Semantic Information Theory in a feedback-control Kuramoto Model

Abstract

Semantic Information Theory (SIT) offers a new approach to evaluating the information architecture of complex systems. In this study we describe the steps required to {\it operationalize} SIT via its application to dynamical problems. Our road map has four steps: (1) separating the dynamical system into agent-environment sub-systems; (2) choosing an appropriate coarse graining and quantifying correlations; (3) identifying a measure of viability; (4) implementing a scrambling protocol and measuring the semantic content. We apply the road map to a model inspired by the neural dynamics of epileptic seizures whereby an agent (a control process) attempts to maintain an environment (a base process) in a desynchronized state. The synchronization dynamics is studied through the well-known Kuramoto model of phase synchronization. Our application of SIT to this problem reveals new features of both semantic information and the Kuramoto model. For the latter we find articulating the correlational structure for agent and environment(the oscillators), allows us to cast the model in in a novel computational (information theoretic) perspective, where the agent-environment dynamics can be thought of as analyzing a communication channel. For the former we find that all the information in our system is semantic. This is in contrast to previous SIT studies of foragers in which semantic thresholds where seen above which no further semantic content was obtained.