Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes
Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes
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DOI：
10.3390/e19080408
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发表时间：
2017-08-01
期刊：
影响因子：
2.7
通讯作者：
Stramaglia, Sebastiano
中科院分区：
文献类型：
--
作者：
Faes, Luca;Marinazzo, Daniele;Stramaglia, Sebastiano
利用状态空间模型理论，我们推导出了在多个时间尺度上观测到的耦合高斯过程的信息传递、冗余传递和协同传递的精确表达式。所有的条款，构成框架称为相互作用信息分解和部分信息分解，因此，可以分析获得不同的时间尺度从VAR模型的参数，适合的过程。我们报告的应用所提出的方法首先基准高斯系统，表明这类系统可能会产生模式的信息分解，其特征在于premandatory冗余或协同信息传递持续跨多个时间尺度，甚至由交替流行的冗余和协同源相互作用取决于时间尺度。然后，我们将我们的方法应用于神经科学中的一个重要课题，即，人类癫痫网络中因果关系的检测，我们显示了部分信息分解的相关性，以检测从癫痫发作区传播的多尺度信息传递。
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.