Cell Reports
Volume 18, Issue 10, 7 March 2017, Pages 2521-2532
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Unsupervised Spike Sorting for Large-Scale, High-Density Multielectrode Arrays

https://doi.org/10.1016/j.celrep.2017.02.038Get rights and content
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Highlights

  • An automated spike sorting method for dense, large-scale recordings is presented

  • Efficient data representation enables sorting of thousands of channels

  • Automated unit selection through model-based quality control

  • Conventional spike sorting frequently fails under non-optimal signal conditions

Summary

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

spike sorting
high-density multielectrode array
electrophysiology
retina
neural cultures

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Co-first author

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Present address: National Physical Laboratory (NPL), Teddington TW11 0LW, UK

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