Statistical modeling approach for detecting generalized synchronization
DC Element | Wert | Sprache |
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dc.contributor.author | Schumacher, Johannes | |
dc.contributor.author | Haslinger, Robert | |
dc.contributor.author | Pipa, Gordon | |
dc.date.accessioned | 2021-12-23T16:19:13Z | - |
dc.date.available | 2021-12-23T16:19:13Z | - |
dc.date.issued | 2012 | |
dc.identifier.issn | 15393755 | |
dc.identifier.uri | https://osnascholar.ub.uni-osnabrueck.de/handle/unios/13043 | - |
dc.description.abstract | Detecting 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(1) and l(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. | |
dc.description.sponsorship | EUEuropean Commission [FET-Open 240763]; NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [K25-NS052422-02]; NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKEUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Neurological Disorders & Stroke (NINDS) [K25NS052422] Funding Source: NIH RePORTER; We thank Sergio Neuenschwander for providing the local field potential recordings. We thank Ingo Fischer and Miguel C. Soriano for providing the simulated Mackey-Glass data. This work was partially supported by the EU-project PHOCUS (FET-Open 240763) (J.S., G. P.) and the NIH Grant No. K25-NS052422-02 (R.H.). | |
dc.language.iso | en | |
dc.publisher | AMER PHYSICAL SOC | |
dc.relation.ispartof | PHYSICAL REVIEW E | |
dc.subject | CHAOS | |
dc.subject | PHASE | |
dc.subject | Physics | |
dc.subject | Physics, Fluids & Plasmas | |
dc.subject | Physics, Mathematical | |
dc.title | Statistical modeling approach for detecting generalized synchronization | |
dc.type | journal article | |
dc.identifier.doi | 10.1103/PhysRevE.85.056215 | |
dc.identifier.isi | ISI:000304530300005 | |
dc.description.volume | 85 | |
dc.description.issue | 5, 2 | |
dc.contributor.orcid | 0000-0002-3416-2652 | |
dc.contributor.researcherid | M-1813-2014 | |
dc.identifier.eissn | 15502376 | |
dc.publisher.place | ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA | |
dcterms.isPartOf.abbreviation | Phys. Rev. E | |
dcterms.oaStatus | Green Accepted, Green Published | |
crisitem.author.dept | Institut für Kognitionswissenschaft | - |
crisitem.author.deptid | institute28 | - |
crisitem.author.orcid | 0000-0002-3416-2652 | - |
crisitem.author.parentorg | FB 08 - Humanwissenschaften | - |
crisitem.author.grandparentorg | Universität Osnabrück | - |
crisitem.author.netid | PiGo340 | - |
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