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YASS: Yet Another Spike Sorter applied to large-scale multi-electrode array recordings in primate retina

JinHyung Lee, View ORCID ProfileCatalin Mitelut, Hooshmand Shokri, Ian Kinsella, Nishchal Dethe, Shenghao Wu, Kevin Li, Eduardo Blancas Reyes, Denis Turcu, Eleanor Batty, Young Joon Kim, Nora Brackbill, Alexandra Kling, Georges Goetz, E.J. Chichilnisky, David Carlson, Liam Paninski
doi: https://doi.org/10.1101/2020.03.18.997924
JinHyung Lee
1Columbia University
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Catalin Mitelut
1Columbia University
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  • ORCID record for Catalin Mitelut
Hooshmand Shokri
1Columbia University
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Ian Kinsella
1Columbia University
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Nishchal Dethe
1Columbia University
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Shenghao Wu
4Carnegie Mellon University
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Kevin Li
1Columbia University
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Eduardo Blancas Reyes
1Columbia University
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Denis Turcu
1Columbia University
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Eleanor Batty
1Columbia University
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Young Joon Kim
1Columbia University
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Nora Brackbill
3Stanford University
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Alexandra Kling
3Stanford University
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Georges Goetz
3Stanford University
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E.J. Chichilnisky
3Stanford University
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David Carlson
2Duke University
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Liam Paninski
1Columbia University
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  • For correspondence: liampaninski@gmail.com
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Abstract

Spike sorting is a critical first step in extracting neural signals from large-scale multi-electrode array (MEA) data. This manuscript presents several new techniques that make MEA spike sorting more robust and accurate. Our pipeline is based on an efficient multi-stage “triage-then-cluster-then-pursuit” approach that initially extracts only clean, high-quality waveforms from the electrophysiological time series by temporarily skipping noisy or “collided” events (representing two neurons firing synchronously). This is accomplished by developing a neural network detection and denoising method followed by efficient outlier triaging. The denoised spike waveforms are then used to infer the set of spike templates through nonparametric Bayesian clustering. We use a divide-and-conquer strategy to parallelize this clustering step. Finally, we recover collided waveforms with matching-pursuit deconvolution techniques, and perform further split-and-merge steps to estimate additional templates from the pool of recovered waveforms. We apply the new pipeline to data recorded in the primate retina, where high firing rates and highly-overlapping axonal units provide a challenging testbed for the deconvolution approach; in addition, the well-defined mosaic structure of receptive fields in this preparation provides a useful quality check on any spike sorting pipeline. We show that our pipeline improves on the state-of-the-art in spike sorting (and outperforms manual sorting) on both real and semi-simulated MEA data with > 500 electrodes; open source code can be found at https://github.com/paninski-lab/yass.

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  • https://github.com/paninski-lab/yass

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Posted March 20, 2020.
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YASS: Yet Another Spike Sorter applied to large-scale multi-electrode array recordings in primate retina
JinHyung Lee, Catalin Mitelut, Hooshmand Shokri, Ian Kinsella, Nishchal Dethe, Shenghao Wu, Kevin Li, Eduardo Blancas Reyes, Denis Turcu, Eleanor Batty, Young Joon Kim, Nora Brackbill, Alexandra Kling, Georges Goetz, E.J. Chichilnisky, David Carlson, Liam Paninski
bioRxiv 2020.03.18.997924; doi: https://doi.org/10.1101/2020.03.18.997924
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YASS: Yet Another Spike Sorter applied to large-scale multi-electrode array recordings in primate retina
JinHyung Lee, Catalin Mitelut, Hooshmand Shokri, Ian Kinsella, Nishchal Dethe, Shenghao Wu, Kevin Li, Eduardo Blancas Reyes, Denis Turcu, Eleanor Batty, Young Joon Kim, Nora Brackbill, Alexandra Kling, Georges Goetz, E.J. Chichilnisky, David Carlson, Liam Paninski
bioRxiv 2020.03.18.997924; doi: https://doi.org/10.1101/2020.03.18.997924

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