Statistical modeling approach for detecting generalized synchronization
Name
Schumacher-2012-Statistical modeling approach for detecting generalized synchronization.pdf
Size
913.56 KB
Format
Adobe PDF
Checksum (MD5)
5f42df4946f425778a988f1ed80c4e60
Author(s)
Haslinger, Robert Heinz
Pipa, Gordon
Schumacher, Johannes
Date Issued
May 2012
Journal
Physical Review E
Publisher
American Physical Society
Citation
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.
Version
Final published version
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[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.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
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.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1103/PhysRevE.85.056215
