Complexity as Causal Information Integration.

A new measure of integrated information, called ΦCII (Causal Information Integration), is proposed as an alternative to existing measures that quantify the strength of causal connections between neurons in the context of Integrated Information Theory of consciousness. Unlike the candidate measure ΦCIS, which lacks a graphical representation and is difficult to analyze, ΦCII satisfies all desirable properties and can be calculated using an iterative information geometric algorithm (the em-algorithm). This allows comparison with existing integrated information measures.

Study at a glance

Abstract

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 cross-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 cross-influences in a system. One promising candidate of these measures, denoted by ΦCIS, 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 ΦCII, 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.