A Study of Deep Clustering in Spike Sorting.

Abstract

Spike sorting is the process of identifying the source neurons for neuronal activity recorded from extracellular electrodes. Traditional spike sorting pipelines separate the process into distinct feature extraction and clustering steps, which may not optimally capture the complex structure of spike data. This study provides a large-scale benchmark of 12 deep clustering algorithms against traditional feature extraction methods combined with K-means clustering for spike sorting. We analyze performance across 95 synthetic datasets with varying cluster counts (2-20) and complexity from the perspective of six performance metrics. Our results demonstrate that a subset of deep clustering algorithms—particularly ACeDeC, DDC, DEC, IDEC and VaDE—significantly outperform traditional methods, especially as dataset complexity increases. These deep clustering approaches effectively learn non-linear representations that better capture the structure of spike data while simultaneously optimizing clustering objectives. This dual optimization produces feature spaces tailored for clustering, combining the two traditionally separate steps of spike sorting. Our findings indicate that deep clustering approaches are most suitable for accurately identifying individual neuronal activity in extracellular recordings, providing guidance for method selection for the increasingly complex modern multi-electrode recordings.