Statistical modeling approach for detecting generalized synchronization

Autor(en): Schumacher, Johannes
Haslinger, Robert
Pipa, Gordon 
Stichwörter: CHAOS; PHASE; Physics; Physics, Fluids & Plasmas; Physics, Mathematical
Erscheinungsdatum: 2012
Herausgeber: AMER PHYSICAL SOC
Journal: PHYSICAL REVIEW E
Volumen: 85
Ausgabe: 5, 2
Zusammenfassung: 
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.
ISSN: 15393755
DOI: 10.1103/PhysRevE.85.056215

Show full item record

Page view(s)

5
Last Week
0
Last month
0
checked on Apr 23, 2024

Google ScholarTM

Check

Altmetric