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Free energy and inference in living systems

Organisms are non-equilibrium, stationary systems self-organized via spontaneous symmetry breaking and undergoing metabolic cycles with broken detailed balance in the environment. The thermodynamic free-energy (FE) principle describes an organism’s homeostasis as the regulation of biochemical work c...

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Autor principal: Kim, Chang Sub
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10102732/
https://www.ncbi.nlm.nih.gov/pubmed/37065269
http://dx.doi.org/10.1098/rsfs.2022.0041
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author Kim, Chang Sub
author_facet Kim, Chang Sub
author_sort Kim, Chang Sub
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description Organisms are non-equilibrium, stationary systems self-organized via spontaneous symmetry breaking and undergoing metabolic cycles with broken detailed balance in the environment. The thermodynamic free-energy (FE) principle describes an organism’s homeostasis as the regulation of biochemical work constrained by the physical FE cost. By contrast, recent research in neuroscience and theoretical biology explains a higher organism’s homeostasis and allostasis as Bayesian inference facilitated by the informational FE. As an integrated approach to living systems, this study presents an FE minimization theory overarching the essential features of both the thermodynamic and neuroscientific FE principles. Our results reveal that the perception and action of animals result from active inference entailed by FE minimization in the brain, and the brain operates as a Schrödinger’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.
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spelling pubmed-101027322023-04-15 Free energy and inference in living systems Kim, Chang Sub Interface Focus Articles Organisms are non-equilibrium, stationary systems self-organized via spontaneous symmetry breaking and undergoing metabolic cycles with broken detailed balance in the environment. The thermodynamic free-energy (FE) principle describes an organism’s homeostasis as the regulation of biochemical work constrained by the physical FE cost. By contrast, recent research in neuroscience and theoretical biology explains a higher organism’s homeostasis and allostasis as Bayesian inference facilitated by the informational FE. As an integrated approach to living systems, this study presents an FE minimization theory overarching the essential features of both the thermodynamic and neuroscientific FE principles. Our results reveal that the perception and action of animals result from active inference entailed by FE minimization in the brain, and the brain operates as a Schrödinger’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. The Royal Society 2023-04-14 /pmc/articles/PMC10102732/ /pubmed/37065269 http://dx.doi.org/10.1098/rsfs.2022.0041 Text en © 2023 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited.
spellingShingle Articles
Kim, Chang Sub
Free energy and inference in living systems
title Free energy and inference in living systems
title_full Free energy and inference in living systems
title_fullStr Free energy and inference in living systems
title_full_unstemmed Free energy and inference in living systems
title_short Free energy and inference in living systems
title_sort free energy and inference in living systems
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10102732/
https://www.ncbi.nlm.nih.gov/pubmed/37065269
http://dx.doi.org/10.1098/rsfs.2022.0041
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