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A Circuit Model for Working Memory Based on Hybrid Positive and Negative-Derivative Feedback Mechanism

Working memory (WM) plays an important role in cognitive activity. The WM system is used to temporarily store information in learning and decision-making. WM always functions in many aspects of daily life, such as the short-term memory of words, cell phone verification codes, and cell phone numbers....

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Autores principales: Wei, Hui, Jin, Xiao, Su, Zihao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9139460/
https://www.ncbi.nlm.nih.gov/pubmed/35624934
http://dx.doi.org/10.3390/brainsci12050547
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author Wei, Hui
Jin, Xiao
Su, Zihao
author_facet Wei, Hui
Jin, Xiao
Su, Zihao
author_sort Wei, Hui
collection PubMed
description Working memory (WM) plays an important role in cognitive activity. The WM system is used to temporarily store information in learning and decision-making. WM always functions in many aspects of daily life, such as the short-term memory of words, cell phone verification codes, and cell phone numbers. In young adults, studies have shown that a central memory store is limited to three to five meaningful items. Little is known about how WM functions at the microscopic neural level, but appropriate neural network computational models can help us gain a better understanding of it. In this study, we attempt to design a microscopic neural network model to explain the internal mechanism of WM. The performance of existing positive feedback models depends on the parameters of a synapse. We use a negative-derivative feedback mechanism to counteract the drift in persistent activity, making the hybrid positive and negative-derivative feedback (HPNF) model more robust to common disturbances. To fulfill the mechanism of WM at the neural circuit level, we construct two main neural networks based on the HPNF model: a memory-storage sub-network (the memory-storage sub-network is composed of several sets of neurons, so we call it “SET network”, or “SET” for short) with positive feedback and negative-derivative feedback and a storage distribution network (SDN) designed by combining SET for memory item storage and memory updating. The SET network is a neural information self-sustaining mechanism, which is robust to common disturbances; the SDN constructs a storage distribution network at the neural circuit level; the experimental results show that our network can fulfill the storage, association, updating, and forgetting of information at the level of neural circuits, and it can work in different individuals with little change in parameters.
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spelling pubmed-91394602022-05-28 A Circuit Model for Working Memory Based on Hybrid Positive and Negative-Derivative Feedback Mechanism Wei, Hui Jin, Xiao Su, Zihao Brain Sci Article Working memory (WM) plays an important role in cognitive activity. The WM system is used to temporarily store information in learning and decision-making. WM always functions in many aspects of daily life, such as the short-term memory of words, cell phone verification codes, and cell phone numbers. In young adults, studies have shown that a central memory store is limited to three to five meaningful items. Little is known about how WM functions at the microscopic neural level, but appropriate neural network computational models can help us gain a better understanding of it. In this study, we attempt to design a microscopic neural network model to explain the internal mechanism of WM. The performance of existing positive feedback models depends on the parameters of a synapse. We use a negative-derivative feedback mechanism to counteract the drift in persistent activity, making the hybrid positive and negative-derivative feedback (HPNF) model more robust to common disturbances. To fulfill the mechanism of WM at the neural circuit level, we construct two main neural networks based on the HPNF model: a memory-storage sub-network (the memory-storage sub-network is composed of several sets of neurons, so we call it “SET network”, or “SET” for short) with positive feedback and negative-derivative feedback and a storage distribution network (SDN) designed by combining SET for memory item storage and memory updating. The SET network is a neural information self-sustaining mechanism, which is robust to common disturbances; the SDN constructs a storage distribution network at the neural circuit level; the experimental results show that our network can fulfill the storage, association, updating, and forgetting of information at the level of neural circuits, and it can work in different individuals with little change in parameters. MDPI 2022-04-26 /pmc/articles/PMC9139460/ /pubmed/35624934 http://dx.doi.org/10.3390/brainsci12050547 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wei, Hui
Jin, Xiao
Su, Zihao
A Circuit Model for Working Memory Based on Hybrid Positive and Negative-Derivative Feedback Mechanism
title A Circuit Model for Working Memory Based on Hybrid Positive and Negative-Derivative Feedback Mechanism
title_full A Circuit Model for Working Memory Based on Hybrid Positive and Negative-Derivative Feedback Mechanism
title_fullStr A Circuit Model for Working Memory Based on Hybrid Positive and Negative-Derivative Feedback Mechanism
title_full_unstemmed A Circuit Model for Working Memory Based on Hybrid Positive and Negative-Derivative Feedback Mechanism
title_short A Circuit Model for Working Memory Based on Hybrid Positive and Negative-Derivative Feedback Mechanism
title_sort circuit model for working memory based on hybrid positive and negative-derivative feedback mechanism
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9139460/
https://www.ncbi.nlm.nih.gov/pubmed/35624934
http://dx.doi.org/10.3390/brainsci12050547
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