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dc.contributor.authorHaslinger, Robert Heinz
dc.contributor.authorPipa, Gordon
dc.contributor.authorSchumacher, Johannes
dc.date.accessioned2012-08-30T14:51:04Z
dc.date.available2012-08-30T14:51:04Z
dc.date.issued2012-05
dc.date.submitted2011-12
dc.identifier.issn1539-3755
dc.identifier.issn1550-2376
dc.identifier.urihttp://hdl.handle.net/1721.1/72458
dc.description.abstractDetecting nonlinear correlations between time series presents a hard problem for data analysis. We present a generative statistical modeling method for detecting nonlinear generalized synchronization. Truncated Volterra series are used to approximate functional interactions. The Volterra kernels are modeled as linear combinations of basis splines, whose coefficients are estimated via l[subscript 1] and l[subscript 2] regularized maximum likelihood regression. The regularization manages the high number of kernel coefficients and allows feature selection strategies yielding sparse models. The method's performance is evaluated on different coupled chaotic systems in various synchronization regimes and analytical results for detecting m:n phase synchrony are presented. Experimental applicability is demonstrated by detecting nonlinear interactions between neuronal local field potentials recorded in different parts of macaque visual cortex.en_US
dc.description.sponsorshipSeventh Framework Programme (European Commission). Project Phocus (grant no. FET-Open 240763)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (grant no. K25-NS052422-02)en_US
dc.language.isoen_US
dc.publisherAmerican Physical Societyen_US
dc.relation.isversionofhttp://dx.doi.org/10.1103/PhysRevE.85.056215en_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.sourceAPSen_US
dc.titleStatistical modeling approach for detecting generalized synchronizationen_US
dc.typeArticleen_US
dc.identifier.citationSchumacher, Johannes, Robert Haslinger, and Gordon Pipa. “Statistical Modeling Approach for Detecting Generalized Synchronization.” Physical Review E 85.5 (2012): 056215. © 2012 American Physical Society.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciencesen_US
dc.contributor.approverHaslinger, Robert Heinz
dc.contributor.mitauthorHaslinger, Robert Heinz
dc.relation.journalPhysical Review Een_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.orderedauthorsSchumacher, Johannes; Haslinger, Robert; Pipa, Gordonen
mit.licensePUBLISHER_POLICYen_US
mit.metadata.statusComplete


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