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A plastic corticostriatal circuit model of adaptation in perceptual decision making

The ability to optimize decisions and adapt them to changing environments is a crucial brain function that increase survivability. Although much has been learned about the neuronal activity in various brain regions that are associated with decision making, and about how the nervous systems may learn...

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Autores principales: Hsiao, Pao-Yueh, Lo, Chung-Chuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3857537/
https://www.ncbi.nlm.nih.gov/pubmed/24339814
http://dx.doi.org/10.3389/fncom.2013.00178
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author Hsiao, Pao-Yueh
Lo, Chung-Chuan
author_facet Hsiao, Pao-Yueh
Lo, Chung-Chuan
author_sort Hsiao, Pao-Yueh
collection PubMed
description The ability to optimize decisions and adapt them to changing environments is a crucial brain function that increase survivability. Although much has been learned about the neuronal activity in various brain regions that are associated with decision making, and about how the nervous systems may learn to achieve optimization, the underlying neuronal mechanisms of how the nervous systems optimize decision strategies with preference given to speed or accuracy, and how the systems adapt to changes in the environment, remain unclear. Based on extensive empirical observations, we addressed the question by extending a previously described cortico-basal ganglia circuit model of perceptual decisions with the inclusion of a dynamic dopamine (DA) system that modulates spike-timing dependent plasticity (STDP). We found that, once an optimal model setting that maximized the reward rate was selected, the same setting automatically optimized decisions across different task environments through dynamic balancing between the facilitating and depressing components of the DA dynamics. Interestingly, other model parameters were also optimal if we considered the reward rate that was weighted by the subject's preferences for speed or accuracy. Specifically, the circuit model favored speed if we increased the phasic DA response to the reward prediction error, whereas the model favored accuracy if we reduced the tonic DA activity or the phasic DA responses to the estimated reward probability. The proposed model provides insight into the roles of different components of DA responses in decision adaptation and optimization in a changing environment.
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spelling pubmed-38575372013-12-11 A plastic corticostriatal circuit model of adaptation in perceptual decision making Hsiao, Pao-Yueh Lo, Chung-Chuan Front Comput Neurosci Neuroscience The ability to optimize decisions and adapt them to changing environments is a crucial brain function that increase survivability. Although much has been learned about the neuronal activity in various brain regions that are associated with decision making, and about how the nervous systems may learn to achieve optimization, the underlying neuronal mechanisms of how the nervous systems optimize decision strategies with preference given to speed or accuracy, and how the systems adapt to changes in the environment, remain unclear. Based on extensive empirical observations, we addressed the question by extending a previously described cortico-basal ganglia circuit model of perceptual decisions with the inclusion of a dynamic dopamine (DA) system that modulates spike-timing dependent plasticity (STDP). We found that, once an optimal model setting that maximized the reward rate was selected, the same setting automatically optimized decisions across different task environments through dynamic balancing between the facilitating and depressing components of the DA dynamics. Interestingly, other model parameters were also optimal if we considered the reward rate that was weighted by the subject's preferences for speed or accuracy. Specifically, the circuit model favored speed if we increased the phasic DA response to the reward prediction error, whereas the model favored accuracy if we reduced the tonic DA activity or the phasic DA responses to the estimated reward probability. The proposed model provides insight into the roles of different components of DA responses in decision adaptation and optimization in a changing environment. Frontiers Media S.A. 2013-12-10 /pmc/articles/PMC3857537/ /pubmed/24339814 http://dx.doi.org/10.3389/fncom.2013.00178 Text en Copyright © 2013 Hsiao and Lo. http://creativecommons.org/licenses/by/3.0/ 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) or licensor 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
Hsiao, Pao-Yueh
Lo, Chung-Chuan
A plastic corticostriatal circuit model of adaptation in perceptual decision making
title A plastic corticostriatal circuit model of adaptation in perceptual decision making
title_full A plastic corticostriatal circuit model of adaptation in perceptual decision making
title_fullStr A plastic corticostriatal circuit model of adaptation in perceptual decision making
title_full_unstemmed A plastic corticostriatal circuit model of adaptation in perceptual decision making
title_short A plastic corticostriatal circuit model of adaptation in perceptual decision making
title_sort plastic corticostriatal circuit model of adaptation in perceptual decision making
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3857537/
https://www.ncbi.nlm.nih.gov/pubmed/24339814
http://dx.doi.org/10.3389/fncom.2013.00178
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