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

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

doi:10.1016/j.celrep.2017.02.038

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

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^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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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.

Original language	English
Pages (from-to)	2521-2532
Number of pages	12
Journal	Cell Reports
Volume	18
Issue number	10
DOIs	https://doi.org/10.1016/j.celrep.2017.02.038
Publication status	Published - 7 Mar 2017
Externally published	Yes

Keywords

electrophysiology
high-density multielectrode array
neural cultures
retina
spike sorting

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10.1016/j.celrep.2017.02.038Licence: CC BY

1-s2.0-S221112471730236X-mainFinal published version, 12.2 MBLicence: CC BY

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Hilgen, G., Sorbaro, M., Pirmoradian, S., Muthmann, J. O., Kepiro, I. E., Ullo, S., Ramirez, C. J., Puente Encinas, A., Maccione, A., Berdondini, L., Murino, V., Sona, D., Cella Zanacchi, F., Sernagor, E., & Hennig, M. H. (2017). Unsupervised Spike Sorting for Large-Scale, High-Density Multielectrode Arrays. Cell Reports, 18(10), 2521-2532. https://doi.org/10.1016/j.celrep.2017.02.038

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

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.",

keywords = "electrophysiology, high-density multielectrode array, neural cultures, retina, spike sorting",

author = "Gerrit Hilgen and Martino Sorbaro and Sahar Pirmoradian and Muthmann, {Jens Oliver} and Kepiro, {Ibolya Edit} and Simona Ullo and Ramirez, {Cesar Juarez} and {Puente Encinas}, Albert and Alessandro Maccione and Luca Berdondini and Vittorio Murino and Diego Sona and {Cella Zanacchi}, Francesca and Evelyne Sernagor and Hennig, {Matthias Helge}",

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doi = "10.1016/j.celrep.2017.02.038",

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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 2017, 'Unsupervised Spike Sorting for Large-Scale, High-Density Multielectrode Arrays', Cell Reports, vol. 18, no. 10, pp. 2521-2532. https://doi.org/10.1016/j.celrep.2017.02.038

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T1 - Unsupervised Spike Sorting for Large-Scale, High-Density Multielectrode Arrays

AU - Hilgen, Gerrit

AU - Sorbaro, Martino

AU - Pirmoradian, Sahar

AU - Muthmann, Jens Oliver

AU - Kepiro, Ibolya Edit

AU - Ullo, Simona

AU - Ramirez, Cesar Juarez

AU - Puente Encinas, Albert

AU - Maccione, Alessandro

AU - Berdondini, Luca

AU - Murino, Vittorio

AU - Sona, Diego

AU - Cella Zanacchi, Francesca

AU - Sernagor, Evelyne

AU - Hennig, Matthias Helge

PY - 2017/3/7

Y1 - 2017/3/7

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AB - 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.

KW - electrophysiology

KW - high-density multielectrode array

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ER -

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

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Keywords

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