Statistical modeling approach for detecting generalized synchronization
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | en_US |
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American Physical Society
2012
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| Online Access: | http://hdl.handle.net/1721.1/72458 |
| _version_ | 1811092795391213568 |
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| author | Haslinger, Robert Heinz Pipa, Gordon Schumacher, Johannes |
| author2 | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Haslinger, Robert Heinz Pipa, Gordon Schumacher, Johannes |
| author_sort | Haslinger, Robert Heinz |
| collection | MIT |
| description | 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[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. |
| first_indexed | 2024-09-23T15:26:29Z |
| format | Article |
| id | mit-1721.1/72458 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:26:29Z |
| publishDate | 2012 |
| publisher | American Physical Society |
| record_format | dspace |
| spelling | mit-1721.1/724582022-09-29T14:43:00Z Statistical modeling approach for detecting generalized synchronization Haslinger, Robert Heinz Pipa, Gordon Schumacher, Johannes Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Haslinger, Robert Heinz Haslinger, Robert Heinz 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[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. Seventh Framework Programme (European Commission). Project Phocus (grant no. FET-Open 240763) National Institutes of Health (U.S.) (grant no. K25-NS052422-02) 2012-08-30T14:51:04Z 2012-08-30T14:51:04Z 2012-05 2011-12 Article http://purl.org/eprint/type/JournalArticle 1539-3755 1550-2376 http://hdl.handle.net/1721.1/72458 Schumacher, 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 http://dx.doi.org/10.1103/PhysRevE.85.056215 Physical Review E Article 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. application/pdf American Physical Society APS |
| spellingShingle | Haslinger, Robert Heinz Pipa, Gordon Schumacher, Johannes Statistical modeling approach for detecting generalized synchronization |
| title | Statistical modeling approach for detecting generalized synchronization |
| title_full | Statistical modeling approach for detecting generalized synchronization |
| title_fullStr | Statistical modeling approach for detecting generalized synchronization |
| title_full_unstemmed | Statistical modeling approach for detecting generalized synchronization |
| title_short | Statistical modeling approach for detecting generalized synchronization |
| title_sort | statistical modeling approach for detecting generalized synchronization |
| url | http://hdl.handle.net/1721.1/72458 |
| work_keys_str_mv | AT haslingerrobertheinz statisticalmodelingapproachfordetectinggeneralizedsynchronization AT pipagordon statisticalmodelingapproachfordetectinggeneralizedsynchronization AT schumacherjohannes statisticalmodelingapproachfordetectinggeneralizedsynchronization |