Information Flows in Causal Networks

Mathematical information theory provides an important framework for understanding cognitive processes. It has been successfully applied to neural systems displaying feed-forward structures. It turns out that the analysis of recurrent structures is more subtle. This is mainly due to the fact that corresponding information-theoretic quantities allow for the ambiguity of their causal and associational interpretations. In order to understand information flows in recurrent networks, one has to make a clear distinction between these two interpretations. In collaboration with Daniel Polani we addressed this problem using a causality theory developed by Judea Pearl and his coworkers. I will discuss some possible applications of this work to complexity theory.