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Computational Mechanisms of Osmoregulation: A Reinforcement Learning Model for Sodium Appetite

Homeostatic control with oral nutrient intake is a vital complex system involving the orderly interactions between the external and internal senses, behavioral control, reward learning, and decision-making. Sodium appetite is a representative system and has been intensively investigated in animal mo...

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Autores principales: Uchida, Yuuki, Hikida, Takatoshi, Yamashita, Yuichi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9160331/
https://www.ncbi.nlm.nih.gov/pubmed/35663557
http://dx.doi.org/10.3389/fnins.2022.857009
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author Uchida, Yuuki
Hikida, Takatoshi
Yamashita, Yuichi
author_facet Uchida, Yuuki
Hikida, Takatoshi
Yamashita, Yuichi
author_sort Uchida, Yuuki
collection PubMed
description Homeostatic control with oral nutrient intake is a vital complex system involving the orderly interactions between the external and internal senses, behavioral control, reward learning, and decision-making. Sodium appetite is a representative system and has been intensively investigated in animal models of homeostatic systems and oral nutrient intake. However, the system-level mechanisms for regulating sodium intake behavior and homeostatic control remain unclear. In the current study, we attempted to provide a mechanistic understanding of sodium appetite behavior by using a computational model, the homeostatic reinforcement learning model, in which homeostatic behaviors are interpreted as reinforcement learning processes. Through simulation experiments, we confirmed that our homeostatic reinforcement learning model successfully reproduced homeostatic behaviors by regulating sodium appetite. These behaviors include the approach and avoidance behaviors to sodium according to the internal states of individuals. In addition, based on the assumption that the sense of taste is a predictor of changes in the internal state, the homeostatic reinforcement learning model successfully reproduced the previous paradoxical observations of the intragastric infusion test, which cannot be explained by the classical drive reduction theory. Moreover, we extended the homeostatic reinforcement learning model to multimodal data, and successfully reproduced the behavioral tests in which water and sodium appetite were mediated by each other. Finally, through an experimental simulation of chemical manipulation in a specific neural population in the brain stem, we proposed a testable hypothesis for the function of neural circuits involving sodium appetite behavior. The study results support the idea that osmoregulation via sodium appetitive behavior can be understood as a reinforcement learning process, and provide a mechanistic explanation for the underlying neural mechanisms of decision-making related to sodium appetite and homeostatic behavior.
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spelling pubmed-91603312022-06-03 Computational Mechanisms of Osmoregulation: A Reinforcement Learning Model for Sodium Appetite Uchida, Yuuki Hikida, Takatoshi Yamashita, Yuichi Front Neurosci Neuroscience Homeostatic control with oral nutrient intake is a vital complex system involving the orderly interactions between the external and internal senses, behavioral control, reward learning, and decision-making. Sodium appetite is a representative system and has been intensively investigated in animal models of homeostatic systems and oral nutrient intake. However, the system-level mechanisms for regulating sodium intake behavior and homeostatic control remain unclear. In the current study, we attempted to provide a mechanistic understanding of sodium appetite behavior by using a computational model, the homeostatic reinforcement learning model, in which homeostatic behaviors are interpreted as reinforcement learning processes. Through simulation experiments, we confirmed that our homeostatic reinforcement learning model successfully reproduced homeostatic behaviors by regulating sodium appetite. These behaviors include the approach and avoidance behaviors to sodium according to the internal states of individuals. In addition, based on the assumption that the sense of taste is a predictor of changes in the internal state, the homeostatic reinforcement learning model successfully reproduced the previous paradoxical observations of the intragastric infusion test, which cannot be explained by the classical drive reduction theory. Moreover, we extended the homeostatic reinforcement learning model to multimodal data, and successfully reproduced the behavioral tests in which water and sodium appetite were mediated by each other. Finally, through an experimental simulation of chemical manipulation in a specific neural population in the brain stem, we proposed a testable hypothesis for the function of neural circuits involving sodium appetite behavior. The study results support the idea that osmoregulation via sodium appetitive behavior can be understood as a reinforcement learning process, and provide a mechanistic explanation for the underlying neural mechanisms of decision-making related to sodium appetite and homeostatic behavior. Frontiers Media S.A. 2022-05-19 /pmc/articles/PMC9160331/ /pubmed/35663557 http://dx.doi.org/10.3389/fnins.2022.857009 Text en Copyright © 2022 Uchida, Hikida and Yamashita. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Uchida, Yuuki
Hikida, Takatoshi
Yamashita, Yuichi
Computational Mechanisms of Osmoregulation: A Reinforcement Learning Model for Sodium Appetite
title Computational Mechanisms of Osmoregulation: A Reinforcement Learning Model for Sodium Appetite
title_full Computational Mechanisms of Osmoregulation: A Reinforcement Learning Model for Sodium Appetite
title_fullStr Computational Mechanisms of Osmoregulation: A Reinforcement Learning Model for Sodium Appetite
title_full_unstemmed Computational Mechanisms of Osmoregulation: A Reinforcement Learning Model for Sodium Appetite
title_short Computational Mechanisms of Osmoregulation: A Reinforcement Learning Model for Sodium Appetite
title_sort computational mechanisms of osmoregulation: a reinforcement learning model for sodium appetite
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9160331/
https://www.ncbi.nlm.nih.gov/pubmed/35663557
http://dx.doi.org/10.3389/fnins.2022.857009
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