{"id":3481,"date":"2023-06-07T15:39:25","date_gmt":"2023-06-07T06:39:25","guid":{"rendered":"http:\/\/cinetjp-static3.nict.go.jp\/japanese\/?post_type=event&#038;p=3481"},"modified":"2023-06-19T09:19:30","modified_gmt":"2023-06-19T00:19:30","slug":"20230630_2769","status":"publish","type":"event","link":"http:\/\/cinetjp-static3.nict.go.jp\/japanese\/event\/20230630_2769\/","title":{"rendered":"\uff1cCiNet \u30e1\u30f3\u30d0\u30fc\u3092\u5bfe\u8c61\u306b\u30cf\u30a4\u30d6\u30ea\u30c3\u30c9\u958b\u50ac\uff1e Friday Lunch Seminar \u9593\u5cf6 \u6176 \uff1a\u201c Machine learning methods for brain decoding analysis \u201d"},"content":{"rendered":"\n<p>2023\u5e746\u670830\u65e5\u3000\u3000Friday Lunch Seminar&nbsp;\uff08\u82f1\u8a9e\u3067\u958b\u50ac\u3057\u307e\u3059\uff09<br>12:15&nbsp;\u301c&nbsp;13:00<br>CiNet\u68df\u5927\u4f1a\u8b70\u5ba4\u3068On-line\u3067\u958b\u50ac\u3044\u305f\u3057\u307e\u3059\u3002<\/p>\n\n\n\n<p>\u6f14\u984c\uff1aMachine learning methods for brain decoding analysis<\/p>\n\n\n\n<p>\u91cf\u5b50\u79d1\u5b66\u6280\u8853\u7814\u7a76\u958b\u767a\u6a5f\u69cb <br>\u91cf\u5b50\u751f\u547d\u79d1\u5b66\u7814\u7a76\u6240<br>\u7814\u7a76\u54e1<br>\u9593\u5cf6 \u6176<\/p>\n\n\n\n<p>\u62c5\u5f53PI : &nbsp;<a href=\"https:\/\/cinet.jp\/japanese\/people\/20141086\/\">\u897f\u672c \u4f38\u5fd7<\/a><\/p>\n\n\n\n<p>Abstract:<br>In this talk, I will introduce several machine learning methods we recently developed for decoding analysis: 1) a method for visualizing subjective images in the human mind based on brain activity [1], 2) a supervised algorithm designed for predicting discrete ordinal variables [2], and 3) a fast algorithm inspired by quantum computation, which approximates PCA and CCA and would allow for the analysis of huge-dimensional neural data [3]. Following these presentations, I would like to have discussions with CiNet members on possible collaborations.<\/p>\n\n\n\n<p>[1] Koide-Majima, Nishimoto, Majima. Mental image reconstruction from human brain activity. bioRxiv preprint. 2023.<br>[2] Satake, Majima, Aoki, Kamitani. Sparse ordinal logistic regression and its application to brain decoding. Frontiers in Neuroinformatics. 2018.<br>[3] Koide-Majima, Majima. Quantum-inspired canonical correlation analysis for exponentially large dimensional data. Neural Networks. 2021.<\/p>\n\n\n\n<p><br><\/p>\n\n\n\n<p><\/p>\n","protected":false},"featured_media":0,"template":"","acf":[],"_links":{"self":[{"href":"http:\/\/cinetjp-static3.nict.go.jp\/japanese\/wp-json\/wp\/v2\/event\/3481"}],"collection":[{"href":"http:\/\/cinetjp-static3.nict.go.jp\/japanese\/wp-json\/wp\/v2\/event"}],"about":[{"href":"http:\/\/cinetjp-static3.nict.go.jp\/japanese\/wp-json\/wp\/v2\/types\/event"}],"wp:attachment":[{"href":"http:\/\/cinetjp-static3.nict.go.jp\/japanese\/wp-json\/wp\/v2\/media?parent=3481"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}