Free energy and inference in living systems

TL;DR

This paper presents an integrated free-energy minimization framework that unifies thermodynamic and neuroscientific principles, explaining how living systems and brains perform active inference to maintain homeostasis and adapt.

Contribution

It introduces a comprehensive theory combining thermodynamic and informational free-energy principles, elucidating neural dynamics and active inference in living organisms.

Findings

Animals' perception and action arise from active inference driven by free-energy minimization.

The brain functions as a Schrödinger's machine, reducing sensory uncertainty through neural mechanics.

The model predicts optimal neural trajectories and bifurcations in neural attractors during active inference.

Abstract

Organisms are nonequilibrium, stationary systems self-organized via spontaneous symmetry breaking and undergoing metabolic cycles with broken detailed balance in the environment. The thermodynamic free-energy principle describes an organism's homeostasis as the regulation of biochemical work constrained by the physical free-energy cost. In contrast, recent research in neuroscience and theoretical biology explains a higher organism's homeostasis and allostasis as Bayesian inference facilitated by the informational free energy. As an integrated approach to living systems, this study presents a free-energy minimization theory overarching the essential features of both the thermodynamic and neuroscientific free-energy principles. Our results reveal that the perception and action of animals result from active inference entailed by free-energy minimization in the brain, and the brain operates as Schr{\"o}dinger's machine conducting the neural mechanics of minimizing sensory uncertainty. A parsimonious model suggests that the Bayesian brain develops the optimal trajectories in neural manifolds and induces a dynamic bifurcation between neural attractors in the process of active inference.