{"id":2979,"date":"2023-06-07T15:45:23","date_gmt":"2023-06-07T06:45:23","guid":{"rendered":"http:\/\/cinetjp-static3.nict.go.jp\/english\/?post_type=event&p=2979"},"modified":"2023-06-19T09:20:40","modified_gmt":"2023-06-19T00:20:40","slug":"20230630_2769","status":"publish","type":"event","link":"http:\/\/cinetjp-static3.nict.go.jp\/english\/event\/20230630_2769\/","title":{"rendered":"Friday Lunch Seminar: Kei Majima: ” Machine learning methods for brain decoding analysis ” (On-line & In-person for CiNet members only: Sign-up required)"},"content":{"rendered":"\n

Friday Lunch Seminar (English)
June 30, 2023\u3000\u3000
12:15 \u301c 13:00
Conference Room, CiNet bldg. \/ On-line<\/p>\n\n\n\n

Talk Title\uff1aMachine learning methods for brain decoding analysis<\/p>\n\n\n\n

Kei Majima
Researcher
Institute for Quantum Life Science
National Institutes for Quantum Science and Technology<\/p>\n\n\n\n

Host PI :  Shinji Nishimoto<\/a><\/p>\n\n\n\n

Abstract:
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

[1] Koide-Majima, Nishimoto, Majima. Mental image reconstruction from human brain activity. bioRxiv preprint. 2023.
[2] Satake, Majima, Aoki, Kamitani. Sparse ordinal logistic regression and its application to brain decoding. Frontiers in Neuroinformatics. 2018.
[3] Koide-Majima, Majima. Quantum-inspired canonical correlation analysis for exponentially large dimensional data. Neural Networks. 2021.<\/p>\n\n\n\n

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