Scaling Spike Detection and Sorting for Next Generation Electrophysiology

TL;DR

This paper reviews recent methods for spike detection and sorting in large-scale electrophysiological data, addressing computational challenges and emphasizing the importance of validation and quality assessment.

Contribution

It provides a comprehensive summary of new algorithms for scalable spike sorting and discusses validation strategies for high-density neural recordings.

Findings

Recent methods enable scalable spike sorting for large datasets

Validation techniques are crucial for ensuring accuracy in spike detection

Discussion highlights ongoing challenges and future directions

Abstract

Reliable spike detection and sorting, the process of assigning each detected spike to its originating neuron, is an essential step in the analysis of extracellular electrical recordings from neurons. The volume and complexity of the data from recently developed large scale, high density microelectrode arrays and probes, which allow recording from thousands of channels simultaneously, substantially complicate this task conceptually and computationally. This chapter provides a summary and discussion of recently developed methods to tackle these challenges, and discuss the important aspect of algorithm validation, and assessment of detection and sorting quality.