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dc.contributor.authorFriedman, Alexander
dc.contributor.authorKeselman, Michael D.
dc.contributor.authorGibb, Leif G.
dc.contributor.authorGraybiel, Ann M.
dc.date.accessioned2015-10-01T12:37:33Z
dc.date.available2015-10-01T12:37:33Z
dc.date.issued2015-04
dc.date.submitted2015-01
dc.identifier.issn0027-8424
dc.identifier.issn1091-6490
dc.identifier.urihttp://hdl.handle.net/1721.1/99117
dc.description.abstractA critical problem faced in many scientific fields is the adequate separation of data derived from individual sources. Often, such datasets require analysis of multiple features in a highly multidimensional space, with overlap of features and sources. The datasets generated by simultaneous recording from hundreds of neurons emitting phasic action potentials have produced the challenge of separating the recorded signals into independent data subsets (clusters) corresponding to individual signal-generating neurons. Mathematical methods have been developed over the past three decades to achieve such spike clustering, but a complete solution with fully automated cluster identification has not been achieved. We propose here a fully automated mathematical approach that identifies clusters in multidimensional space through recursion, which combats the multidimensionality of the data. Recursion is paired with an approach to dimensional evaluation, in which each dimension of a dataset is examined for its informational importance for clustering. The dimensions offering greater informational importance are given added weight during recursive clustering. To combat strong background activity, our algorithm takes an iterative approach of data filtering according to a signal-to-noise ratio metric. The algorithm finds cluster cores, which are thereafter expanded to include complete clusters. This mathematical approach can be extended from its prototype context of spike sorting to other datasets that suffer from high dimensionality and background activity.en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (Grant R01 MH060379)en_US
dc.description.sponsorshipUnited States. Defense Advanced Research Projects Agencyen_US
dc.description.sponsorshipUnited States. Army Research Office (Grant W911NF-10-1-0059)en_US
dc.description.sponsorshipCure Huntington’s Disease Initiative, Inc. (Grant A-5552)en_US
dc.language.isoen_US
dc.publisherNational Academy of Sciences (U.S.)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1073/pnas.1503940112en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceNational Academy of Sciences (U.S.)en_US
dc.titleA multistage mathematical approach to automated clustering of high-dimensional noisy dataen_US
dc.typeArticleen_US
dc.identifier.citationFriedman, Alexander, Michael D. Keselman, Leif G. Gibb, and Ann M. Graybiel. “A Multistage Mathematical Approach to Automated Clustering of High-Dimensional Noisy Data.” Proc Natl Acad Sci USA 112, no. 14 (March 23, 2015): 4477–4482.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciencesen_US
dc.contributor.departmentMcGovern Institute for Brain Research at MITen_US
dc.contributor.mitauthorFriedman, Alexanderen_US
dc.contributor.mitauthorKeselman, Michael D.en_US
dc.contributor.mitauthorGibb, Leif G.en_US
dc.contributor.mitauthorGraybiel, Ann M.en_US
dc.relation.journalProceedings of the National Academy of Sciencesen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsFriedman, Alexander; Keselman, Michael D.; Gibb, Leif G.; Graybiel, Ann M.en_US
dc.identifier.orcidhttps://orcid.org/0000-0002-4326-7720
mit.licensePUBLISHER_POLICYen_US
mit.metadata.statusComplete


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