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Unsupervised Detection of Cell-Assembly Sequences by Similarity-Based Clustering
https://oist.repo.nii.ac.jp/records/1079
https://oist.repo.nii.ac.jp/records/10798170bb3f-98a0-4660-b3b0-eb0b71252d69
名前 / ファイル | ライセンス | アクション |
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Watanabe-2019-Unsupervised Detection of Cell-A (7.1 MB)
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Creative Commons Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/)
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2019-08-30 | |||||
タイトル | ||||||
言語 | en | |||||
タイトル | Unsupervised Detection of Cell-Assembly Sequences by Similarity-Based Clustering | |||||
言語 | ||||||
言語 | eng | |||||
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) |
Watanabe, Keita
× Watanabe, Keita× Haga, Tatsuya× Tatsuno, Masami× Euston, David R.× Fukai, Tomoki |
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書誌情報 |
en : Frontiers in Neuroinformatics 巻 13, p. 39, 発行日 2019-05-31 |
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抄録 | ||||||
内容記述タイプ | Other | |||||
内容記述 | Neurons which fire in a fixed temporal pattern (i.e., "cell assemblies") are hypothesized to be a fundamental unit of neural information processing. Several methods are available for the detection of cell assemblies without a time structure. However, the systematic detection of cell assemblies with time structure has been challenging, especially in large datasets, due to the lack of efficient methods for handling the time structure. Here, we show a method to detect a variety of cell-assembly activity patterns, recurring in noisy neural population activities at multiple timescales. The key innovation is the use of a computer science method to comparing strings ("edit similarity"), to group spikes into assemblies. We validated the method using artificial data and experimental data, which were previously recorded from the hippocampus of male Long-Evans rats and the prefrontal cortex of male Brown Norway/Fisher hybrid rats. From the hippocampus, we could simultaneously extract place-cell sequences occurring on different timescales during navigation and awake replay. From the prefrontal cortex, we could discover multiple spike sequences of neurons encoding different segments of a goal-directed task. Unlike conventional event-driven statistical approaches, our method detects cell assemblies without creating event-locked averages. Thus, the method offers a novel analytical tool for deciphering the neural code during arbitrary behavioral and mental processes. | |||||
出版者 | ||||||
出版者 | Frontiers Media | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 1662-5196 | |||||
PubMed番号 | ||||||
関連タイプ | isIdenticalTo | |||||
識別子タイプ | PMID | |||||
関連識別子 | info:pmid/31214005 | |||||
DOI | ||||||
関連タイプ | isIdenticalTo | |||||
識別子タイプ | DOI | |||||
関連識別子 | info:doi/10.3389/fninf.2019.00039 | |||||
権利 | ||||||
権利情報 | © 2019 Watanabe, Haga, Tatsuno, Euston and Fukai. | |||||
情報源 | ||||||
関連名称 | https://creativecommons.org/licenses/by/4.0/ | |||||
関連サイト | ||||||
識別子タイプ | URI | |||||
関連識別子 | https://www.frontiersin.org/articles/10.3389/fninf.2019.00039/full | |||||
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 |