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Neural circuit mechanisms of hierarchical sequence learning tested on large-scale recording data
https://oist.repo.nii.ac.jp/records/2727
https://oist.repo.nii.ac.jp/records/27276363e792-08e2-40fa-aa22-fd58777f926d
名前 / ファイル | ライセンス | アクション |
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Asabuki-2022-Neural circuit mechanisms of hier-vor (5.6 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 | 2022-08-01 | |||||
タイトル | ||||||
言語 | en | |||||
タイトル | Neural circuit mechanisms of hierarchical sequence learning tested on large-scale recording data | |||||
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) |
Asabuki, Toshitake
× Asabuki, Toshitake× Kokate, Prajakta× Fukai, Tomoki |
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書誌情報 |
en : PLOS Computational Biology 巻 18, 号 6, p. e1010214, 発行日 2022-06-21 |
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抄録 | ||||||
内容記述タイプ | Other | |||||
内容記述 | The brain performs various cognitive functions by learning the spatiotemporal salient features of the environment. This learning requires unsupervised segmentation of hierarchically organized spike sequences, but the underlying neural mechanism is only poorly understood. Here, we show that a recurrent gated network of neurons with dendrites can efficiently solve difficult segmentation tasks. In this model, multiplicative recurrent connections learn a context-dependent gating of dendro-somatic information transfers to minimize error in the prediction of somatic responses by the dendrites. Consequently, these connections filter the redundant input features represented by the dendrites but unnecessary in the given context. The model was tested on both synthetic and real neural data. In particular, the model was successful for segmenting multiple cell assemblies repeating in large-scale calcium imaging data containing thousands of cortical neurons. Our results suggest that recurrent gating of dendro-somatic signal transfers is crucial for cortical learning of context-dependent segmentation tasks. | |||||
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/35727828 | |||||
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.1010214 | |||||
権利 | ||||||
権利情報 | © 2022 Asabuki et al. | |||||
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.1010214 | |||||
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 |