A Robust Estimation of Information Flow in Coupled Nonlinear Systems

摘要: Transfer entropy (TE) is a recently proposed measure of the information flow between coupled linear or nonlinear systems. In this study, we first suggest improvements in selection parameters for estimation TE that significantly enhance its accuracy and robustness identifying direction level observed data series generated by complex Second, new measure, net transfer (NTE), defined based on TE. Third, employ surrogate analysis to show statistical significance measures. Fourth, effect measurement noise measures’ performance investigated up \(S/N = 3\) dB. We demonstrate usefulness improved method analyzing from chaotic oscillators. Our findings NTE may play critical role elucidating functional connectivity networks