A data-driven regularization approach for template matching in spike sorting with high-density neural probes

摘要

Spike sorting is the process of assigning neural spikes in an extracellular brain recording to their putative neurons. Optimal pre-whitened template matching filters that are used in spike sorting typically suffer from ill-conditioning. In this paper, we investigate the origin of this ill-conditioning and the way in which it influences the resulting filters. Two data-driven subspace regularization approaches are proposed, and those are shown to outperform a regularization approach used in recent literature. The comparison of the methods is based on ground truth data that are recorded in-vivo.