论文标题
作为因果信息整合的复杂性
Complexity as Causal Information Integration
论文作者
论文摘要
意识综合信息理论的背景下的复杂性测量试图量化不同神经元之间因果关系的强度。这是通过最大程度地减少完整系统与无因果关系之间的KL差异来完成的。在这种情况下，已经提出了各种措施并进行了比较。我们将讨论一系列信息几何措施，旨在评估系统中的内在因果影响。由$φ_{CIS} $表示这些措施的一个有希望的候选人是基于条件独立语句，并且确实满足了所有假定的属性。不幸的是，它没有图形表示，这使其不太直观且难以分析。我们提出了一种使用潜在变量来建模常见外部影响的替代方法。这导致了$φ_{CII} $，因果信息集成，满足所有必需条件。我们的度量可以使用迭代信息几何算法（EM-Algorithm）计算。因此，我们能够将其行为与现有的集成信息度量进行比较。
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.
