{"created":"2025-01-18T23:36:03.856062+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00082113","sets":["1164:2735:6701:6774"]},"path":["6774"],"owner":"11","recid":"82113","title":["Estimating Distribution of Dendritic Membrane Resistance Using Markov Random Field"],"pubdate":{"attribute_name":"公開日","attribute_value":"2012-05-10"},"_buckets":{"deposit":"4d0f8db5-369b-4de7-af32-a08304fa87f1"},"_deposit":{"id":"82113","pid":{"type":"depid","value":"82113","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"Estimating Distribution of Dendritic Membrane Resistance Using Markov Random Field","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Estimating Distribution of Dendritic Membrane Resistance Using Markov Random Field"},{"subitem_title":"Estimating Distribution of Dendritic Membrane Resistance Using Markov Random Field","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2012-05-10","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Frontier Sciences, The University of Tokyo"},{"subitem_text_value":"Graduate School of Engineering, Kobe University"},{"subitem_text_value":"Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology"},{"subitem_text_value":"Graduate School of Frontier Sciences, The University of Tokyo／RIKEN Brain Science Institute"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Frontier Sciences, The University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering, Kobe University","subitem_text_language":"en"},{"subitem_text_value":"Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Frontier Sciences, The University of Tokyo / RIKEN Brain Science Institute","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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/82113/files/IPSJ-MPS12088010.pdf"},"date":[{"dateType":"Available","dateValue":"2014-05-10"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MPS12088010.pdf","filesize":[{"value":"423.6 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"7dff7c34-a30b-41c3-ad4b-5f8cc6df3a5c","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2012 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Jun, Kitazono"},{"creatorName":"Toshiaki, Omori"},{"creatorName":"Toru, Aonishi"},{"creatorName":"Masato, Okada"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Jun, Kitazono","creatorNameLang":"en"},{"creatorName":"Toshiaki, Omori","creatorNameLang":"en"},{"creatorName":"Toru, Aonishi","creatorNameLang":"en"},{"creatorName":"Masato, Okada","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10505667","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_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"With developments in optical imaging over the past decade, statistical methods for estimating dendritic membrane resistance from observed noisy signals have been proposed. In most of previous studies, membrane resistance over a dendritic tree was assumed to be constant, or membrane resistance at a point rather than that distributed over a dendrite was investigated. Membrane resistance, however, is actually non-uniformly distributed. Although in a previous study a method was proposed in which a specific non-homogeneous distribution form was assumed, it is applicable only when the appropriate distribution form is known. We propose a statistical method, that does not assume a particular distribution form of membrane resistance, for estimating membrane resistance distribution from observed membrane potentials. We use the Markov random field (MRF) as a prior of the membrane-resistance distribution. In the MRF, any specific distribution form of membrane resistance is not assumed, but only spatial smoothness of membrane resistance is assumed. We apply our method to synthetic data to evaluate its efficacy, and show that even when we do not know the appropriate distribution form, our method can accurately estimate the membrane-resistance distribution.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"With developments in optical imaging over the past decade, statistical methods for estimating dendritic membrane resistance from observed noisy signals have been proposed. In most of previous studies, membrane resistance over a dendritic tree was assumed to be constant, or membrane resistance at a point rather than that distributed over a dendrite was investigated. Membrane resistance, however, is actually non-uniformly distributed. Although in a previous study a method was proposed in which a specific non-homogeneous distribution form was assumed, it is applicable only when the appropriate distribution form is known. We propose a statistical method, that does not assume a particular distribution form of membrane resistance, for estimating membrane resistance distribution from observed membrane potentials. We use the Markov random field (MRF) as a prior of the membrane-resistance distribution. In the MRF, any specific distribution form of membrane resistance is not assumed, but only spatial smoothness of membrane resistance is assumed. We apply our method to synthetic data to evaluate its efficacy, and show that even when we do not know the appropriate distribution form, our method can accurately estimate the membrane-resistance distribution.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告数理モデル化と問題解決（MPS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2012-05-10","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"10","bibliographicVolumeNumber":"2012-MPS-88"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"id":82113,"updated":"2025-01-21T19:08:43.547125+00:00","links":{}}