论文标题
在共同复杂动态过程中的因果分解的扩展
Extension of causal decomposition in the mutual complex dynamic process
论文作者
论文摘要
因果分解描述了不是基于预测概念而是基于时间序列的阶段依赖性的原因效应关系。它在随机和确定性系统中均已验证，现在可以预期其在复杂的动态过程中的应用。在这里，我们介绍了共同复杂动态过程中因果分解的扩展：时间序列的原因和效果是在类似的时间尺度的内在组件的分解中遗传的。此外，我们说明了与神经科学中使用的主要方法的比较研究，并显示了该方法在脑肌肉相互作用中的适用性，特别是对生理时间序列的适用性，这意味着在复杂的生理过程中对因果关系分析的潜力。
Causal decomposition depicts a cause-effect relationship that is not based on the concept of prediction, but based on the phase dependence of time series. It has been validated in both stochastic and deterministic systems and is now anticipated for its application in the complex dynamic process. Here, we present an extension of causal decomposition in the mutual complex dynamic process: cause and effect of time series are inherited in the decomposition of intrinsic components in a similar time scale. Furthermore, we illustrate comparative studies with predominate methods used in neuroscience, and show the applicability of the method particularly to physiological time series in brain-muscle interactions, implying the potential to the causality analysis in the complex physiological process.
