Three-dimensional spike localization and improved motion correction for Neuropixels recordings

Abstract

AbstractNeuropixels (NP) probes are dense linear multi-electrode arrays that have rapidly become essential tools for studying the electrophysiology of large neural populations. Unfortunately, a number of challenges remain in analyzing the large datasets output by these probes. Here we introduce several new methods for extracting useful spiking information from NP probes. First, we use a simple point neuron model, together with a neural-network denoiser, to efficiently map single spikes detected on the probe into three-dimensional localizations. Previous methods localized individual spikes in two dimensions only; we show that the new localization approach is significantly more robust and provides an improved feature set for clustering spikes according to neural identity (“spike sorting”). Next, we denoise the resulting three-dimensional point-cloud representation of the data, and show that the resulting 3D images can be accurately registered over time, leading to improved tracking of time-varying neural activity over the probe, and in turn, crisper estimates of neural clusters over time. Open source code is available at https://github.com/int-brain-lab/spikes_localization_registration.git.