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Drive competition underlies effective allostatic orchestration
Living systems ensure their fitness by self-regulating. The optimal matching of their behavior to the opportunities and demands of the ever-changing natural environment is crucial for satisfying physiological and cognitive needs. Although homeostasis has explained how organisms maintain their intern...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9755511/ https://www.ncbi.nlm.nih.gov/pubmed/36530500 http://dx.doi.org/10.3389/frobt.2022.1052998 |
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author | Rosado, Oscar Guerrero Amil, Adrian F. Freire, Ismael T. Verschure, Paul F. M. J. |
author_facet | Rosado, Oscar Guerrero Amil, Adrian F. Freire, Ismael T. Verschure, Paul F. M. J. |
author_sort | Rosado, Oscar Guerrero |
collection | PubMed |
description | Living systems ensure their fitness by self-regulating. The optimal matching of their behavior to the opportunities and demands of the ever-changing natural environment is crucial for satisfying physiological and cognitive needs. Although homeostasis has explained how organisms maintain their internal states within a desirable range, the problem of orchestrating different homeostatic systems has not been fully explained yet. In the present paper, we argue that attractor dynamics emerge from the competitive relation of internal drives, resulting in the effective regulation of adaptive behaviors. To test this hypothesis, we develop a biologically-grounded attractor model of allostatic orchestration that is embedded into a synthetic agent. Results show that the resultant neural mass model allows the agent to reproduce the navigational patterns of a rodent in an open field. Moreover, when exploring the robustness of our model in a dynamically changing environment, the synthetic agent pursues the stability of the self, being its internal states dependent on environmental opportunities to satisfy its needs. Finally, we elaborate on the benefits of resetting the model’s dynamics after drive-completion behaviors. Altogether, our studies suggest that the neural mass allostatic model adequately reproduces self-regulatory dynamics while overcoming the limitations of previous models. |
format | Online Article Text |
id | pubmed-9755511 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97555112022-12-17 Drive competition underlies effective allostatic orchestration Rosado, Oscar Guerrero Amil, Adrian F. Freire, Ismael T. Verschure, Paul F. M. J. Front Robot AI Robotics and AI Living systems ensure their fitness by self-regulating. The optimal matching of their behavior to the opportunities and demands of the ever-changing natural environment is crucial for satisfying physiological and cognitive needs. Although homeostasis has explained how organisms maintain their internal states within a desirable range, the problem of orchestrating different homeostatic systems has not been fully explained yet. In the present paper, we argue that attractor dynamics emerge from the competitive relation of internal drives, resulting in the effective regulation of adaptive behaviors. To test this hypothesis, we develop a biologically-grounded attractor model of allostatic orchestration that is embedded into a synthetic agent. Results show that the resultant neural mass model allows the agent to reproduce the navigational patterns of a rodent in an open field. Moreover, when exploring the robustness of our model in a dynamically changing environment, the synthetic agent pursues the stability of the self, being its internal states dependent on environmental opportunities to satisfy its needs. Finally, we elaborate on the benefits of resetting the model’s dynamics after drive-completion behaviors. Altogether, our studies suggest that the neural mass allostatic model adequately reproduces self-regulatory dynamics while overcoming the limitations of previous models. Frontiers Media S.A. 2022-12-02 /pmc/articles/PMC9755511/ /pubmed/36530500 http://dx.doi.org/10.3389/frobt.2022.1052998 Text en Copyright © 2022 Rosado, Amil, Freire and Verschure. 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 | Robotics and AI Rosado, Oscar Guerrero Amil, Adrian F. Freire, Ismael T. Verschure, Paul F. M. J. Drive competition underlies effective allostatic orchestration |
title | Drive competition underlies effective allostatic orchestration |
title_full | Drive competition underlies effective allostatic orchestration |
title_fullStr | Drive competition underlies effective allostatic orchestration |
title_full_unstemmed | Drive competition underlies effective allostatic orchestration |
title_short | Drive competition underlies effective allostatic orchestration |
title_sort | drive competition underlies effective allostatic orchestration |
topic | Robotics and AI |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9755511/ https://www.ncbi.nlm.nih.gov/pubmed/36530500 http://dx.doi.org/10.3389/frobt.2022.1052998 |
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