Integrated information in the thermodynamic limit

Aguilera, Miguel and Di Paolo, Ezequiel A (2019) Integrated information in the thermodynamic limit. Neural Networks, 114. pp. 136-146. ISSN 0893-6080

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Abstract

The capacity to integrate information is a prominent feature of biological, neural, and cognitive processes. Integrated Information Theory (IIT) provides mathematical tools for quantifying the level of integration in a system, but its computational cost generally precludes 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 integration as it interacts with its environment. After revising some assumptions of the theory, we show for the first time how modified measures of information integration scale when a neural network becomes very large. Using kinetic Ising models and mean-field approximations, we show that information integration diverges in the thermodynamic limit at certain critical points. Moreover, by comparing different divergent tendencies of blocks that make up a system at these critical points, we can use information integration to 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.

Item Type: Article
Keywords: Criticality, Integrated information, Ising model, Mean-field, Phi, Thermodynamic limit, Humans, Information Theory, Neural Networks, Computer, Thermodynamics
Schools and Departments: School of Engineering and Informatics > Informatics
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 12 Apr 2021 07:23
Last Modified: 12 Apr 2021 07:30
URI: http://sro.sussex.ac.uk/id/eprint/98341

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