Integrated information and dimensionality in continuous attractor dynamics

A new framework proposes that the topological dimensionality of shared attractor dynamics can serve as an indicator of integrated information in continuous dynamical systems, addressing practical and theoretical problems in testing integrated information theory (IIT) of consciousness with neuronal signals. The approach uses delay embedding to reconstruct the effects of unobserved nodes on attractor dynamics, allowing identification of the embedded attractor's dimensionality from partial observations. This topological measure is invariant to general coordinate transformations, extending IIT to continuous systems and relaxing conditions needed for evaluating integrated information in real neural data.

Study at a glance

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.