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Integrated information in the thermodynamic limit
journal contribution
posted on 2023-06-09, 23:32 authored by Miguel Aguilera, Ezequiel Di PaoloThe 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.
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Publication status
- Published
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- Published version
Journal
Neural NetworksISSN
0893-6080Publisher
ElsevierExternal DOI
Volume
114Page range
136-146Event location
United StatesDepartment affiliated with
- Informatics Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2021-04-12First Open Access (FOA) Date
2021-04-12First Compliant Deposit (FCD) Date
2021-04-12Usage metrics
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