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J Physiol Paris. 2016 Nov;110(4 Pt A):327-335. doi: 10.1016/j.jphysparis.2017.02.005. Epub 2017 Mar 2.

Recent progress in multi-electrode spike sorting methods.

Author information

1
Sorbonne Universités, UPMC Univ Paris 06, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, 75012 Paris, France; Laboratoire de Physique Statistique, UPMC-Sorbonne Universités, CNRS, ENS-PSL Research University, 24 rue Lhomond, 75005 Paris, France. Electronic address: baptiste.lefebvre@ens.fr.
2
Sorbonne Universités, UPMC Univ Paris 06, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, 75012 Paris, France.

Abstract

In recent years, arrays of extracellular electrodes have been developed and manufactured to record simultaneously from hundreds of electrodes packed with a high density. These recordings should allow neuroscientists to reconstruct the individual activity of the neurons spiking in the vicinity of these electrodes, with the help of signal processing algorithms. Algorithms need to solve a source separation problem, also known as spike sorting. However, these new devices challenge the classical way to do spike sorting. Here we review different methods that have been developed to sort spikes from these large-scale recordings. We describe the common properties of these algorithms, as well as their main differences. Finally, we outline the issues that remain to be solved by future spike sorting algorithms.

KEYWORDS:

Electrophysiology; Microelectrode array; Multi-electrode array; Signal processing; Spike sorting; Template matching

PMID:
28263793
PMCID:
PMC5581741
DOI:
10.1016/j.jphysparis.2017.02.005
[Indexed for MEDLINE]
Free PMC Article

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