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Multiscale representations of community structures in attractor neural networks
https://oist.repo.nii.ac.jp/records/2224
https://oist.repo.nii.ac.jp/records/22246f5eaa01-2455-41c5-a1e5-a43d22c8ba14
名前 / ファイル | ライセンス | アクション |
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Haga-2021-Multiscale-representations-of-commu (4.0 MB)
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CC BY 4.0
Creative Commons Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/) |
Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2021-09-06 | |||||
タイトル | ||||||
言語 | en | |||||
タイトル | Multiscale representations of community structures in attractor neural networks | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | FINE INFORMATION | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | NEURONS | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | MEMORY | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | DYNAMICS | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | CIRCUIT | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | STORAGE | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | MODEL | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
資源タイプ | journal article | |||||
著者(英) |
Haga, Tatsuya
× Haga, Tatsuya× Fukai, Tomoki |
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書誌情報 |
en : PLOS Computational Biology 巻 17, 号 8, p. e1009296, 発行日 2021-08-23 |
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抄録 | ||||||
内容記述タイプ | Other | |||||
内容記述 | Our cognition relies on the ability of the brain to segment hierarchically structured events on multiple scales. Recent evidence suggests that the brain performs this event segmentation based on the structure of state-transition graphs behind sequential experiences. However, the underlying circuit mechanisms are poorly understood. In this paper we propose an extended attractor network model for graph-based hierarchical computation which we call the Laplacian associative memory. This model generates multiscale representations for communities (clusters) of associative links between memory items, and the scale is regulated by the heterogenous modulation of inhibitory circuits. We analytically and numerically show that these representations correspond to graph Laplacian eigenvectors, a popular method for graph segmentation and dimensionality reduction. Finally, we demonstrate that our model exhibits chunked sequential activity patterns resembling hippocampal theta sequences. Our model connects graph theory and attractor dynamics to provide a biologically plausible mechanism for abstraction in the brain. | |||||
出版者 | ||||||
出版者 | Public Library of Science | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 1553-7358 | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 1553-734X | |||||
PubMed番号 | ||||||
関連タイプ | isIdenticalTo | |||||
識別子タイプ | PMID | |||||
関連識別子 | info:pmid/34424901 | |||||
DOI | ||||||
関連タイプ | isIdenticalTo | |||||
識別子タイプ | DOI | |||||
関連識別子 | info:doi/10.1371/journal.pcbi.1009296 | |||||
権利 | ||||||
権利情報 | © 2021 Haga, Fukai. | |||||
関連サイト | ||||||
識別子タイプ | URI | |||||
関連識別子 | https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009296 | |||||
著者版フラグ | ||||||
出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 |