Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes

被引:70
作者
Faes, Luca [1 ,2 ]
Marinazzo, Daniele [3 ]
Stramaglia, Sebastiano [4 ,5 ]
机构
[1] Bruno Kessler Fdn, I-38123 Trento, Italy
[2] Univ Trento, Dept Ind Engn, BIOtech, I-38123 Trento, Italy
[3] Univ Ghent, Data Anal Dept, B-9000 Ghent, Belgium
[4] Univ Aldo Moro, Dipartimento Fis, I-70126 Bari, Italy
[5] Ist Nazl Fis Nucl, Sez Bari, I-70126 Bari, Italy
关键词
information dynamics; information transfer; multiscale entropy; multivariate time series analysis; redundancy and synergy; state space models; vector autoregressive models; GRANGER CAUSALITY; DYNAMICS;
D O I
10.3390/e19080408
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Exploiting the theory of state space models, we derive the exact expressions of the information transfer, as well as redundant and synergistic transfer, for coupled Gaussian processes observed at multiple temporal scales. All of the terms, constituting the frameworks known as interaction information decomposition and partial information decomposition, can thus be analytically obtained for different time scales from the parameters of the VAR model that fits the processes. We report the application of the proposed methodology firstly to benchmark Gaussian systems, showing that this class of systems may generate patterns of information decomposition characterized by prevalently redundant or synergistic information transfer persisting across multiple time scales or even by the alternating prevalence of redundant and synergistic source interaction depending on the time scale. Then, we apply our method to an important topic in neuroscience, i.e., the detection of causal interactions in human epilepsy networks, for which we show the relevance of partial information decomposition to the detection of multiscale information transfer spreading from the seizure onset zone.
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页数:18
相关论文
共 61 条
[1]  
[Anonymous], P 4 INT S IND COMP A
[2]  
[Anonymous], 1987, PROBLEMY PEREDACHI I
[3]  
[Anonymous], SCI REP UK
[4]  
[Anonymous], 1954, T IRE PROF GROUP INF, DOI [10.1109/TIT.1954.1057469, DOI 10.1109/TIT.1954.1057469]
[5]  
Aoki M, 1991, ECONOMET REV, V10, P1, DOI DOI 10.1080/07474939108800194
[6]  
Aoki Masanao, 2013, STATE SPACE MODELING
[7]   Detectability of Granger causality for subsampled continuous-time neurophysiological processes [J].
Barnett, Lionel ;
Seth, Anil K. .
JOURNAL OF NEUROSCIENCE METHODS, 2017, 275 :93-121
[8]   Granger causality for state-space models [J].
Barnett, Lionel ;
Seth, Anil K. .
PHYSICAL REVIEW E, 2015, 91 (04)
[9]   Information Flow in a Kinetic Ising Model Peaks in the Disordered Phase [J].
Barnett, Lionel ;
Lizier, Joseph T. ;
Harre, Michael ;
Seth, Anil K. ;
Bossomaier, Terry .
PHYSICAL REVIEW LETTERS, 2013, 111 (17)
[10]   Exploration of synergistic and redundant information sharing in static and dynamical Gaussian systems [J].
Barrett, Adam B. .
PHYSICAL REVIEW E, 2015, 91 (05)