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Multichannel electrophysiological spike sorting via joint dictionary learning and mixture modeling

dc.contributor.author Carin, Lawrence
dc.contributor.author Carlson, DE
dc.contributor.author Dunson, David B
dc.contributor.author Kipke, D
dc.contributor.author Lian, W
dc.contributor.author Stoetzner, CR
dc.contributor.author Vogelstein, Joshua T
dc.contributor.author Weber, D
dc.contributor.author Wu, Q
dc.contributor.author Zhou, M
dc.date.accessioned 2017-10-01T21:16:34Z
dc.date.available 2017-10-01T21:16:34Z
dc.date.issued 2014-01-01
dc.identifier.issn 0018-9294
dc.identifier.uri https://hdl.handle.net/10161/15596
dc.description.abstract We propose a methodology for joint feature learning and clustering of multichannel extracellular electrophysiological data, across multiple recording periods for action potential detection and classification (sorting). Our methodology improves over the previous state of the art principally in four ways. First, via sharing information across channels, we can better distinguish between single-unit spikes and artifacts. Second, our proposed "focused mixture model" (FMM) deals with units appearing, disappearing, or reappearing over multiple recording days, an important consideration for any chronic experiment. Third, by jointly learning features and clusters, we improve performance over previous attempts that proceeded via a two-stage learning process. Fourth, by directly modeling spike rate, we improve the detection of sparsely firing neurons. Moreover, our Bayesian methodology seamlessly handles missing data. We present the state-of-the-art performance without requiring manually tuning hyperparameters, considering both a public dataset with partial ground truth and a new experimental dataset. © 2013 IEEE.
dc.relation.ispartof IEEE Transactions on Biomedical Engineering
dc.relation.isversionof 10.1109/TBME.2013.2275751
dc.title Multichannel electrophysiological spike sorting via joint dictionary learning and mixture modeling
dc.type Journal article
pubs.begin-page 41
pubs.end-page 54
pubs.issue 1
pubs.organisational-group Basic Science Departments
pubs.organisational-group Biostatistics & Bioinformatics
pubs.organisational-group Civil and Environmental Engineering
pubs.organisational-group Duke
pubs.organisational-group Duke Clinical Research Institute
pubs.organisational-group Duke Institute for Brain Sciences
pubs.organisational-group Electrical and Computer Engineering
pubs.organisational-group Institutes and Centers
pubs.organisational-group Institutes and Provost's Academic Units
pubs.organisational-group Pratt School of Engineering
pubs.organisational-group School of Medicine
pubs.organisational-group Statistical Science
pubs.organisational-group Trinity College of Arts & Sciences
pubs.organisational-group University Institutes and Centers
pubs.publication-status Published
pubs.volume 61
dc.identifier.eissn 1558-2531


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