Integrated information in the thermodynamic limit

TL;DR

This paper investigates how measures of information integration scale in large systems using statistical mechanics, revealing divergence at critical points and proposing a model that maintains integration amidst environmental changes.

Contribution

It introduces a novel analysis of information integration in the thermodynamic limit using kinetic Ising models and mean-field theory, highlighting divergence and adaptive maintenance of integration.

Findings

Information integration diverges at critical points in large systems.

Different divergence patterns help distinguish system units from the environment.

A model demonstrates adaptive maintenance of integration across environmental changes.

Abstract

The capacity to integrate information is a prominent feature of biological and cognitive systems. Integrated Information Theory (IIT) provides a mathematical approach to quantify the level of integration in a system, yet its computational cost generally precludes its applications beyond relatively small models. In consequence, it is not yet well understood how integration scales up with the size of a system or with different temporal scales of activity, nor how a system maintains its integration as its interacts with its environment. Here, we show for the first time how measures of information integration scale when systems become very large. Using kinetic Ising models and mean-field approximations from statistical mechanics, we show that information integration diverges in the thermodynamic limit at certain critical points. Moreover, by comparing different divergent tendencies of blocks of a system at these critical points, we delimit the boundary between an integrated unit and its environment. Finally, we present a model that adaptively maintains its integration despite changes in its environment by generating a critical surface where its integrity is preserved. We argue that the exploration of integrated information for these limit cases helps in addressing a variety of poorly understood questions about the organization of biological, neural, and cognitive systems.