Unsupervised Spike Sorting for Large-Scale, High-Density Multielectrode Arrays

Hilgen, Gerrit, Sorbaro, Martino, Pirmoradian, Sahar, Muthmann, Jens-Oliver, Kepiro, Ibolya Edit, Ullo, Simona, Ramirez, Cesar Juarez, Puente Encinas, Albert, Maccione, Alessandro, Berdondini, Luca, Murino, Vittorio, Sona, Diego, Cella Zanacchi, Francesca, Sernagor, Evelyne and Hennig, Matthias Helge (2017) Unsupervised Spike Sorting for Large-Scale, High-Density Multielectrode Arrays. Cell Reports, 18 (10). pp. 2521-2532. ISSN 2211-1247

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Official URL: https://doi.org/10.1016/j.celrep.2017.02.038

Abstract

We present a method for automated spike sorting for recordings with high-density, large-scale multielectrode arrays. Exploiting the dense sampling of single neurons by multiple electrodes, an efficient, low-dimensional representation of detected spikes consisting of estimated spatial spike locations and dominant spike shape features is exploited for fast and reliable clustering into single units. Millions of events can be sorted in minutes, and the method is parallelized and scales better than quadratically with the number of detected spikes. Performance is demonstrated using recordings with a 4,096-channel array and validated using anatomical imaging, optogenetic stimulation, and model-based quality control. A comparison with semi-automated, shape-based spike sorting exposes significant limitations of conventional methods. Our approach demonstrates that it is feasible to reliably isolate the activity of up to thousands of neurons and that dense, multi-channel probes substantially aid reliable spike sorting.

Item Type: Article
Uncontrolled Keywords: electrophysiology, high-density multielectrode array, neural cultures, retina, spike sorting
Subjects: C700 Molecular Biology, Biophysics and Biochemistry
Department: Faculties > Health and Life Sciences > Applied Sciences
Depositing User: Elena Carlaw
Date Deposited: 12 May 2020 14:39
Last Modified: 12 May 2020 14:45
URI: http://nrl.northumbria.ac.uk/id/eprint/43094

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