{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00218673","sets":["1164:5352:10882:10963"]},"path":["10963"],"owner":"44499","recid":"218673","title":["モジュール型レザバーコンピューティングにおける大記憶容量を有する動的直交基底の創発"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-06-20"},"_buckets":{"deposit":"2b014369-a1eb-4c9b-93c5-7a7c5c61d13d"},"_deposit":{"id":"218673","pid":{"type":"depid","value":"218673","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"モジュール型レザバーコンピューティングにおける大記憶容量を有する動的直交基底の創発","author_link":["569248","569247","569242","569246","569244","569245","569241","569243"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"モジュール型レザバーコンピューティングにおける大記憶容量を有する動的直交基底の創発"},{"subitem_title":"Emergence of Dynamical Orthogonal Basis Acquiring Large Memory Capacity in Modular Reservoir Computing","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2022-06-20","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"大阪大学先導的学際研究機構附属共生知能システム研究センター"},{"subitem_text_value":"大阪大学先導的学際研究機構附属共生知能システム研究センター／情報通信研究機構脳情報通信融合研究センター"},{"subitem_text_value":"中部大学創発学術院／AI 数理データサイエンスセンター"},{"subitem_text_value":"大阪大学先導的学際研究機構附属共生知能システム研究センター／情報通信研究機構脳情報通信融合研究センター／中部大学創発学術院／AI 数理データサイエンスセンター／大阪国際工科専門職大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Symbiotic Intelligent Systems Research Center, Open and Transdisciplinary Research Initiatives, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Symbiotic Intelligent Systems Research Center, Open and Transdisciplinary Research Initiatives, Osaka University/Center for Information and Neural Networks, National Institute of Information and Communications Technology","subitem_text_language":"en"},{"subitem_text_value":"Chubu University Academy of Emerging Sciences /Center for Mathematical Science and Artificial Intelligence","subitem_text_language":"en"},{"subitem_text_value":"Symbiotic Intelligent Systems Research Center, Open and Transdisciplinary Research Initiatives, Osaka University/Center for Information and Neural Networks, National Institute of Information and Communications Technology/Chubu University Academy of Emerging Sciences /Center for Mathematical Science and Artificial Intelligence/ International Professional University of Technology in Osaka","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/218673/files/IPSJ-BIO22070043.pdf","label":"IPSJ-BIO22070043.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-BIO22070043.pdf","filesize":[{"value":"2.1 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"41"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"6ca920db-3be8-4b19-9822-6c41985ff9de","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"河合, 祐司"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"朴, 志勲"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"津田, 一郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"浅田, 稔"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yuji, Kawai","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Jihoon, Park","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ichiro, Tsuda","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Minoru, Asada","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12055912","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8590","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"特定のタイミングで複雑な時空間パターンを生成する脳の能力は，運動学習や時系列予測に重要である．そのような機能を実現するためにランダム結合のリカレントニューラルネットワーク（レザバー）の自励的な神経活動を用いるアプローチでは，軌道不安定性が課題になる．本稿では，ネットワークサイズの小さなレザバーが軌道安定であることを利用して，それらをモジュールとして多数並列に結合し，それらの出力の線形和（リードアウト）の学習により任意の時系列を生成するシステムを提案する．実験により，モジュール出力軌道は互いに直交し，すなわち直交基底をなし，数十秒のインターバルのタイミング学習やローレンツ系の学習が可能であることを示す．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"The brain’s ability to generate complex spatiotemporal patterns with a specific timing is essential for motor learning and time series prediction. An approach that realizes this function using the self-sustained neural activity of a randomly\nconnected recurrent neural network (reservoir) has the problem of orbital instability. We propose a novel system that learns an arbitrary time series as the linear sum (readout) of stable trajectories from a large number of small network modules. Our experimental results show that the trajectories of the module outputs are orthogonal to each other, i.e., an orthogonal basis emerges, and the system could learn the timing of intervals of tens of seconds and the Lorenz system.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告バイオ情報学（BIO）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-06-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"43","bibliographicVolumeNumber":"2022-BIO-70"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":218673,"updated":"2025-01-19T15:05:10.870687+00:00","links":{},"created":"2025-01-19T01:19:01.647064+00:00"}