Validation of neural spike sorting algorithms without ground-truth information
This work addresses the need for automated validation in neuroscience to reduce human labor and support large-scale spike sorting, though it is incremental as it builds on existing sorting methods.
The authors tackled the problem of validating neural spike sorting algorithms without ground-truth information by developing a suite of metrics that assess stability under perturbations, enabling reproducibility in electrophysiological recordings.
We describe a suite of validation metrics that assess the credibility of a given automatic spike sorting algorithm applied to a given electrophysiological recording, when ground-truth is unavailable. By rerunning the spike sorter two or more times, the metrics measure stability under various perturbations consistent with variations in the data itself, making no assumptions about the noise model, nor about the internal workings of the sorting algorithm. Such stability is a prerequisite for reproducibility of results. We illustrate the metrics on standard sorting algorithms for both in vivo and ex vivo recordings. We believe that such metrics could reduce the significant human labor currently spent on validation, and should form an essential part of large-scale automated spike sorting and systematic benchmarking of algorithms.