A general spectral decomposition of causal influences applied to integrated information
A general spectral decomposition of causal influences applied to integrated information
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DOI：
10.1016/j.jneumeth.2019.108443
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发表时间：
2020-01-15
影响因子：
3
通讯作者：
Oizumi, Masafumi
中科院分区：
文献类型：
--
作者：
Cohen, Dror;Sasai, Shuntaro;Oizumi, Masafumi
背景资料：量化许多神经元之间的相互作用是理解系统级现象的基础，如注意力，学习甚至意识体验。大脑中的因果影响被量化为综合信息，被认为支持主观意识体验。最近的实证工作表明，因果影响的频谱分解，例如使用格兰杰因果关系，可以揭示在时域中未观察到的频率特定的影响。然而，谱分解的综合信息还没有提出，限制了其采用的分析neural data.New方法：我们提出了一个通用的和灵活的框架，推导的谱分解的因果自回归过程。我们表明，其他著名的措施，包括格兰杰因果关系，可以使用相同的框架。使用模拟，我们证明了一个复杂的相互作用的频谱分解的综合信息和其他措施，是没有观察到在时域。与现有的方法比较：本文首次提供了一个频谱分解的综合信息。虽然频谱分解的格兰杰因果关系已被推导出，该方法只适用于单向的因果关系的影响，而不是多方向的因果关系的影响所需的综合information.Conclusions：我们的新框架可以用来推导出的频谱分解的单向和多向的因果关系的影响措施。我们使用这个框架来获得综合信息的谱分解，为更好地理解大脑中特定频率的因果影响与认知的关系铺平了道路。
Background: Quantifying interactions among many neurons is fundamental to understanding system-level phenomena such as attention, learning and even conscious experience. Causal influences in the brain, quantified as integrated information, are thought to support subjective conscious experience. Recent empirical work has shown that the spectral decomposition of causal influences, for example using Granger causality, can reveal frequency-specific influences that are not observed in the time domain. However, a spectral decomposition of integrated information has not been put forward, limiting its adoption for analyzing neural data.New method: We present a general and flexible framework for deriving the spectral decomposition of causal influences in autoregressive processes.Results: We use the framework to derive a spectral decomposition of integrated information. We show that other well-known measures, including Granger causality, can be derived using the same framework. Using simulations, we demonstrate a complex interplay between the spectral decomposition of integrated information and other measures that is not observed in the time domain.Comparison with existing methods: This paper provides a spectral decomposition of integrated information for the first time. Although a spectral decomposition of Granger causality has been derived, that approach is only applicable to uni-directional causal influences, not multi-directional causal influences as required for integrated information.Conclusions: Our novel framework can be used to derive the spectral decomposition of uni- and multi-directional measures of causal influences. We use this framework to derive a spectral decomposition of integrated information, paving the way for better understanding how frequency-specific causal influences in the brain relate to cognition.