A Fully Automated Approach to Spike Sorting.

By Jason E. Chung, Jeremy F. Magland, A. Barnett, Vanessa M. Tolosa, A. Tooker, K. Lee, Kedar G. Shah, Sarah H. Felix, Loren M. Frank, Leslie Greengard. Neuron, 2017

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Spike sorting with Kilosort4 (2024, unknown): “5 and 3, these predefined templates were constrained to be negative-going spikes, whereas in Kilosort4 we consider both positive and negative-going spikes using pairs of inverted templates (for fast computation).”
High-density, long-lasting, and multi-region electrophysiological recordings using polymer electrode arrays (2018, unknown): “Spike sorting using MountainSort (Chung et al., 2017) on data from these 512 channels 45 days after implantation produced 1533 clusters with a”
Challenges and opportunities for large-scale electrophysiology with Neuropixels probes (2018, background): “No algorithm can be trusted blindly without quantitative evidence that it has low error rates, and the development of metrics to assess these error rates is an area of active research [41,47].”
Single-Neuron Recordings Research in Children: Ethical Considerations, Feasibility, Technical Aspects, and Scientific Opportunities (2025, unknown): “Modern spike sorting algorithms like MountainSort [95] or OSort [96] accommodate waveform drift and overlapping signals, improving single-unit isolation.”