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

Publication date: 7 March 2017
Source:Cell Reports, Volume 18, Issue 10
Author(s): Gerrit Hilgen, Martino Sorbaro, Sahar Pirmoradian, Jens-Oliver Muthmann, Ibolya Edit Kepiro, Simona Ullo, Cesar Juarez Ramirez, Albert Puente Encinas, Alessandro Maccione, Luca Berdondini, Vittorio Murino, Diego Sona, Francesca Cella Zanacchi, Evelyne Sernagor, Matthias Helge Hennig
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.

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Teaser

Data volume and complexity make spike sorting for large-scale extracellular recordings computationally extremely challenging. Hilgen et al. introduce a method enabling analysis of recordings with thousands of channels and provide tools for automated quality control and unit selection.


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