Integrated information and dimensionality in continuous attractor dynamics

TL;DR

This paper introduces a topological dimensionality approach to measure integrated information in continuous attractor dynamics, addressing practical and theoretical challenges in applying IIT to neuronal signals.

Contribution

It proposes using topological dimensionality of attractor dynamics as an invariant indicator of integrated information, extending IIT to continuous systems and enabling empirical testing.

Findings

Dimensionality reflects integrated information in attractor dynamics.

Delay embedding reconstructs unobserved node effects.

Framework aligns with neural data from animals.

Abstract

There has been increasing interest in the integrated information theory (IIT) ofconsciousness, which hypothesizes that consciousness is integrated information withinneuronal dynamics. However, the current formulation of IIT poses both practical andtheoretical problems when we aim to empirically test the theory by computingintegrated information from neuronal signals. For example, measuring integratedinformation requires observing all the elements in the considered system at the sametime, but this is practically rather difficult. In addition, the interpretation of the spatialpartition needed to compute integrated information becomes vague in continuous time-series variables due to a general property of nonlinear dynamical systems known as"embedding." Here, we propose that some aspects of such problems are resolved byconsidering the topological dimensionality of shared attractor dynamics as an indicatorof integrated information in continuous attractor dynamics. In this formulation, theeffects of unobserved nodes on the attractor dynamics can be reconstructed using atechnique called delay embedding, which allows us to identify the dimensionality of anembedded attractor from partial observations. We propose that the topologicaldimensionality represents a critical property of integrated information, as it is invariantto general coordinate transformations. We illustrate this new framework with simpleexamples and discuss how it fits together with recent findings based on neuralrecordings from awake and anesthetized animals. This topological approach extendsthe existing notions of IIT to continuous dynamical systems and offers a much-neededframework for testing the theory with experimental data by substantially relaxing theconditions required for evaluating integrated information in real neural systems.