{"created":"2023-06-26T11:00:15.803591+00:00","id":688,"links":{},"metadata":{"_buckets":{"deposit":"10b62403-8ae3-460c-8412-001890608ca5"},"_deposit":{"created_by":26,"id":"688","owners":[26],"pid":{"revision_id":0,"type":"depid","value":"688"},"status":"published"},"_oai":{"id":"oai:oist.repo.nii.ac.jp:00000688","sets":["6:26"]},"author_link":["3522","3523","3521"],"item_10001_biblio_info_7":{"attribute_name":"Bibliographic Information","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2018-03-10","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"137","bibliographicPageStart":"120","bibliographicVolumeNumber":"102","bibliographic_titles":[{},{"bibliographic_title":"Neural Networks","bibliographic_titleLang":"en"}]}]},"item_10001_creator_3":{"attribute_name":"Author","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Magrans de Abril, Ildefons"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yoshimoto, Junichiro"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Doya, Kenji"}],"nameIdentifiers":[{}]}]},"item_10001_description_5":{"attribute_name":"Abstract","attribute_value_mlt":[{"subitem_description":"This article presents a review of computational methods for connectivity inference from neural activity data derived from multi-electrode recordings or fluorescence imaging. We first identify biophysical and technical challenges in connectivity inference along the data processing pipeline. We then review connectivity inference methods based on two major mathematical foundations, namely, descriptive model-free approaches and generative model-based approaches. We investigate representative studies in both categories and clarify which challenges have been addressed by which method. We further identify critical open issues and possible research directions.","subitem_description_type":"Other"}]},"item_10001_publisher_8":{"attribute_name":"Publisher","attribute_value_mlt":[{"subitem_publisher":"Elsevier Ltd. "}]},"item_10001_relation_13":{"attribute_name":"PubMedNo.","attribute_value_mlt":[{"subitem_relation_type":"isIdenticalTo","subitem_relation_type_id":{"subitem_relation_type_id_text":"info:pmid/29571122","subitem_relation_type_select":"PMID"}}]},"item_10001_relation_14":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type":"isIdenticalTo","subitem_relation_type_id":{"subitem_relation_type_id_text":"info:doi/10.1016/j.neunet.2018.02.016","subitem_relation_type_select":"DOI"}}]},"item_10001_relation_17":{"attribute_name":"Related site","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"https://www.sciencedirect.com/science/article/pii/S0893608018300704?via%3Dihub","subitem_relation_type_select":"URI"}}]},"item_10001_rights_15":{"attribute_name":"Rights","attribute_value_mlt":[{"subitem_rights":"© 2018 The Author(s). "}]},"item_10001_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"0893-6080","subitem_source_identifier_type":"ISSN"}]},"item_10001_version_type_20":{"attribute_name":"Author's flag","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2018-08-23"}],"displaytype":"detail","filename":"1-s2.0-S0893608018300704-main.pdf","filesize":[{"value":"722.6 kB"}],"format":"application/pdf","license_note":"Creative Commons Attribution 4.0 International\n(http://creativecommons.org/licenses/by/4.0/) ","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"1-s2.0-S0893608018300704-main","url":"https://oist.repo.nii.ac.jp/record/688/files/1-s2.0-S0893608018300704-main.pdf"},"version_id":"d1d3dd88-2063-4308-8c0a-f9b2333ac167"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"Connectivity inference from neural recording data: Challenges, mathematical bases and research directions","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Connectivity inference from neural recording data: Challenges, mathematical bases and research directions","subitem_title_language":"en"}]},"item_type_id":"10001","owner":"26","path":["26"],"pubdate":{"attribute_name":"公開日","attribute_value":"2018-08-23"},"publish_date":"2018-08-23","publish_status":"0","recid":"688","relation_version_is_last":true,"title":["Connectivity inference from neural recording data: Challenges, mathematical bases and research directions"],"weko_creator_id":"26","weko_shared_id":26},"updated":"2023-06-26T12:05:05.197668+00:00"}