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Unsupervised spike sorting for large scale, high density multielectrode arrays

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Original languageEnglish
Pages (from-to)2521-2532
Number of pages12
JournalCell Reports
Issue number10
Publication statusPublished - 7 Mar 2017


A new method for automated spike sorting for recordings with high density, large scale multielectrode arrays is presented. It is based on an efficient, low-dimensional representation of detected events by their estimated spatial current source locations and dominant spike shape features. Millions of events can be sorted in just minutes, and the full analysis chain scales roughly linearly with recording time. We demonstrate this method using recordings from the mouse retina with a 4,096 channel array, and present validation based on anatomical imaging and model-based quality control. Our analysis shows that it is feasible to reliably isolate the activity of hundreds to thousands of retinal ganglion cells in single recordings.

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