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A novel and fully automatic spike-sorting implementation with variable number of features

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journal contribution
posted on 12.08.2019, 13:39 by Fernando J. Chaure, Hernan G. Rey, Rodrigo Quian Quiroga
The most widely used spike-sorting algorithms are semiautomatic in practice, requiring manual tuning of the automatic solution to achieve good performance. In this work, we propose a new fully automatic spike-sorting algorithm that can capture multiple clusters of different sizes and densities. In addition, we introduce an improved feature selection method, by using a variable number of wavelet coefficients, based on the degree of non-Gaussianity of their distributions. We evaluated the performance of the proposed algorithm with real and simulated data. With real data from single-channel recordings, in ~95% of the cases the new algorithm replicated, in an unsupervised way, the solutions obtained by expert sorters, who manually optimized the solution of a previous semiautomatic algorithm. This was done while maintaining a low number of false positives. With simulated data from single-channel and tetrode recordings, the new algorithm was able to correctly detect many more neurons compared with previous implementations and also compared with recently introduced algorithms, while significantly reducing the number of false positives. In addition, the proposed algorithm showed good performance when tested with real tetrode recordings.

Funding

This research was supported by the Medical Research Council (G1002100) and the Human Frontiers Research Program.

History

Citation

Journal of Neurophysiology, 2018, 120, pp. 1859-1871

Author affiliation

/Organisation/COLLEGE OF LIFE SCIENCES/Biological Sciences/Neuroscience, Psychology and Behaviour

Version

VoR (Version of Record)

Published in

Journal of Neurophysiology

Publisher

American Physiological Society

issn

1522-1598

Acceptance date

09/07/2018

Copyright date

2018

Available date

12/08/2019

Publisher version

https://www.physiology.org/doi/full/10.1152/jn.00339.2018

Language

en

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