Multichannel electrophysiological spike sorting via joint dictionary learning and mixture modeling
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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.
Published Version (Please cite this version)10.1109/TBME.2013.2275751
Publication InfoCarin, Lawrence; Carlson, DE; Dunson, David B; Kipke, D; Lian, W; Stoetzner, CR; ... Zhou, M (2014). Multichannel electrophysiological spike sorting via joint dictionary learning and mixture modeling. IEEE Transactions on Biomedical Engineering, 61(1). pp. 41-54. 10.1109/TBME.2013.2275751. Retrieved from https://hdl.handle.net/10161/15596.
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James L. Meriam Distinguished Professor of Electrical and Computer Engineering
Lawrence Carin earned the BS, MS, and PhD degrees in electrical engineering at the University of Maryland, College Park, in 1985, 1986, and 1989, respectively. In 1989 he joined the Electrical Engineering Department at Polytechnic University (Brooklyn) as an Assistant Professor, and became an Associate Professor there in 1994. In September 1995 he joined the Electrical Engineering Department at Duke University, where he is now a Professor, and Vice Provost for Research. From 2003-2014 he held th
Arts and Sciences Distinguished Professor of Statistical Science
Development of novel approaches for representing and analyzing complex data. A particular focus is on methods that incorporate geometric structure (both known and unknown) and on probabilistic approaches to characterize uncertainty. In addition, a big interest is in scalable algorithms and in developing approaches with provable guarantees.This fundamental work is directly motivated by applications in biomedical research, network data analysis, neuroscience, genomics, ecol
Alphabetical list of authors with Scholars@Duke profiles.