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

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.

Publication metadata

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

Year: 2017

Volume: 18

Issue: 10

Pages: 2521-2532

Online publication date: 07/03/2017

Acceptance date: 13/02/2017

Date deposited: 09/03/2017

ISSN (electronic): 2211-1247

Publisher: Elsevier


DOI: 10.1016/j.celrep.2017.02.038


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Funder referenceFunder name
096975/Z/11/ZWellcome Trust