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Lookup NU author(s): Dr Gerrit HilgenORCiD,
Professor Evelyne SernagorORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
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.
Author(s): Hilgen G, Sorbaro M, Pirmoradian S, Muthmann JO, Kepiro IE, Ullo S, Ramirez CJ, Puente Encinas A, Maccione A, Berdondini L, Murino V, Sona D, Cella Zanacchi F, Sernagor E, Hennig MH
Publication type: Article
Publication status: Published
Journal: Cell Reports
Online publication date: 07/03/2017
Acceptance date: 13/02/2017
Date deposited: 09/03/2017
ISSN (electronic): 2211-1247
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