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dc.date.accessioned2018-08-23T12:43:19Z
dc.date.available2018-08-23T12:43:19Z
dc.date.created2018-01-31T15:42:38Z
dc.date.issued2017
dc.identifier.citationLee, Jin Hyung Carlson, David E. Shokri Razaghi, Hooshmand Yao, Weichi Goetz, Georges A. Hagen, Espen Batty, Eleanor Chichilnisky, E.J. Einevoll, Gaute Paninski, Liam . YASS: Yet Another Spike Sorter. Advances in Neural Information Processing Systems. 2017, 30, 4005-4015
dc.identifier.urihttp://hdl.handle.net/10852/63658
dc.description.abstractSpike sorting is a critical first step in extracting neural signals from large-scale electrophysiological data. This manuscript describes an efficient, reliable pipeline for spike sorting on dense multi-electrode arrays (MEAs), where neural signals appear across many electrodes and spike sorting currently represents a major computational bottleneck. We present several new techniques that make dense MEA spike sorting more robust and scalable. 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 method followed by efficient outlier triaging. The clean waveforms are then used to infer the set of neural spike waveform templates through nonparametric Bayesian clustering. Our clustering approach adapts a "coreset" approach for data reduction and uses efficient inference methods in a Dirichlet process mixture model framework to dramatically improve the scalability and reliability of the entire pipeline. The "triaged" waveforms are then finally recovered with matching-pursuit deconvolution techniques. The proposed methods improve on the state-of-the-art in terms of accuracy and stability on both real and biophysically-realistic simulated MEA data. Furthermore, the proposed pipeline is efficient, learning templates and clustering much faster than real-time for a 500-electrode dataset, using primarily a single CPU core.en_US
dc.languageEN
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleYASS: Yet Another Spike Sorteren_US
dc.typeJournal articleen_US
dc.creator.authorLee, Jin Hyung
dc.creator.authorCarlson, David E.
dc.creator.authorShokri Razaghi, Hooshmand
dc.creator.authorYao, Weichi
dc.creator.authorGoetz, Georges A.
dc.creator.authorHagen, Espen
dc.creator.authorBatty, Eleanor
dc.creator.authorChichilnisky, E.J.
dc.creator.authorEinevoll, Gaute
dc.creator.authorPaninski, Liam
cristin.unitcode185,15,4,10
cristin.unitnameKondenserte fasers fysikk
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.cristin1559177
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Advances in Neural Information Processing Systems&rft.volume=30&rft.spage=4005&rft.date=2017
dc.identifier.jtitleAdvances in Neural Information Processing Systems
dc.identifier.volume30
dc.identifier.startpage4005
dc.identifier.endpage4015
dc.identifier.doihttp://dx.doi.org/10.1101/151928
dc.identifier.urnURN:NBN:no-66205
dc.type.documentTidsskriftartikkelen_US
dc.type.peerreviewedPeer reviewed
dc.source.issn1049-5258
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/63658/4/151928.full.pdf
dc.type.versionPublishedVersion
dc.relation.projectNFR/250128


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