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A simplified memory network model based on pattern formations

Many experiments have evidenced the transition with different time scales from short-term memory (STM) to long-term memory (LTM) in mammalian brains, while its theoretical understanding is still under debate. To understand its underlying mechanism, it has recently been shown that it is possible to h...

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Detalles Bibliográficos
Autores principales: Xu, Kesheng, Zhang, Xiyun, Wang, Chaoqing, Liu, Zonghua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4271251/
https://www.ncbi.nlm.nih.gov/pubmed/25524172
http://dx.doi.org/10.1038/srep07568
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author Xu, Kesheng
Zhang, Xiyun
Wang, Chaoqing
Liu, Zonghua
author_facet Xu, Kesheng
Zhang, Xiyun
Wang, Chaoqing
Liu, Zonghua
author_sort Xu, Kesheng
collection PubMed
description Many experiments have evidenced the transition with different time scales from short-term memory (STM) to long-term memory (LTM) in mammalian brains, while its theoretical understanding is still under debate. To understand its underlying mechanism, it has recently been shown that it is possible to have a long-period rhythmic synchronous firing in a scale-free network, provided the existence of both the high-degree hubs and the loops formed by low-degree nodes. We here present a simplified memory network model to show that the self-sustained synchronous firing can be observed even without these two necessary conditions. This simplified network consists of two loops of coupled excitable neurons with different synaptic conductance and with one node being the sensory neuron to receive an external stimulus signal. This model can be further used to show how the diversity of firing patterns can be selectively formed by varying the signal frequency, duration of the stimulus and network topology, which corresponds to the patterns of STM and LTM with different time scales. A theoretical analysis is presented to explain the underlying mechanism of firing patterns.
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spelling pubmed-42712512014-12-30 A simplified memory network model based on pattern formations Xu, Kesheng Zhang, Xiyun Wang, Chaoqing Liu, Zonghua Sci Rep Article Many experiments have evidenced the transition with different time scales from short-term memory (STM) to long-term memory (LTM) in mammalian brains, while its theoretical understanding is still under debate. To understand its underlying mechanism, it has recently been shown that it is possible to have a long-period rhythmic synchronous firing in a scale-free network, provided the existence of both the high-degree hubs and the loops formed by low-degree nodes. We here present a simplified memory network model to show that the self-sustained synchronous firing can be observed even without these two necessary conditions. This simplified network consists of two loops of coupled excitable neurons with different synaptic conductance and with one node being the sensory neuron to receive an external stimulus signal. This model can be further used to show how the diversity of firing patterns can be selectively formed by varying the signal frequency, duration of the stimulus and network topology, which corresponds to the patterns of STM and LTM with different time scales. A theoretical analysis is presented to explain the underlying mechanism of firing patterns. Nature Publishing Group 2014-12-19 /pmc/articles/PMC4271251/ /pubmed/25524172 http://dx.doi.org/10.1038/srep07568 Text en Copyright © 2014, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder in order to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Xu, Kesheng
Zhang, Xiyun
Wang, Chaoqing
Liu, Zonghua
A simplified memory network model based on pattern formations
title A simplified memory network model based on pattern formations
title_full A simplified memory network model based on pattern formations
title_fullStr A simplified memory network model based on pattern formations
title_full_unstemmed A simplified memory network model based on pattern formations
title_short A simplified memory network model based on pattern formations
title_sort simplified memory network model based on pattern formations
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4271251/
https://www.ncbi.nlm.nih.gov/pubmed/25524172
http://dx.doi.org/10.1038/srep07568
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