脳の情報処理の原理とその応用を議論する。 We discuss the brain dynamics and its applications

脳は様々な領域同士が結びつき，巨大な情報ネットワークを構成している．近年，脳の情報ネットワークは，人の認知機能の生物学的な基盤の解明に大きく貢献している．本授業では，脳のネットワークを中心的なテーマとして，脳情報の解読（デコード・エンコード），ニューロフィードバック，脳刺激，神経細胞活動記録など，認知神経科学の関連分野の文献を輪読する．最近出版された英語論文（展望・原著）を購読し，その解析の原理を理解し，受講者自身が興味を持つテーマへの応用を考察してもらう．受講者には，割り当てられた発表だけでなく，積極的な質疑応答が求められる．

本ゼミは、駒場IIキャンパスにおいて行われている最先端の生命科学やバイオテクノロジーの研究開発の内容を、学部生の皆さんに伝え、実際の研究に触れ、関わっていくきっかけとすることを目指しています。駒場IIキャンパスにおいて行われている先端生命科学研究に関しては、システム生物医学ラボラトリーズ（Laboratories for Systems Biology and Medicine: LSBM）のホームページ（https://www.lsbm.org/*****）に掲載しています。 【先端研とは】 学部生のみなさんが主に学ぶ駒場Ⅰキャンパスから歩いてすぐの所、駒場Ⅱリサーチキャンパスにある先端科学技術研究センター（先端研）は、「学術の発展と社会の変化から生じる新たな課題へ機動的に挑戦し、人間と社会に向かう先端科学技術の新領域を開拓することによって、科学技術の発展に貢献することを目的とする」研究所です。 【LSBMとは】 LSBMは、次世代生命医科学を開拓すべく有機的に協働する、バーチャルな十以上の研究室集合体です。主に先端研のメンバーを中心として理学・工学・情報科学・医学の研究者が、積極的なコラボレーションを展開しています。 This seminar aims to inform undergraduate students about the cutting-edge life science and biotechnology research and development taking place at the Komaba II campus, and to provide them with an opportunity to experience and become involved in actual research. Information about the advanced life science research being conducted at the Komaba II campus is posted on the Laboratories for Systems Biology and Medicine (LSBM) website (https://www.lsbm.org)./***** The Research Center for Advanced Science and Technology (RCAST), located on the Komaba II Research Campus, a short walk from the Komaba I Campus where most undergraduate students study, is dedicated to "contributing to the advancement of science and technology by taking on new challenges arising from academic development and social change, and by exploring new areas of advanced science and technology for the benefit of humanity and society. LSBM is a virtual group of more than ten laboratories that collaborate organically to pioneer the next generation of biomedical science. LSBM is a virtual group of more than ten laboratories that collaborate organically to pioneer the next generation of biomedical science.

Statistics and biometrics have emerged as crucial disciplines not only in agricultural sciences but also in various other fields. This significance is primarily attributed to three factors: Firstly, advancements in data measurement techniques have facilitated the collection of extensive and diverse biological and agronomic data that were previously unattainable. Secondly, the evolution of data science methodologies has enabled the integration and modeling of such collected data. Thirdly, the enhancement of computational capabilities has empowered the utilization of these methodologies. These advancements have rendered statistical and biometric methods indispensable for extracting insights from the vast and varied biological and agronomic datasets. Throughout this lecture series, a diverse array of biological and agronomic datasets will serve as illustrative examples to demonstrate various analytical methods. Delivered in a hands-on format, utilizing R, Python, and Matlab, the aim is to equip students with practical analysis skills. The initial portion of the course, spanning the first one-third, will focus primarily on techniques for summarizing, visualizing, and modeling relationships within multivariate datasets. In the subsequent one-third, students will delve into linear models, linear mixed models, local regression, and nonlinear models. Finally, in the last segment, students will explore image analysis, machine learning, and deep learning methods. While the course will cover a broad spectrum of methods, ranging from introductory to advanced levels, the emphasis will be on developing the capability to independently conduct analyses rather than on elaborating on the theoretical underpinnings of the methods.

What is for a person to live a good life, that is, a life that it good for the person? It is usually said that such a good life consists in the happiness – or well-being – of the person. But what, then, is well-being? Philosophers have given various answers to this question, and raised challenges to the coherence or utility of the notion itself. This course will survey the major philosophical theories of well-being.

What is for a person to live a good life, that is, a life that it good for the person? It is usually said that such a good life consists in the happiness – or well-being – of the person. But what, then, is well-being? Philosophers have given various answers to this question, and raised challenges to the coherence or utility of the notion itself. This course will survey the major philosophical theories of well-being.

What is for a person to live a good life, that is, a life that it good for the person? It is usually said that such a good life consists in the happiness – or well-being – of the person. But what, then, is well-being? Philosophers have given various answers to this question, and raised challenges to the coherence or utility of the notion itself. This course will survey the major philosophical theories of well-being.

What is for a person to live a good life, that is, a life that it good for the person? It is usually said that such a good life consists in the happiness – or well-being – of the person. But what, then, is well-being? Philosophers have given various answers to this question, and raised challenges to the coherence or utility of the notion itself. This course will survey the major philosophical theories of well-being.

脳の理論的アプローチである計算論的神経科学に関して講義を行う。心理分野の学生向けにシステム神経科学や認知科学分野に重点を置く。感覚処理・意思決定・運動制御の計算論モデリングと、認知課題中の脳信号解析法に関して講義する。心理物理の行動データや脳活動信号データを計算論を通して理解できるようになることを目標とする。

人間の発達は、胚の形態形成にはじまり、胎児や新生児に与えられた遺伝的要因と生得的な能力、自らを変化・発展させる内在的な機構、物理的環境や他者との相互作用により適応・学習していく機構などが複雑にからみあう現象である。本演習では、講義及び受講者による演習、原著論文講読、レポート作成、プレゼンテーションを行い、発達脳科学に関わる基本的概念を学ぶ。