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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2014
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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. |
format | Online Article Text |
id | pubmed-4271251 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
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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