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 InfoCarlson, David E; Vogelstein, Joshua T; Wu, Qisong; Lian, Wenzhao; Zhou, Mingyuan; Stoetzner, Colin R; ... Carin, Lawrence (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 and Computer Engineering (ECE) Department at Duke University, where he is now a Professor. He was ECE Department Chair from 2011
Assistant Professor of Civil and Environmental Engineering
Arts and Sciences Distinguished Professor of Statistical Science
My research focuses on developing new tools for probabilistic learning from complex data - methods development is directly motivated by challenging applications in ecology/biodiversity, neuroscience, environmental health, criminal justice/fairness, and more. We seek to develop new modeling frameworks, algorithms and corresponding code that can be used routinely by scientists and decision makers. We are also interested in new inference framework and in studying theoretical properties
Alphabetical list of authors with Scholars@Duke profiles.