Validation of neural spike sorting algorithms without ground-truth information

Autor:	Barnett, Alex H., Magland, Jeremy F., Greengard, Leslie F.
Rok vydání:	2015
Předmět:	Quantitative Biology - Neurons and Cognition Computer Science - Computer Vision and Pattern Recognition 94A12 92C55 62M10
Druh dokumentu:	Working Paper
Popis:	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. Comment: 22 pages, 7 figures; submitted to J. Neurosci. Meth
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1508.06936 Zobrazit plný text záznamu View this record from Arxiv