Journal
CELL REPORTS
Volume 18, Issue 10, Pages 2521-2532Publisher
CELL PRESS
DOI: 10.1016/j.celrep.2017.02.038
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Funding
- 7th Framework Program for Research of the European Commission [600847]
- Future and Emerging Technologies [FET] program Neuro-bio-inspired Systems
- Wellcome Trust [096975/Z/11/Z]
- EuroSPIN Erasmus Mundus Program
- EPSRC Doctoral Training Centre in Neuroinformatics [EP/F500385/1, BB/F529254/1]
- NCBS/TIFR
- Wellcome Trust [096975/Z/11/Z] Funding Source: Wellcome Trust
- BBSRC [BB/H023607/1, BB/H023569/1, BB/I001042/1] Funding Source: UKRI
- MRC [G0900425] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BB/H023569/1, BB/H023607/1, BB/I001042/1] Funding Source: researchfish
- Engineering and Physical Sciences Research Council [1538690] Funding Source: researchfish
- Medical Research Council [G0900425] Funding Source: researchfish
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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.
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