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Unsupervised Spike Sorting for Large-Scale, High-Density Multielectrode Arrays
KTH, School of Engineering Sciences (SCI), Physics, Theoretical & Computational Biophysics.
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2017 (English)In: Cell reports, ISSN 2211-1247, E-ISSN 2211-1247, Vol. 18, no 10, p. 2521-2532Article in journal (Refereed) Published
Abstract [en]

We present amethod 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.

Place, publisher, year, edition, pages
CELL PRESS , 2017. Vol. 18, no 10, p. 2521-2532
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-205467DOI: 10.1016/j.celrep.2017.02.038ISI: 000397329500019PubMedID: 28273464Scopus ID: 2-s2.0-85014523793OAI: oai:DiVA.org:kth-205467DiVA, id: diva2:1097246
Note

QC 20170522

Available from: 2017-05-22 Created: 2017-05-22 Last updated: 2018-01-13Bibliographically approved

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Citation style
  • apa
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  • Other locale
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Output format
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