(日本語) 第86回 セミナー

Sorry, this entry is only available in Japanese.

85th Seminar

Date and Time: January 28 (Tue.), 2020, 13:00 – 14:30
Place: Multimedia Hall #306, Building E-3(Map No.27), UEC
Speaker: Yoich Miyawaki (Professor, Faculty of Informatics and Engineering, The University of Electro-Communications)
Chair: Assoc. Prof. Shunji SATO
Title: Ultra-fast acquisition of ultra-high field MRI signals: go and study abroad!
Abstract:  ヒト脳活動を高時空間分解能で計測し、解析する技術の開発は、ヒト脳における情報処理原理の理解において極めて重要です。ヒト脳活動を最も高精細に計測することができる方法としてfMRIが知られていますが、計測対象が血流であるため、高速な神経活動をとるうえで時間分解能が不足していると考えられています。こうしたこれまでの常識を打ち破るべく、私が現在挑戦している、超高磁場fMRI信号の超高速計測という新しい研究手法と最新の結果について紹介します。この研究を行うにあたって、アメリカ国立衛生研究所(National Institutes of Health)に1年余り留学する機会を得ました。この研究留学は、私の価値観を変革し、研究者としてのあり方を再考するうえで極めて大きな影響を与えるものでした。


84th Seminar

Date and Time: January 10 (Fri.), 2020, 13:00 – 14:30
Place: Multimedia Hall #306, Building E-3(Map No.27), UEC
Speaker: Vasileios Tserolas (Researcher, Center of Neuroscience and Biomedical Engineering, The University of Electro-Communications)
Chair: Specially Appointed Prof. Shigeru TANAKA
Title: Spiking neural networks as a paradigm of artificial intelligence
Abstract: There is a long-lasting dream in creating artificial intelligence (AI). Today’s artificial intelligence is not yet there. The approach of today is to implement algorithms based on insights of human engineering. Hence, much effort is being invested into engineering new learning algorithms and information processing systems. The hope is that a right set of algorithms will be eventually created – making up a machine that will be able to learn on its own to the extent of becoming an AI. New general algorithms, once that could bring us closer to AI, do not seem to come out easily from such efforts. Some of the best general algorithms used today (e.g., deep learning) stem largely from 1980’s. The current AI technologies can do very well some things that require effort for a human (e.g., calculating prime numbers, searching databases) but have difficulties doing things that are for humans easy (e.g., perceiving, walking, navigating through space). So, what can we do? Is there any alternative or are we simply stuck with specialized AI?
For further developments of AI, we are in a need of using principles of biology to a higher degree than what we have been able to do so far. The new developments of AI must be based on a new theoretical approach on how biology and brain work resulting in a radically new view on what the nature of mental and cognitive operation is. Brains have evolved to control bodies in a very sophisticated way. Hence their abilities to quickly perceive the environment, rapidly detect statistical anomalies, control multiple degrees of freedom in real time, accumulate knowledge from many modalities, self-wire (learn) to optimize future behavior. When an animal navigates in the world, neurons can flexibly represent the position of the animal in a given environment, the composition of the environment, the head direction, the running speed, etc. These mental representations of the world are flexible and dynamic, determined and modulated by a range of environmental, behavioral, and neural parameters. In a similar way, to create biological-like AI it is necessary to mimic biology in respect to the levels of organization at which a biological agent adapts to its environment.
A good model problem for that is the control of an autonomous robot. Spiking neural networks (SNN) provide enormously rich expressing power but are not easy to control and very likely a pile of research will need to go into that field before first spiking chips will show amazing capabilities. Nevertheless, I believe that this is the right direction to go, in the long run.

83rd Seminar

Date and Time:  December 5 (Thu.), 2019, 13:00 – 14:30
Place: Multimedia Hall #306, Building E-3(Map No.27), UEC
Speaker: Nguyen Vu Trung (The Vice Director, National Hospital for Tropical Diseases, Vietnam)
Chair: Assist. Prof. Guaghao SUN
Title: Biomedical Engineering in Vietnam: Application and Future Collaboration
Abstract: Biomedical engineers work at the intersection of engineering, the life sciences and healthcare. The BME take principles from applied science (including mechanical, electrical, chemical and computer engineering) and physical sciences (including physics, chemistry and mathematics) and apply them to biology and medicine. Although the human body is a more complex system than even the most sophisticated machine, many of the same concepts that go into building and programming a machine can be applied to biological structures and diagnostic and therapeutic tools. A biomedical engineer is someone who analyzes and designs solutions to problems in biology and medicine, with the goal of improving the quality and effectiveness of patient care.
There is an increasing demand for biomedical engineers, due largely because of the general shift towards the everyday use of machinery and technology in all aspects of life. Biological knowledge combined with engineering principles to address medical needs has greatly contributed to the development of both life-changing and life-saving concepts and products such as: artificial organs; pacemakers; artificial hips; surgical robots; advanced prosthetics; and kidney dialysis. Even, for some kinds of diseases like infectious diseases, there are still a lot of needs for the BME to apply the technology for diagnosis, care and treament for patients. Since infectious diseases progress fast and have some simptom changes over the time. In addition, the possibility of transmission make the close contack risky for medical staffs and others. The application of non-contact approches could be of trend for the future in medical settings and home care.
Further more, in many LMIC, the medical training programs still lack the subjects of BME, so that later on, in health care settings, even in the big or national hospitals, there are not many biomedical engineers to take care of the machines, equipments.
In the future, the collaboration between the medical staffs and the biomedical engineers is impactul association not only for training, research but also for health care practices in order to bring the advances in technology for patients and community.

82nd Seminar

Date and Time:  November 29 (Fri.), 2019, 13:00 – 14:30
Place: Multimedia Hall #306, Building E-3(Map No.27), UEC
Speaker: Naoki TANAKA (Professor, Department of Biomedical Engineering, Toyo University)
Chair: Prof. Yoshiki KASHIMORI
Title: Causality indices and their application to biological systems
Abstract: 因果関係の概念は科学的研究にとって重要な概念の一つです.因果関係は,通常,緻密な計画に基づいた実験によって明らかにされます.一方,計画的な実験が容易でない場合もあります.近年,計測データ(特に時系列データ)のみに基づいて因果関係を推定する研究が盛んに行われています.セミナーでは,代表的な因果性指標であるGranger因果性,Transfer entropy, Convergent cross mapping 等を概観し,これらをヒトおよびマウス大脳皮質のネットワーク解析や脳血液量・心拍数・血圧等の揺らぎ関係解析等に応用した例を紹介します.

81st Seminar

Date and Time: November 28 (Thu.), 2019, 13:30 – 15:00
Place: Multimedia Hall #306, Building E-3(Map No.27), UEC
Speaker: Gerard Marriott (Professor, Department of Bioengineering, UC-Berkeley(University of California, Berkeley), USA)
Chair: Assoc. Prof. Shojiro MAKI
Title: Engineering new hydrogels for multiplexed detection of disease biomarkers and for passive release of medications
Abstract: I will present new findings from projects related to the engineering of hydrogels to detect disease biomarkers for at-home diagnostic devices, and for passive and long-term release of drugs to manage diseases of the eye. First, I introduce a novel bead-based immunocomplex entrapment assay (ICEA) and a related enzyme-linked ICEA (ELICEA) that allow for rapid and selective sequestration and entrapment of disease biomarkers with minimal needs for user-intervention and equipment. For example, in ICEA, target molecule-entrapment is achieved simply by injecting a bond-cleaving buffer, while in ELICEA, one also injects a chromogenic substrate. In both cases, sedimented beads generate brilliantly colored or fluorescent signals whose intensity correlates linearly with the amount of biomarker in the sample. In proof-of-practice studies, we used ICEA and ELICEA platforms to rapidly detect the kappa-light chain, a biomarker of the Bence-Jones disease, which we detected at a concentration that would correspond to an early stage of the disease. We also show the ICEA and ELICEA platforms can be used for multiplexed detection of biomarkers within individual beads. In the second part of my presentation, I will discuss a new type of hydrogel for long-term release of drugs to the eye, including glaucoma. In these studies, we use betadine, an FDA-approved medication to bring about specific chemical reactions in the eye that lead to the formation of drug-entrapped hydrogel from fluid precursors. Further optimization of the composition and structure of the hydrogel is used to delay the rate of drug release allowing passive and sustained release of the medication for up to 30 days. At the end of the therapy, the hydrogel is removed from the conjunctiva simply by bathing the eye in a dilute solution of cysteamine, which is also approved by the FDA to manage ocular conditions.

80th Seminar

Date and Time:  October 18 (Fri.), 2019, 13:00 – 14:30
Place: Multimedia Hall #306, Building E-3(Map No.27), UEC
Speaker: Takahiro Ushida (Professor, Multidisciplinary Pain Center and Institute of Physical Fitness, Aichi Medical University)
Chair: Assoc. Prof. Norihiro KOIZUMI
Title: Musculoskeletal pain and brain function changes
Abstract: 痛みは脳で経験する不快な感覚と情動体験であると定義されています.すぐに治る痛みであれば日常生活への影響は少ないですが、それが治らずに長引き治療抵抗性であるなどすると直接的な局所の障害にとどまらず,痛みに伴う身体の障害が疼痛行動も相まって我々の生活を大きく変えてしまいます.


79th Seminar

Date and Time:  September 9 (Tue.), 2019, 13:00 – 14:30
Place: Multimedia Hall #306, Building E-3(Map No.27), UEC
Speaker: Takashi SHINOZAKI, (Research Scientist, CiNet(Center for Information and Neural Networks), Osaka University)
Chair: Prof. Hayaru SHOUNO
Title: Interpretation of a convolutional neural network as a brain-like information processing system
Abstract: 深層学習の基盤技術のひとつである畳み込みニューラルネットワーク(Convolutional Neural Network, 以下CNN)は脳における情報処理機構をヒントに構成されたもので、従来の機械学習では困難であった、超多次元信号の解析可能な次元数への縮減を実現し、これによって人工知能技術の様々な対象への応用を可能としてきました。本講演ではCNNの内部動作を、脳における視覚情報処理と対比させることによって、その情報処理過程における意義について明らかにします。さらに超大規模なCNNの実現のために、より脳に近い、逆行伝播誤差を用いない学習法についても紹介します。


78th Seminar

Date and Time:  June 14 (Fri.), 2019, 15:00 – 16:30
Place: Meeting Room #802, Building E-4(Map No.11), UEC
Speaker: Atsu AIBA (Professor, Center for Disease Biology and Integrative Medicine, Faculty of Medicine, The University of Tokyo)
Chair: Assoc. Prof. Shinji MATSUDA
Title: Generation and analysis of model animals for neuropsychiatry diseases
Abstract: CRISPR/Cas9システムを用いたゲノム編集技術により、動物に導入する遺伝子変異の種類や変異を導入する動物種の制約が大幅に少なくなりました。我々の研究室では2種の遺伝性疾患、22q11.2欠失症候群および結節性硬化症に対するモデル動物の作製と解析を行っています。22q11.2欠失症候群は、ヒト22番染色体の微細欠失が原因で、患者の多くでは45の遺伝子の欠失が生じ、約30%が統合失調症を発症します。また、結節性硬化症は、TSC1もしくはTSC2遺伝子の変異が原因で、細胞増殖・成長に関与するmTOR経路の活性化が生じ、高頻度で自閉スペクトラム症等の精神疾患を発症します。本講演では、変異動物を用いた精神疾患モデル動物の現状と課題について議論したいと考えています。


77th Seminar

Date and Time:  May 31 (Fri.), 2019, 13:00 – 14:30
Place: Meeting Room #306, Building E-3, UEC
Speaker: Satoshi OOTA(Senior Research Scientist, Center of Advanced Photonics, Image Processing Research Team, RIKEN (Institute of Physical and Chemical Research))
Chair: Assoc. Prof. Tadashi YAMAZAKI
Title: Human augmentation by cognitive and motor intervention using endoskeleton robot suits (StillSuit) and high-precision augmented/virtual reality (LVAR)
Abstract: 私たちは産業技術総合研究所との共同研究において、認知・運動介入を実現するためのツールとして,高精度拡張/仮想現実(LVAR)と統合された内骨格ロボットスーツ(StillSuit)を開発しています。その目的は,生物学的に合理的な介入による認知・運動機能の強化と回復(生物学的な人間拡張)をもって,日本の直面する超高齢化問題の解決に貢献することです。そのためには、脳機能と運動機能の統合的な理解が欠かせません。内骨格ロボットスーツ開発のための基礎データの収集の一環として,遺伝学と生体力学を組み合わせた新しい枠組みと,モデル生物から得られるディープデータをヒトに外挿する方法について紹介します。また将来展望として、脳計算モデルと神経筋骨格モデルを結合する手法についても議論したいと思います。