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Bridging integrated information theory and the free-energy principle in living neuronal networks

Teruki Mayama, Sota Shimizu, Yuki Takano, Dai Akita, Hirokazu Takahashi

arXiv Preprint Archive October 5, 2025 via arXiv

Summary

AI-generated from the abstract

Repeated stimulation from hidden sources caused neuronal cultures to develop source selectivity. Variational free energy decreased across sessions while accuracy and Bayesian surprise increased. A proxy measure of integrated information and the size of the main complex followed a hill-shaped trajectory, with informational cores organizing diverse neuronal activity. Integrated information correlated strongly and positively with Bayesian surprise, modestly and heterogeneously with accuracy, and showed no significant relationship with variational free energy. The positive coupling between integrated information and Bayesian surprise likely reflects the diversity of activity observed in critical dynamics. These findings suggest integrated information increases specifically during belief updating when sensory inputs are most informative, rather than tracking model efficiency.

Study at a glance

Characteristics Observational study Peer reviewed
Population Dissociated neuronal cultures
Keywords Q-bio.nc
Key finding Integrated information correlates positively with Bayesian surprise and follows a hill-shaped trajectory during inference, increasing specifically during belief updating when sensory inputs are most informative.

Abstract

The relationship between Integrated Information Theory (IIT) and the Free-Energy Principle (FEP) remains unresolved, particularly with respect to how integrated information, proposed as the intrinsic substrate of consciousness, behaves within variational Bayesian inference. We investigated this issue using dissociated neuronal cultures, previously shown to perform perceptual inference consistent with the FEP. Repeated stimulation from hidden sources induced robust source selectivity: variational free energy (VFE) decreased across sessions, whereas accuracy and Bayesian surprise (complexity) increased. Network-level analyses revealed that a proxy measure of integrated information and the size of the main complex followed a hill-shaped trajectory, with informational cores organizing diverse neuronal activity. Across experiments, integrated information correlated strongly and positively with Bayesian surprise, modestly and heterogeneously with accuracy, and showed no significant relationship with VFE. The positive coupling between Φ and Bayesian surprise likely reflects the diversity of activity observed in critical dynamics. These findings suggest that integrated information increases specifically during belief updating, when sensory inputs are most informative, rather than tracking model efficiency. The hill-shaped trajectory of Φ during inference can be functionally interpreted as a transition from exploration to exploitation. This work provides empirical evidence linking the physical account of consciousness advanced by IIT with the functional perspective offered by the FEP, contributing to a unified framework for the mechanisms and adaptive roles of phenomenology.

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