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Pycabnn: Efficient and Extensible Software to Construct an Anatomical Basis for a Physiologically Realistic Neural Network Model
https://oist.repo.nii.ac.jp/records/1667
https://oist.repo.nii.ac.jp/records/166790752345-843f-4ad2-95f6-23a5ff1df7c7
名前 / ファイル | ライセンス | アクション |
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Wichert-2020-Pycabnn_ Efficient and Extensible (2.3 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 | 2020-08-06 | |||||
タイトル | ||||||
言語 | en | |||||
タイトル | Pycabnn: Efficient and Extensible Software to Construct an Anatomical Basis for a Physiologically Realistic Neural Network Model | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | neural network model | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | anatomical basis | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | cell position | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | network connectivity | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | cerebellum | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | cerebellar granule cell | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | Python | |||||
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) |
Wichert, Ines
× Wichert, Ines× Jee, Sanghun× De Schutter, Erik× Hong, Sungho |
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書誌情報 |
en : Frontiers in Neuroinformatics 巻 14, p. 31, 発行日 2020-07-07 |
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抄録 | ||||||
内容記述タイプ | Other | |||||
内容記述 | Physiologically detailed models of neural networks are an important tool for studying how biophysical mechanisms impact neural information processing. An important, fundamental step in constructing such a model is determining where neurons are placed and how they connect to each other, based on known anatomical properties and constraints given by experimental data. Here we present an open-source software tool, pycabnn, that is dedicated to generating an anatomical model, which serves as the basis of a full network model. In pycabnn, we implemented efficient algorithms for generating physiologically realistic cell positions and for determining connectivity based on extended geometrical structures such as axonal and dendritic morphology. We demonstrate the capabilities and performance of pycabnn by using an example, a network model of the cerebellar granular layer, which requires generating more than half a million cells and computing their mutual connectivity. We show that pycabnn is efficient enough to carry out all the required tasks on a laptop computer within reasonable runtime, although it can also run in a parallel computing environment. Written purely in Python with limited external dependencies, pycabnn is easy to use and extend, and it can be a useful tool for computational neural network studies in the future. | |||||
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/32733226 | |||||
DOI | ||||||
関連タイプ | isIdenticalTo | |||||
識別子タイプ | DOI | |||||
関連識別子 | info:doi/10.3389/fninf.2020.00031 | |||||
権利 | ||||||
権利情報 | © 2020 Wichert, Jee, DeSchutter and Hong. | |||||
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.2020.00031/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 |