Free energy and inference in living systems

Biological systems maintain internal stability through mechanisms that remain partially unresolved. Prior research has established that organisms regulate homeostasis through biochemical work constrained by thermodynamic principles. However, recent studies propose that Bayesian inference may underlie higher-order homeostatic and allostatic regulation in organisms. This gap motivated the exploration of how thermodynamic and informational free-energy principles might converge in a unified framework. No prior work had resolved how perception and action could emerge from a shared free-energy minimization process. The distinction between physical and informational free energy remains a central uncertainty in systems biology. This uncertainty drives the need to integrate thermodynamic and neuroscientific perspectives on biological regulation. A unified framework could clarify how sensory uncertainty is minimized through active inference in neural systems.

This study aims to unify thermodynamic and neuroscientific free-energy principles into a single framework for understanding biological regulation. The specific problem addressed is how perception and action emerge from free-energy minimization in living systems. The motivation stems from the need to reconcile physical and informational perspectives on homeostasis. The study proposes that active inference in the brain arises from minimizing sensory uncertainty. This approach seeks to explain how neural mechanics contribute to dynamic bifurcations between attractor states. The goal is to model how Bayesian inference in the brain aligns with thermodynamic constraints. The framework aims to clarify the role of neural manifolds in optimizing perception and action. The study tests whether free-energy minimization can account for both metabolic and inferential processes.

The study employs a theoretical framework integrating thermodynamic and Bayesian principles. It uses mathematical modeling to describe free-energy minimization in neural systems. The approach involves analyzing how sensory uncertainty is minimized through active inference. The model incorporates neural manifolds to represent optimal trajectories in perception and action. The study draws on concepts from statistical mechanics and information theory. It applies Schrödinger's machine as a metaphor for neural inference processes. The framework is tested through simulations of dynamic bifurcations between neural attractors. The model emphasizes how Bayesian inference aligns with metabolic constraints in biological systems.

The strongest finding is that perception and action in animals result from free-energy minimization in the brain. The brain operates as a Schrödinger's machine minimizing sensory uncertainty. The model suggests that Bayesian inference generates optimal trajectories in neural manifolds. Dynamic bifurcations between neural attractors emerge during active inference. The study demonstrates how thermodynamic and informational free-energy principles converge. The results show that free-energy minimization underlies both metabolic and inferential processes. The model accounts for how sensory uncertainty is minimized through active inference. The findings propose that neural mechanics facilitate homeostasis and allostasis through free-energy regulation.

The authors propose that free-energy minimization unifies thermodynamic and Bayesian principles in biological systems. They suggest that perception and action arise from minimizing sensory uncertainty in the brain. The study concludes that the brain functions as a Schrödinger's machine conducting neural mechanics. The findings indicate that Bayesian inference aligns with metabolic constraints in living systems. The authors claim that dynamic bifurcations between neural attractors emerge from active inference. The study implies that neural manifolds optimize trajectories for perception and action. The conclusions emphasize the role of free-energy minimization in both homeostasis and allostasis. The authors propose that this framework provides a parsimonious explanation for biological regulation.