Upper bounds for integrated information

Originally developed as a theory of consciousness, integrated information theory provides a mathematical framework to quantify the causal irreducibility of systems and subsets of units in the system. Specifically, mechanism integrated information quantifies how much of the causal powers of a subset of units in a state, also referred to as a mechanism, cannot be accounted for by its parts. If the causal powers of the mechanism can be fully explained by its parts, it is reducible and its integrated information is zero. Here, we study the upper bound of this measure and how it is achieved. We study mechanisms in isolation, groups of mechanisms, and groups of causal relations among mechanisms. We put forward new theoretical results that show mechanisms that share parts with each other cannot all achieve their maximum. We also introduce techniques to design systems that can maximize the integrated information of a subset of their mechanisms or relations. Our results can potentially be used to exploit the symmetries and constraints to reduce the computations significantly and to compare different connectivity profiles in terms of their maximal achievable integrated information.

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Publication Details

Journal
PLoS Computational Biology
Published
2024-08-05
DOI
https://doi.org/10.1371/journal.pcbi.1012323
Citations
5
Primary Topic
Neural dynamics and brain function
Type
article
Field-Weighted Citation Impact
1.03

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article

Upper bounds for integrated information

Giulio Tononi, Alireza Zaeemzadeh
5 citations
PLoS Computational Biology
Neural dynamics and brain function
1.03
article

Upper bounds for integrated information

Giulio Tononi, Alireza Zaeemzadeh
article en
5 citations

Abstract

Originally developed as a theory of consciousness, integrated information theory provides a mathematical framework to quantify the causal irreducibility of systems and subsets of units in the system. Specifically, mechanism integrated information quantifies how much of the causal powers of a subset of units in a state, also referred to as a mechanism, cannot be accounted for by its parts. If the causal powers of the mechanism can be fully explained by its parts, it is reducible and its integrated information is zero. Here, we study the upper bound of this measure and how it is achieved. We study mechanisms in isolation, groups of mechanisms, and groups of causal relations among mechanisms. We put forward new theoretical results that show mechanisms that share parts with each other cannot all achieve their maximum. We also introduce techniques to design systems that can maximize the integrated information of a subset of their mechanisms or relations. Our results can potentially be used to exploit the symmetries and constraints to reduce the computations significantly and to compare different connectivity profiles in terms of their maximal achievable integrated information.

PLoS Computational BiologyVol. 20(8)
University of Wisconsin–Madison (US)
University of Wisconsin-Madison, Tiny Blue Dot Foundation, Templeton World Charity Foundation
Openalex Percentile: Top 25%
Neural dynamics and brain function
1.03
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Upper bounds for integrated information — Giulio Tononi, Alireza Zaeemzadeh · PLoS Computational Biology (2024) | TGRS Research Map | TGRS