60th
| Date and Time: | March 2 (Fri.), 2018, 13:00 – 14:30 |
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| Place: | Meeting room #306, Building E-3, UEC |
| Speaker: | Dr. Motohiro KAWASAKI (Lecturer, Department of Orthopaedic Surgery, Kochi Medical School, Kochi University) |
| Chair: | Assoc. Prof. Norihiro KOIZUMI |
| Title: | Treatment of Chronic Pain Associated with Bone and Joint Diseases by using Focused Ultrasound |
| Abstract: | 痛みの緩和は、いずれの骨関節疾患においても対処すべき重要な課題です。なかでも、長引く痛みである慢性痛は心身に影響を及ぼし、患者の活動性や生活の質を低下させます。このような痛みに対してさまざまな治療が実施されますが、できるだけ身体的負担が少なく効果的な治療法が理想的です。今回紹介する集束超音波治療は、体表に侵襲を与えることなく、多数の強力超音波を体内で集束させて熱による蛋白変性を利用し標的部位を治療します。これをMR画像の誘導により、安全にピンポイントの疼痛緩和治療が達成できます。この治療効果を生かして、痛みを伴う骨転移、慢性の腰痛や膝痛に対する治療を実施してきましたので、その成果と今後の課題について発表いたします。 |
59th
| Date and Time: | January 19 (Fri.), 2018, 13:00 – 14:30 |
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| Place: | Meeting room #306, Building E-3, UEC |
| Speaker: | Dr. Ken Takiyama (Associate Professor, Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology) |
| Chair: | Prof. Hayaru Shono |
| Title: | Prospective coding in human motor learning and decision making (Talk spoken in Japanese) |
| Abstract: | In our daily life, we make predictions in various situations, e.g., we predict tomorrow’s weather, outcomes of soccer matches, or stock price. In those predictions, our neural system receives some inputs (e.g., sky scene in predicting tomorrow’s weather) and represent future states (e.g., tomorrow’s weather). This representation of future states is referred to as prospective coding (ref. Komura et al., 2001). Here, I demonstrate that the prospective coding plays an essential role in human motor learning and motor decision making. First, I explain about our computational model of motor learning. Diverse features of motor learning have been reported in numerous studies, but no single theoretical framework concurrently accounts for these features. We propose models for motor learning to explain these features in a unified way by extending a motor primitive framework (ref. Thoroughman & Shadmehr, 2000, Nature). Our model assumes that the recruitment pattern of motor primitives is determined by the predicted movement error of an upcoming movement (prospective error). I demonstrate that this model has a strong explanatory power to reproduce a wide variety of motor-learning-related phenomena that have been separately explained by different computational models. Second, I explain about motor decision making in a competitive game. Although risk-seeking behavior in human motor decision making has been reported in several studies (e.g., Wu et al., 2009), those studies focused on an experiment with a single subject. In our daily life (especially in music or sports), our decision making (action selection) can be influenced by opponents in competitive games and partners in collaborative games; however, how decision making is affected by others remains unclear. Our experimental results demonstrate that subjects show risk-averse behavior at the onset of a competitive game, in contrast to risk-seeking behavior when they performed the same movement without any opponent. To understand the risk-averse behavior in a competitive game, we propose a computational model. Our computational model suggests that the risk-averse behavior is a result of optimization when our decision making is influenced by the predicted actions and results of ourselves and opponents (prospective outcome). References: [1] K. Takiyama, M. Hirashima, D. Nozaki, Prospective errors determine motor learning, Nature Communications, 6, 5925: 1-12 (2015), [2] K. Ota, K. Takiyama, Competitive game influences risk-sensitivity in motor decision-making, Program No. 316.2. 2017 Washington, DC: Society for Neuroscience, 2017. |
58th
| Date and Time: | December 15 (Fri.), 2017, 13：00 – 14:30 |
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| Place: | Meeting room #306, Building E-3, UEC |
| Speaker: | Dr. Jun Igarashi (Senior Center Researcher, Advanced Center for Computing and Communication, RIKEN) |
| Chair: | Assoc. Prof. Tadashi Yamazaki |
| Title: | Simulation of the neural network with the size of the human cerebral cortex using an exa-flops class computer |
| Abstract: | 近年、スーパーコンピュータを用いた脳のシミュレーションが盛んに行われている。しかし、究極の目標である約1000億個の神経細胞と約1000兆個結合を持つ人間の脳の規模の神経回路シミュレーションは、現在の計算機では性能不足のため困難である。そこで、我々は2021年頃に完成する京コンピュータの次の世代のエクサフロップス級（1秒間に10の18乗の浮動小数点演算）の計算機を用いて、大脳皮質、小脳、大脳基底核からなる人間の全脳規模の脳シミュレーションを行い、運動や思考の解明を行うことを目指している。本講演では、私のグループが担当している大脳皮質のシミュレーションに関する取り組みを中心に紹介する。 |
57th
| Date and Time: | November 28 (Tue.), 2017, 14：00 – 15:30 |
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| Place: | Meeting room #306, Building E-3, UEC |
| Speaker: | Dr. Kazuhisa Kohda (Professor, Laboratory of Physiology, St. Marianna University School of Medicine) |
| Chair: | Assoc. Prof. Shinya Matsuda |
| Title: | How does the signaling of Cbln1-delta2glutamate receptor control the formation, maintenance and plasticity of synapses? |
| Abstract: | 脳の様々な部位で発生期から成体に至るまで生じているシナプス形成・維持とその可塑性は、脳がその機能を実現する上で必須の現象である。我々は、運動の協調性や運動学習に重要な役割を果たす、小脳の平行線維－プルキンエ細胞シナプスにおけるその分子機構について、特にCbln1－デルタ２グルタミン酸受容体（GluD2）シグナリングに焦点を当てて研究を進めてきた。本セミナーでは、GluD2及びCbln1欠損マウスを用いた表現型回復実験を通して明らかになった、平行線維－プルキンエ細胞シナプスの形成・維持と可塑性の特異なメカニズムを紹介するとともに、その普遍的意義について議論したい。 |
56th
| Date and Time: | November 14 (Tue.), 2017, 13：00 – 14:30 |
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| Place: | Meeting room #306, Building E-3, UEC |
| Speaker: | Dr. Dmitri B. Papkovsky (Professor, School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland) |
| Chair: | Prof. Kazuto Masamoto |
| Title: | New insights into cell/tissue function and metabolism by means of phosphorescent oxygen sensing probes |
| Abstract: | Molecular oxygen (O2) has a multitude of important biological roles. It is also a useful marker of cell/tissue function and readout parameter which can report on changes in cell metabolism and bioenergetics, tissue (patho)physiology, responses to drug treatment and other stimuli. Various in vitro, ex-vivo and in vivo cell and tissue models are currently used in biomedical research, however for many of them control of sample oxygenation and cellular O2 levels is inadequate. Phosphorescence based O2 sensing technologies can address these challenges and provide convenient and versatile means for direct, real-time, quantitative monitoring of O2 levels in various compartments of complex biological samples, including in situ monitoring of cellular O2 and high-resolution mapping O2 concentration in 3D. A number of advanced O2 sensing and imaging platforms have been developed in recent years, which operate with solid-state sensors, soluble probes or imaging nanosensors and in conjunction with portable handheld instruments, commercial plate readers and sophisticated live cell imaging platforms. I will provide examples how these sensor systems can be used in physiological studies with simple 2D cell models, more complex micro-tissue models (multicellular spheroids, heterocellular organoids, cultured tissue slices), live animals, and with common disease models such as hypoxia, cancer, inflammation. |
55th
| Date and Time: | October 25 (Wed.), 2017, 13：00 – 14:30 |
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| Place: | Meeting room #306, Building E-3, UEC |
| Speaker: | Dr. Soichi Ando (Associate Professor, Department of Mechanical Engineering and Intelligent Systems, Graduate School of Informatics and Engineering, The University of Electro-Communications) |
| Chair: | Prof. Hidetaka Okada |
| Title: | Transient excise and cognitive function |
| Abstract: | 近年，継続した運動だけではなく，一回の運動であっても認知機能に対して有益な効果がみられることは広く知られるようになりました．そこで今回のセミナーでは，低酸素環境下など様々な条件下での一過性の運動がヒトの認知機能に及ぼす影響に関して，我々のデータを中心に紹介します．さらに，一過性の運動による脳血流の変化が認知機能にどのような影響を及ぼすのかについて検討した研究についても紹介します．最後に，なぜ一過性の運動が認知機能を向上させるのかについて議論したい． |
54th
| Date and Time: | August 4 (Fri.), 2017, 13：00 – 14:30 |
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| Place: | Meeting room #306, Building E-3, UEC |
| Speaker: | Dr. Tomoki Fukai (Laboratory Head, Laboratory for Neural Circuit Theory, Brain Science Institute, RIKEN) |
| Chair: | Prof. Shigeru Tanaka |
| Title: | Brain’s network mechanisms to model the external world |
| Abstract: | How does the brain model the external world? What circuit mechanisms underlie this computation? These questions are deeply connected to the biological mechanisms of learning. To understand the underlying mechanisms of brain’s modeling of the external world, I show two recent computational studies from my group. The first topic is sequence learning in the hippocampus. Recently, the role of spontaneous activity, or preplay, in the formation of place-cell sequences has been debated. Preplay is a phenomenon in which spontaneous sequences preexisting before experience turn into place-cell sequences during spatial navigation. Preplay suggests that the innate structure of cortical circuits is useful for information coding, and hence is conceptually interesting. We construct a recurrent network of two-compartment neurons to utilize spontaneous sequences for robust one-shot learning of place-cell sequences. Our model suggests that dendritic computation in pyramidal cells is crucial for this processing. The second topics is chunking, which is the ability of the brain to detect repeated segments of patterns from complex sequences. I will present a novel mechanism of chunk learning in recurrent neural networks. The key is to use independent multiple reservoir computing systems that supervise each other during learning. Interestingly, readout neurons in our model behaves like “stop cells” which have been discovered in the basal ganglia after motor sequence learning. |
53rd
| Date and Time: | July 20 (Thu.), 2017, 13：00 – 14:30 |
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| Place: | Meeting room #301, Building E-3, UEC |
| Speaker: | Prof. CAO Qixin (Professor, School of Mechanical Engineering, Shanghai Jiaotong University, Visiting Professor of BLSC, UEC) |
| Chair: | Assoc. Prof. JIANG Yinlai |
| Title: | Present Status and Future Prospect in Application of Robotics to Surgical Operations |
| Abstract: | 中国のロボット販売台数は４年連続で世界No. 1となっている。その中でも、外科手術ロボットは、産業用ロボットに続いて将来第２位の売上規模になると予想されている。外科手術ロボットは、統合医学、ロボット工学、材料科学、機械工学、コンピュータおよび情報技術をインテグレーション（総合）する必要がある複雑なロボットシステムである。この高度な技術の応用は、伝統的な外科技術に大きな変化と効果をもたらしている。本講演では、外科手術の動向を紹介した上で整形外科手術、インターベンション手術、低侵襲内視鏡手術の３つの側面からロボット手術の研究状況を紹介する。医療ロボット開発の分野では，外科手術ロボットのダヴィンチと比べて、過大な外科手術スペースが要求されず、外傷が少なく、手術後の回復が速い単孔式外科手術ロボットが、次世代の手術ロボットプラットフォームとして期待されている。 |
52nd
| Date and Time: | June 13 (Tue.), 2017, 13：00 – 14:30 |
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| Place: | Meeting room #306, Building E-3, UEC |
| Speaker: | Dr. Haruo Hosoya (and Dr. Aapo Hyvarinen) (Senior Researcher, Department of Dynamic Brain Imaging, Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International (ATR)) |
| Chair: | Prof. Yoichi Miyawaki |
| Title: | A mixture of sparse coding models explaining properties of face neurons related to holistic and parts-based processing |
| Abstract: | Experimental studies have revealed evidence of both parts-based and holistic representations of objects and faces in the primate visual system. However, it is still a mystery how such seemingly contradictory types of processing can coexist within a single system. Here, we propose a novel theory called mixture of sparse coding models, inspired by the formation of category-specific subregions in the inferotemporal (IT) cortex. We developed a hierarchical network that constructed a mixture of two sparse coding submodels on top of a simple Gabor analysis. The submodels were each trained with face or non-face object images, which resulted in separate representations of facial parts and object parts. Importantly, evoked neural activities were modeled by Bayesian inference, which had a top-down explaining-away effect that enabled recognition of an individual part to depend strongly on the category of the whole input. We show that this explaining-away effect was indeed crucial for the units in the face submodel to exhibit significant selectivity to face images over object images in a similar way to actual face-selective neurons in the macaque IT cortex. Furthermore, the model explained, qualitatively and quantitatively, several tuning properties to facial features found in the middle patch of face processing in IT as documented by Freiwald, Tsao, and Livingstone (2009). These included, in particular, tuning to only a small number of facial features that were often related to geometrically large parts like face outline and hair, preference and anti-preference of extreme facial features (e.g., very large/small inter-eye distance), and reduction of the gain of feature tuning for partial face stimuli compared to whole face stimuli. Thus, we hypothesize that the coding principle of facial features in the middle patch of face processing in the macaque IT cortex may be closely related to mixture of sparse coding models. |
51st
| Date and Time: | June 9 (Fri.), 2017, 13：00 – 14:30 |
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| Place: | Meeting room #306, Building E-3, UEC |
| Speaker: | Dr. Hiroshi Kawaguchi (Senior Researcher, Brain Function Measurement Research Group, Human Informatics Research Institute, National Institute of Advanced Industrial Science and Technology (AIST)) |
| Chair: | Prof. Kazuto Masamoto |
| Title: | R&D of elementary technologies of fNIRS for brain function monitoring in human life environment — toward actual implementation of neuro-rehabilitation — |
| Abstract: | 機能的近赤外分光法(fNIRS)は可搬性の高さや拘束性の低さから生活環境における脳機能モニタリングに適しています。一方、ノイズの影響が大きいことや計測が不安定であることが技術的課題として残されています。本セミナーではこれらfNIRSにおける課題の克服に向けて開発している要素技術を紹介します。また、ニューロリハビリテーションの社会実装を加速するために脳損傷後の運動機能の回復過程をサルモデルで解析しており、fNIRSを脳機能評価に応用しています。fNIRSをサルに適用した際の技術開発や損傷モデルでの計測から得られた知見も紹介します。 |