# Validation of neural spike sorting algorithms without ground-truth information

arXiv: https://arxiv.org/abs/1508.06936 · HTML: https://mlsanity.com/paper/1508.06936/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2015.

## Built on by (most cited first, 1 of 1)

- [Tackling water table depth modeling via machine learning: From proxy observations to verifiability](https://mlsanity.com/paper/2405.04579/) (2024)

## Abstract (arXiv)

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.