作为因果信息整合的复杂性
Complexity as Causal Information Integration
摘要：
在意识的整合信息论的背景下，复杂性测量试图量化不同神经元之间因果联系的强度。这是通过最小化一个完整的系统和一个没有因果关系的系统之间的KL发散来完成的。在这种情况下，已经提出了各种措施，并进行了比较。我们将讨论一类旨在评估系统中内在因果影响的信息几何度量。这些措施的一个有前途的候选人，表示为$Φ_{CIS}$，是基于条件独立性声明，并满足所有的属性，已被假定为理想的。不幸的是，它没有图形表示，这使得它不那么直观，难以分析。我们提出了一种替代方法，使用一个潜在的变量模型一个共同的外部影响。这导致了一个度量$Φ_{CII}$，因果信息整合，它满足所有的必要条件。我们的测量可以使用迭代信息几何算法（em算法）来计算。因此，我们能够将其行为与现有的综合信息措施进行比较。
英文摘要：
Complexity measures in the context of the Integrated Information Theory of consciousness try to quantify the strength of the causal connections between different neurons. This is done by minimizing the KL-divergence between a full system and one without causal connections. Various measures have been proposed and compared in this setting. We will discuss a class of information geometric measures that aim at assessing the intrinsic causal influences in a system. One promising candidate of these measures, denoted by $Φ_{CIS}$, is based on conditional independence statements and does satisfy all of the properties that have been postulated as desirable. Unfortunately it does not have a graphical representation which makes it less intuitive and difficult to analyze. We propose an alternative approach using a latent variable which models a common exterior influence. This leads to a measure $Φ_{CII}$, Causal Information Integration, that satisfies all of the required conditions. Our measure can be calculated using an iterative information geometric algorithm, the em-algorithm. Therefore we are able to compare its behavior to existing integrated information measures.
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