2023年6月30日　　Friday Lunch Seminar （英語で開催します）
12:15 〜 13:00
CiNet棟大会議室とOn-lineで開催いたします。

演題：Machine learning methods for brain decoding analysis

量子科学技術研究開発機構
量子生命科学研究所
研究員
間島 慶

担当PI :  西本 伸志

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.

[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.