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Quantifying the Landscape of Decision Making From Spiking Neural Networks
The decision making function is governed by the complex coupled neural circuit in the brain. The underlying energy landscape provides a global picture for the dynamics of the neural decision making system and has been described extensively in the literature, but often as illustrations. In this work,...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8581041/ https://www.ncbi.nlm.nih.gov/pubmed/34776914 http://dx.doi.org/10.3389/fncom.2021.740601 |
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author | Ye, Leijun Li, Chunhe |
author_facet | Ye, Leijun Li, Chunhe |
author_sort | Ye, Leijun |
collection | PubMed |
description | The decision making function is governed by the complex coupled neural circuit in the brain. The underlying energy landscape provides a global picture for the dynamics of the neural decision making system and has been described extensively in the literature, but often as illustrations. In this work, we explicitly quantified the landscape for perceptual decision making based on biophysically-realistic cortical network with spiking neurons to mimic a two-alternative visual motion discrimination task. Under certain parameter regions, the underlying landscape displays bistable or tristable attractor states, which quantify the transition dynamics between different decision states. We identified two intermediate states: the spontaneous state which increases the plasticity and robustness of changes of minds and the “double-up” state which facilitates the state transitions. The irreversibility of the bistable and tristable switches due to the probabilistic curl flux demonstrates the inherent non-equilibrium characteristics of the neural decision system. The results of global stability of decision-making quantified by barrier height inferred from landscape topography and mean first passage time are in line with experimental observations. These results advance our understanding of the stochastic and dynamical transition mechanism of decision-making function, and the landscape and kinetic path approach can be applied to other cognitive function related problems (such as working memory) in brain networks. |
format | Online Article Text |
id | pubmed-8581041 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85810412021-11-12 Quantifying the Landscape of Decision Making From Spiking Neural Networks Ye, Leijun Li, Chunhe Front Comput Neurosci Neuroscience The decision making function is governed by the complex coupled neural circuit in the brain. The underlying energy landscape provides a global picture for the dynamics of the neural decision making system and has been described extensively in the literature, but often as illustrations. In this work, we explicitly quantified the landscape for perceptual decision making based on biophysically-realistic cortical network with spiking neurons to mimic a two-alternative visual motion discrimination task. Under certain parameter regions, the underlying landscape displays bistable or tristable attractor states, which quantify the transition dynamics between different decision states. We identified two intermediate states: the spontaneous state which increases the plasticity and robustness of changes of minds and the “double-up” state which facilitates the state transitions. The irreversibility of the bistable and tristable switches due to the probabilistic curl flux demonstrates the inherent non-equilibrium characteristics of the neural decision system. The results of global stability of decision-making quantified by barrier height inferred from landscape topography and mean first passage time are in line with experimental observations. These results advance our understanding of the stochastic and dynamical transition mechanism of decision-making function, and the landscape and kinetic path approach can be applied to other cognitive function related problems (such as working memory) in brain networks. Frontiers Media S.A. 2021-10-28 /pmc/articles/PMC8581041/ /pubmed/34776914 http://dx.doi.org/10.3389/fncom.2021.740601 Text en Copyright © 2021 Ye and Li. 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 Ye, Leijun Li, Chunhe Quantifying the Landscape of Decision Making From Spiking Neural Networks |
title | Quantifying the Landscape of Decision Making From Spiking Neural Networks |
title_full | Quantifying the Landscape of Decision Making From Spiking Neural Networks |
title_fullStr | Quantifying the Landscape of Decision Making From Spiking Neural Networks |
title_full_unstemmed | Quantifying the Landscape of Decision Making From Spiking Neural Networks |
title_short | Quantifying the Landscape of Decision Making From Spiking Neural Networks |
title_sort | quantifying the landscape of decision making from spiking neural networks |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8581041/ https://www.ncbi.nlm.nih.gov/pubmed/34776914 http://dx.doi.org/10.3389/fncom.2021.740601 |
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