Multichannel electrophysiological spike sorting via joint dictionary learning and mixture modeling
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.
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https://hdl.handle.net/10161/15596Published Version (Please cite this version)
10.1109/TBME.2013.2275751Publication Info
Carlson, 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.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Lawrence Carin
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
David Carlson
Assistant Professor of Civil and Environmental Engineering
My general research focus is on developing novel machine learning and artificial intelligence
techniques that can be used to accelerate scientific discovery. I work extensively
both on the fundamental theory and algorithms as well as translating them into scientific
applications. I have extensive partnerships deploying machine learning techniques
in environmental health, mental health, and neuroscience.
David B. Dunson
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
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