The brain is composed of two intertwined aspects: the physical and the information processing. For the physical aspect, large-scale databases of the brain are being constructed by combining the latest measurement and information technologies, such as those used in the Allen Brain Atlas and the Human Connectome Project. In terms of information processing of brain, artificial intelligence inspired by the brain's structure and algorithms has made significant breakthroughs and produced products for general use, like ChatGPT. The concept of the "digital brain" focuses on integrating both of these aspects, databasing the brain and its information processing, to bring forth a new era of neuroscience. Seminars are being organized to further develop this concept.
Organizer: Ken Nakae (ExCELLS), Daisuke Tagami (Kyushu University), Kenji Doya (OIST)
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Speaker：Kei Hirose (Kyushu University)
Place: Zoom (Please find the zoom link by the registration)
Date：2025/2/17 10:30〜 (JST)
Title：High-dimensional interpretable factor analysis via penalization
Abstract: Factor analysis is a statistical method for identifying latent factors from the correlation structures of high-dimensional data. It was originally developed for applications in social and behavioral sciences but has since been applied to various research fields, including the natural sciences. An advantage of factor analysis is that it leads to interpretable latent factors, enabling applications such as the identification of active brain regions in neuroscience. In this study, we propose a penalized maximum likelihood estimation method aimed at enhancing the interpretability of latent factors. In particular, the Prenet (Product-based elastic net) penalization allows for the estimation of a perfect simple structure, a desirable characteristic in the factor analysis literature. The usefulness of the proposed method is investigated through real data analyses. Finally, we discuss potential extensions and applications of the proposed method in neuroscience.
Speaker: 田上 大助(九州大学)
Place: Zoom (Please find the zoom link by the registration)