| 宇恵 勝紀 | D, 中間発表 | 数理情報学 (計算神経科学) | 池田 和司, 作村 諭一（BS）, 川鍋 一晃（客員教授）, 田中 沙織, 久保 孝富 |
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title: *** Investigation of a biophysical mathematical model to reproduce the MEG signal ***
abstract: *** There are many mathematical models for resting-state brain activity, and different researchers deal with different models. And there is little debate about which biophysical mathematical model is better for directly understanding the mechanisms that generate brain activity. This may prevent better understanding of brain dynamics. In this presentation, I will discuss the reproducibility of signals of brain activity by the Wilson-Cowan model as a first step for considering an appropriate model. Unlike in the previous doctoral program, I will target the reproduction of MEG signals that indicate early brain activity. Finally, I will show the research that I plan to conduct in the future. *** language of the presentation: *** Japanese *** 発表題目: *** MEG信号を再現する生物物理的な数理モデルの検討 *** 発表概要: *** 安静時脳活動を表す数理モデルは多数存在し、研究者ごとに扱うモデルが異なる。そして、脳活動の発生メカニズムの理解に直接的に役立つ生物物理的な数理モデルについては、どのモデルが良いかについての議論はされていない。このため、脳ダイナミクスへの理解が進まない恐れがある。今回の発表では、適切なモデルの検討の前段階として、Wilson-Cowanモデルによる脳活動の信号の再現性について検討する。博士前期課程とは異なり、早い脳の活動を示すMEG信号を再現目標とする。最後に、今後行う予定の研究内容を示す。 *** | |||
| 山口 晴久 | M, 1回目発表 | 数理情報学(計算神経科学 ) | 池田 和司, 作村 諭一（BS）, 川鍋 一晃（客員教授）, 田中 沙織 |
| 植原 真人 | M, 2回目発表 | 数理情報学(計算神経科学 ) | 池田 和司, 川鍋 一晃（客員教授）, 杉本 徳和（客員准教授）, 田中 沙織 |
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title: Development of an easy-to-use brain-computer interface using optically pumped magnetometer
abstract: Brain-computer interface(BCI), which decodes information directly from brain activity, has been researched. The mainstream of non-invasive brain activity measurement for BCI is Electroencephalogram(EEG) because of its ease of use. Recently, a new high-sensitivity magnetometer called an optically pumped magnetometer(OPM) was developed. OPM has the potential to measure high-quality magnetoencephalography(MEG) non-invasively, but it has never attempted to use for motor imagery BCI. This study aims to investigate the characteristics of OPM-MEG through motor imagery task experiments to help develop ease-of-use BCI. language of the presentation: Japanese | |||
| CHEN YEN-WEI | M, 2回目発表 | 数理情報学 | 池田 和司, 作村 諭一（BS）, 久保 孝富, 日永田 智絵 |
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title: Exploring High-Dimensional Data Embeddings with CEBRA for Enhanced Analysis and Bifurcation Detection
abstract: CEBRA is a library designed for estimating consistent embeddings of high-dimensional recordings, primarily in the fields of biology and neuroscience. It offers self-supervised learning algorithms implemented in PyTorch and supports various datasets commonly encountered in these fields. CEBRA provides an interface for easy integration with existing data analysis libraries and tools. The primary use of CEBRA is to extract latent factors from time-series data, although it can also be applied to non-time-series data. Its main advantages include generating robust and consistent embeddings, making it suitable for tasks such as measuring consistency across different conditions, hypothesis-guided decoding, and exploring the resulting embedding spaces. In this study, we aim to explain certain high-dimensional data using CEBRA and transform the information into a new representation through embedding, making it easier for analysis. As part of our further future research plans, we hope to conduct studies on bifurcation detection using this approach. language of the presentation: English | |||