by enabling large-scale neural recordings at single-cell resolution. However, existing technologies have provided limited functionality in nonhuman primates (NHPs) such as macaques. In the present report, we describe the design, fabrication and performance of Neuropixels 1.

Formation of Anisotropic Conducting Interlayer for High-Resolution Epidermal Electromyography Using Mixed-Conducting Particulate Composite.

Epidermal electrophysiology is a non-invasive method used in research and clinical practices to study the electrical activity of the brain, heart, nerves, and muscles. However, electrode/tissue interlayer materials such as ionically conducting pastes can negatively affect recordings by introducing lateral electrode-to-electrode ionic crosstalk and reducing spatial resolution. To overcome this issue, biocompatible, anisotropic-conducting interlayer composites (ACI) that establish an electrically anisotropic interface with the skin are developed, enabling the application of dense cutaneous sensor arrays.

Modified Neuropixels probes for recording human neurophysiology in the operating room.

Neuropixels are silicon-based electrophysiology-recording probes with high channel count and recording-site density. These probes offer a turnkey platform for measuring neural activity with single-cell resolution and at a scale that is beyond the capabilities of current clinically approved devices. Our team demonstrated the first-in-human use of these probes during resection surgery for epilepsy or tumors and deep brain stimulation electrode placement in patients with Parkinson's disease.

A neural mechanism for terminating decisions.

The brain makes decisions by accumulating evidence until there is enough to stop and choose. Neural mechanisms of evidence accumulation are established in association cortex, but the site and mechanism of termination are unknown. Here, we show that the superior colliculus (SC) plays a causal role in terminating decisions, and we provide evidence for a mechanism by which this occurs.

Large-scale high-density brain-wide neural recording in nonhuman primates.

High-density, integrated silicon electrodes have begun to transform systems neuroscience, by enabling large-scale neural population recordings with single cell resolution. Existing technologies, however, have provided limited functionality in nonhuman primate species such as macaques, which offer close models of human cognition and behavior. Here, we report the design, fabrication, and performance of Neuropixels 1.

Flexible neural control of motor units.

Voluntary movement requires communication from cortex to the spinal cord, where a dedicated pool of motor units (MUs) activates each muscle. The canonical description of MU function rests upon two foundational tenets. First, cortex cannot control MUs independently but supplies each pool with a common drive.

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

Recent advances in multi-electrode array technology have made it possible to monitor large neuronal ensembles at cellular resolution in animal models. In humans, however, current approaches restrict recordings to a few neurons per penetrating electrode or combine the signals of thousands of neurons in local field potential (LFP) recordings. Here we describe a new probe variant and set of techniques that enable simultaneous recording from over 200 well-isolated cortical single units in human participants during intraoperative neurosurgical procedures using silicon Neuropixels probes.

Cortical preparatory activity indexes learned motor memories.

The brain's remarkable ability to learn and execute various motor behaviours harnesses the capacity of neural populations to generate a variety of activity patterns. Here we explore systematic changes in preparatory activity in motor cortex that accompany motor learning. We trained rhesus monkeys to learn an arm-reaching task in a curl force field that elicited new muscle forces for some, but not all, movement directions.

Dendritic calcium signals in rhesus macaque motor cortex drive an optical brain-computer interface.

Calcium imaging is a powerful tool for recording from large populations of neurons in vivo. Imaging in rhesus macaque motor cortex can enable the discovery of fundamental principles of motor cortical function and can inform the design of next generation brain-computer interfaces (BCIs). Surface two-photon imaging, however, cannot presently access somatic calcium signals of neurons from all layers of macaque motor cortex due to photon scattering.

Discovering Precise Temporal Patterns in Large-Scale Neural Recordings through Robust and Interpretable Time Warping.

Though the temporal precision of neural computation has been studied intensively, a data-driven determination of this precision remains a fundamental challenge. Reproducible spike patterns may be obscured on single trials by uncontrolled temporal variability in behavior and cognition and may not be time locked to measurable signatures in behavior or local field potentials (LFP). To overcome these challenges, we describe a general-purpose time warping framework that reveals precise spike-time patterns in an unsupervised manner, even when these patterns are decoupled from behavior or are temporally stretched across single trials.

Accurate Estimation of Neural Population Dynamics without Spike Sorting.

A central goal of systems neuroscience is to relate an organism's neural activity to behavior. Neural population analyses often reduce the data dimensionality to focus on relevant activity patterns. A major hurdle to data analysis is spike sorting, and this problem is growing as the number of recorded neurons increases.

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

Neuroscience is experiencing a revolution in which simultaneous recording of thousands of neurons is revealing population dynamics that are not apparent from single-neuron responses. This structure is typically extracted from data averaged across many trials, but deeper understanding requires studying phenomena detected in single trials, which is challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. We introduce latent factor analysis via dynamical systems, a deep learning method to infer latent dynamics from single-trial neural spiking data.