{"id":402,"date":"2018-01-11T10:05:02","date_gmt":"2018-01-11T01:05:02","guid":{"rendered":"http:\/\/www.cns.atr.jp\/kawato?page_id=402"},"modified":"2018-03-08T15:20:25","modified_gmt":"2018-03-08T06:20:25","slug":"top","status":"publish","type":"page","link":"https:\/\/bicr.atr.jp\/kawato\/en?page_id=402","title":{"rendered":"Research"},"content":{"rendered":"

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\nATR\u306b\u51fa\u5411\u3057\u3066\u3044\u305fNTT\u306e\u4e94\u5473\u88d5\u7ae0\u3055\u3093\u3089\u3068\u767a\u5c55\u3055\u305b\u305f\u5c0f\u8133\u5185\u90e8\u30e2\u30c7\u30eb[1,2]\u3001ATR\u306b\u79c1\u3092\u62db\u3044\u3066\u4e0b\u3055\u3063\u305f\u4e7e\u654f\u90ce\u5148\u751f\u3068\u63d0\u6848\u3057\u305f\u9806\u9006\u5149\u5b66\u30e2\u30c7\u30eb[3]\u3001Daniel Wolpert\u3055\u3093\u3089\u3068\u63d0\u6848\u3057Neural Networks\u8a8c\u306b\u63b2\u8f09\u3057\u305fMOSAIC[4]\u306a\u3069\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n

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\n<\/strong>\u30ed\u30dc\u30c3\u30c8\u306b\u8133\u306e\u30e2\u30c7\u30eb\u3092\u57cb\u3081\u8fbc\u3080\u3068\u8a00\u3046\u65ac\u65b0\u306a\u30d1\u30e9\u30c0\u30a4\u30e0\u3092\u3001ERATO\u5b66\u7fd2\u52d5\u614b\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u306e\u30b0\u30eb\u30fc\u30d7\u30ea\u30fc\u30c0\u3092\u52d9\u3081\u3066\u304f\u308c\u305fStefan Schaal\u3055\u3093\u3001ICORP\u8a08\u7b97\u8133\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u306e\u7c73\u56fd\u5074\u306e\u76f8\u65b9\u306eChristopher Atkeson\u3055\u3093\u3068\u30b0\u30eb\u30fc\u30d7\u30ea\u30fc\u30c0\u306eGordon Cheng\u3055\u3093\u7b49\u3068\u69cb\u7bc9\u3057\u307e\u3057\u305f[5]\u3002\u73fe\u5728ATR\u8133\u60c5\u5831\u7814\u7a76\u6240\u30d6\u30ec\u30a4\u30f3\u30ed\u30dc\u30c3\u30c8\u30a4\u30f3\u30bf\u30d5\u30a7\u30fc\u30b9\u7814\u7a76\u5ba4\u30fb\u5ba4\u9577\u306e\u68ee\u672c\u6df3\u3055\u3093\u304cMiguel Nicolelis\u3055\u3093\u3068\u884c\u3063\u305f\u3001Duke\u5927\u5b66\u306e\u30b5\u30eb\u3068ATR\u306e\u30d2\u30e5\u30fc\u30de\u30ce\u30a4\u30c9\u3092\u30a4\u30f3\u30bf\u30fc\u30cd\u30c3\u30c8\u3067\u53cc\u65b9\u5411\u306b\u7e4b\u3044\u3067\u3001\u30d6\u30ec\u30a4\u30f3\u30de\u30b7\u30f3\u30a4\u30f3\u30bf\u30d5\u30a7\u30fc\u30b9\u306b\u3088\u3063\u3066\u6b69\u884c\u3055\u305b\u308b\u5b9f\u9a13\u304c\u5370\u8c61\u6df1\u304b\u3063\u305f\u3067\u3059[6]\u3002<\/span><\/p>\n

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\n<\/span><\/strong>\u69d8\u3005\u306a\u8a08\u7b97\u30e2\u30c7\u30eb\u3092\u5b9f\u9a13\u3067\u691c\u8a3c\u3059\u308b\u3053\u3068\u3084\u3001\u7406\u8ad6\u306b\u3082\u3068\u3065\u304f\u5b9f\u9a13\u3092\u884c\u3046\u4e8b\u304c\u51fa\u6765\u307e\u3057\u305f\u3002\u5f53\u6642\u96fb\u7dcf\u7814\u306b\u304a\u3089\u308c\u305f\u6cb3\u91ce\u61b2\u4e8c\u5148\u751f\u306e\u7814\u7a76\u30b0\u30eb\u30fc\u30d7\u3068\u5171\u540c\u3067\u5c0f\u8133\u5185\u90e8\u30e2\u30c7\u30eb\u4eee\u8aac\u3092\u691c\u8a3c\u3059\u308b\u3053\u3068\u304c\u51fa\u6765\u305f\u306e\u306f\u3068\u3066\u3082\u5e78\u904b\u3067\u3057\u305f[7,8]\u3002\u4e94\u5473\u88d5\u7ae0\u3055\u3093\u3001\u5927\u9808\u7406\u82f1\u5b50\u3055\u3093\u3084Etienne Burdet\u3055\u3093\u3089\u3068\u306f\u30ed\u30dc\u30c3\u30c8\u30de\u30cb\u30d4\u30e5\u30e9\u30f3\u30c0\u30e0[9,10]\u3001\u4eca\u6c34\u5bdb\u3055\u3093\u7b49\u3068\u306ffMRI[11]\u3092\u7528\u3044\u305f\u5b9f\u9a13\u306b\u3082\u624b\u3092\u4ed8\u3051\u308b\u3088\u3046\u306b\u306a\u308a\u3001\u5f37\u5316\u5b66\u7fd2\u30e2\u30c7\u30eb\u306b\u57fa\u3065\u304ffMRI\u5b9f\u9a13\u306b\u3064\u3044\u3066\u306f\u3001\u79c1\u3068\u9285\u8c37\u8ce2\u6cbb\u3055\u3093\u3001\u6625\u91ce\u96c5\u5f66\u3055\u3093\u304c\u3001\u4e16\u754c\u3067\u521d\u3081\u3066computational model based neuroimaging\u3068\u8a00\u3046\u7528\u8a9e\u3092\u4f7f\u3044\u307e\u3057\u305f [12,13]\u3002<\/span><\/p>\n

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\n\u8d85\u591a\u6b21\u5143\u306e\u30c7\u30fc\u30bf\u3092\u591a\u6570\u306e\u30b5\u30f3\u30d7\u30eb\u306b\u3064\u3044\u3066\u8a08\u6e2c\u3057\u3001\u6a5f\u68b0\u5b66\u7fd2\u3092\u5fdc\u7528\u3057\u3066\u8133\u79d1\u5b66\u3092\u7cbe\u5bc6\u79d1\u5b66\u306b\u3059\u308b\u3068\u3044\u3046\u65b9\u5411\u6027\u304c\u6709\u671b\u3060\u3068\u611f\u3058\u3066\u3044\u307e\u3059\u3002\u4f8b\u3048\u3070\u5b89\u9759\u6642\u8133\u6d3b\u52d5\u3092\u8907\u6570\u306e\u30e2\u30c0\u30ea\u30c6\u30a3\u30fc\u3067\u9577\u6642\u9593\u8a08\u6e2c\u3059\u308b\u7cbe\u5bc6\u306a\u5b9f\u9a13\u3001\u60a3\u8005\u3055\u3093\u3092\u542b\u30802\u5343\u4eba\u306e\u8133\u30d3\u30c3\u30b0\u30c7\u30fc\u30bf[14]\u3001David Marr\u6d41\u306e\u8a08\u7b97\u7406\u8ad6\u3068\u8133\u30c0\u30a4\u30ca\u30df\u30af\u30b9\u30e2\u30c7\u30eb\u304c\u521d\u3081\u3066\u7d71\u5408\u3055\u308c\u308b\u5146\u3057\u304c\u3042\u308b\u3053\u3068\u3001\u67f4\u7530\u548c\u4e45\u3055\u3093\u3001\u6e21\u908a\u6b66\u90ce\u5148\u751f\u3001\u4f50\u3005\u6728\u7531\u9999\u5148\u751f\u3068\u5171\u540c\u3067\u958b\u767a\u3057\u305fDecoded Neurofeedback[15]\u3084\u3001\u798f\u7530\u3081\u3050\u307f\u3055\u3093\u304c\u4e2d\u5fc3\u306b\u306a\u3063\u3066\u958b\u767a\u3057\u305fFunctional Connectivity Neurofeedback[16]\u306a\u3069\u306e\u65b0\u3057\u3044\u30c4\u30fc\u30eb\u3092\u7528\u3044\u3066\u8133\u6d3b\u52d5\u304b\u3089\u5fc3\u3078\u306e\u56e0\u679c\u95a2\u4fc2\u3092\u660e\u3089\u304b\u306b\u51fa\u6765\u308b\u795e\u7d4c\u79d1\u5b66\u3092\u69cb\u7bc9\u3057\u305f\u3044\u3068\u8003\u3048\u3066\u3044\u307e\u3059[17]\u3002<\/p>\n


\n<\/strong>[1] Kawato, M., Furukawa, K., Suzuki, R. (1987): A hierarchical neural-network model for control and learning of voluntary movement, <\/span>Biological Cybernetics<\/em>, <\/span>Vol.57<\/strong>, pp.169-185.<\/span><\/p>\n[2] Kawato, M., Gomi, H. (1992): A computational model of four regions of the cerebellum based on feedback-error-learning, Biological Cybernetics<\/em>, Vol.68<\/strong>, pp.95-103.<\/p>\n[3] Kawato, M., Hayakawa, H., Inui, T. (1993): A forward-inverse optics model of reciprocal connections between visual cortical areas, Network:Computation in Neural systems<\/em>, Vol.4<\/strong>, pp.415-422.<\/p>\n[4] Wolpert, D., Kawato, M. (1998): Multiple paired forward and inverse models for motor control, Neural Networks<\/em>, Vol.11,<\/strong> pp.1317-1329.<\/p>\n[5] Atkeson, CG., Hale, J., Pollick, F., Riley, M., Kotosaka, S., Schaal, S., Shibata, T., Tevatia, G., Vijayakumar, S., Ude, A., Kawato, M. (2000): Using humanoid robots to study human behavior, IEEE Intelligent Systems: Special Issue on Humanoid Robotics<\/em>, Vol.15<\/strong>, pp.46-56.<\/p>\n[6] Morimoto, J., Kawato, M. (2015): Creating the brain and interacting with the brain: an integrated approach to understanding the brain, Journal of the Royal Society Interface<\/em>, Vol.12<\/strong>, 20141250.<\/p>\n[7] Shidara, M., Kawano, K., Gomi, H., Kawato, M. (1993): Inverse-dynamics model eye movement control by purkinje cells in the cerebellum, Nature<\/em>, Vol.365<\/strong>, pp.50-52.<\/p>\n[8] Kawato, M. (1999): Internal models for motor control and trajectory planning, Current Opinion in Neurobiology<\/em>, Vol.9<\/strong>, pp.718-727.<\/p>\n[9] Gomi, H., Kawato, M. (1996): Equilibrium-point control hypothesis examined by measured arm-stiffness during multijoint movement, Science<\/em>, Vol.272<\/strong>, pp.117-120.<\/p>\n[10] Burdet, E., Osu, R., Franklin, D., Milner, T., Kawato, M. (2001): The central nervous system stabilizes unstable dynamics by learning optimal impedance, Nature<\/em>, Vol.414<\/strong>, pp.446-449.<\/p>\n[11] Imamizu, H., Miyauchi, S., Tamada, T., Sasaki, Y., Takino, R., Puetz, B., Yoshioka, T., Kawato, M. (2000): Human cerebellar activity reflecting an acquired internal model of a new tool, Nature<\/em>, Vol.403<\/strong>, pp.192-195.<\/p>\n[12] Haruno, M., Kuroda, T., Doya, K., Toyama, K., Kimura, M., Samejima, K., Imamizu, H., Kawato, M. (2004): A neural correlate of reward-based behavioral learning in caudate nucleus: a functional magnetic resonance imaging study of a stochastic decision task, Journal of Neuroscience<\/em>, Vol.24<\/strong>, pp.1660-1665.<\/p>\n[13] Haruno, M., Kawato, M. (2006): Different neural correlates of reward expectation and reward expectation error in the putamen and caudate nucleus during stimulus-action-reward association learning, Journal of Neurophysiology<\/em>, Vol.95<\/strong>, pp.948-959.<\/p>\n[14] Yahata, N., Morimoto, J., Hashimoto, R., Lisi, G., Shibata, K., Kawakubo, Y., Kuwabara, H., Kuroda, M., Yamada, T., Megumi, F., Imamizu, H., Nanez, JE.,\u00a0Takahashi, H., Okamoto, Y., Kasai, K., Kato, N., Sasaki, Y., Watanabe, T., Kawato, M. (2016): A small number of abnormal brain connections predicts adult autism spectrum disorder, Nature Communications<\/em>, Vol.7<\/strong>, p.11254.<\/p>\n[15] Shibata, K., Watanabe, T., Sasaki, Y., Kawato, M. (2011): Perceptual learning incepted by decoded fMRI neurofeedback without stimulus presentation, Science<\/em>, Vol.334<\/strong>, No.6061, pp.1413-1415<\/p>\n[16] Megumi, F., Yamashita, A., Kawato, M., Imamizu, H. (2015): Functional MRI neurofeedback training on connectivity between two regions induces long-lasting changes in intrinsic functional network, Frontiers in Human Neuroscience<\/em>, Vol.9<\/strong>, p.160.<\/p>\n[17] Watanabe, T., Sasaki, Y., Shibata, K., Kawato, M. (2017): Advances in fMRI real-time neurofeedback,\u00a0Trends in Cognitive Sciences<\/em>, Vol.21<\/strong>, pp.997-1010<\/p>\n

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Computational Study of the Brain: From Sensory-Motor Integration to Communication<\/span><\/strong><\/span><\/p>\n

Computational Neuroscience
\n<\/span><\/strong>Neuroscience, the discipline which studies structures and functions of the brain, has developed enormously in the past 50 years. Unfortunately, its major successes are limited to elucidating brain loci responsible for some functions, and identifying substances included in some brain processes. We are still quite ignorant about information representations in the brain, as well as about information processing in the brain for specific computations. If we had enough knowledge about these, we would be able to build artificial machines or computer programs that could solve difficult problems such as visual information processing, smooth and dexterous motor control, or natural language processing. After reflecting on these past failures of conventional neuroscience research, we adopted the computational approach. That is, we construct a brain in order to understand the brain, and we understand the brain through building a brain and to the extent that we can build a brain. More concretely, we investigated the information processing of the brain with the long-term goal that machines, either computer programs or robots, could solve the same computational problems as those that the human brain solves, while using essentially the same principles (ref 1). With these general approaches, we made progresses in elucidating visual information processing, optimal control principles for arm trajectory planning, internal models in the cerebellum, teaching by demonstration for robots (ref 1), human interfaces based on electoromyogram, applications in rehabilitation medicine, and so on. Because of space limitations, I explain here only internal models and robot learning.<\/p>\n

Internal Models in the Cerebellum
\n<\/span><\/strong>Internal models are neural networks within the brain that mimic input-output transformation of some dynamical processes in the external (to the brain) world (ref 2). We postulated that the cerebellum acquires internal models of motor apparatus through motor learning. Our specific theory called feedback-error-learning model predicts that the climbing fiber inputs encode the error signal in the motor-command coordinates, and the cerebellar cortex acquires the inverse dynamics model by changing synaptic weights between parallel-fiber inputs and Purkinje cells. These predictions have been confirmed by monkey physiological experiments (ref 1,3), human behavioral experiments (ref 4,5), and human brain imaging (ref 6,7). It is now generally accepted that cerebellar internal models are important not only for sensory-motor integration, but also for human cognitive functions (ref 1,3,8).<\/p>\n

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Robot Learning by watching<\/strong><\/span><\/p>\n

Brain functions cannot be studied dealing with only the brain. We also need to reproduce bodies and surrounding environments. Then, it is obvious that robotics research is very much related. In the past, this scientific objective of robotics to elucidate information processing of human intelligence has not been emphasized. Furthermore, on the contrary, this objective was even hidden, made implicit or neglected. We have developed a humanoid robot DB for computational neuroscience research with the help from SARCOS. DB is quick in movements, very compliant, with the same dimension and weight with humans, and possesses 30 degrees of freedom. It has four cameras, artificial vestibular sensor, joint angle sensors and force sensors for all the actuators (DB’s Home page). DB now can demonstrate 24 different behaviors. They are classified into 3 main classes. The first class is learning from demonstration (1) Okinawa Dance Imitation (Kachyaasi), (2) Rock’n Roll Dance Imitation, (3) Pole Balancing Imitation, (4) Tennis Swing Imitation, (5) Real-Time Visual Tracking of Human Motion, (6) Punching Imitation, (7) Juggling, (8) Devil Stick, (9) Real-Time Hand Movement Imitation, (10) Air Hockey Imitation, (11) Tumbling a box, (12) Moving a small box and Robota. The second class is eye movements, and includes (13) VOR Adaptation. (14) Smooth Pursuit Learning, (15) Saccade, (16) Combination of 3 Eye Movement Primitives. The third class depends on task dynamics, physical interaction, and learning (17) Paddling, (18) Learning of Visuo-Motor Transformation, (19) Catching a Ball, (20) Drumming Joint-Performance, (21) Sticky Hand, (22) Non-Calibrated Visuo-Motor Transformation, (23) Yo-yo and Slinky, (24) Flexible Object Manipulation. Essential computational principles of some of these demonstrations are (A) cerebellar internal models, (B) reinforcement learning in the basal ganglia, and (C) cerebral stochastic internal model.<\/p>\n

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1. Kawato M: From ” Understanding the brain by creating the brain” toward Manipulative Neuroscience.” Philosophical Transactions of the Royal Society B (2007)<\/p>\n

2. Kawato M: Internal models for motor control and trajectory planning. Current Opinion in Neurobiology, 9,718-727(1999). (c) Elsevier Science Ltd.<\/p>\n

3. Shidara M, Kawano K, Gomi H, Kawato M: Inverse-dynamics model eye movement control by Purkinje cells in the cerebellum. Nature 365,50-52(1993).<\/p>\n

4. Gomi H, Kawato M: Equilibrium-point control hypothesis examined by measured arm-stiffness during multi-joint movement. Science 272,117-120(1996).<\/p>\n

5. Burdet E, Osu R, Franklin D, Milner T, Kawato M: The central nervous system stabilizes unstable dynamics by learning optimal impedance. Nature, 414,446-449(2001). (c) Macmillan Magazines Ltd.<\/p>\n

6. Imamizu H, Miyauchi S, Tamada T, Sasaki Y, Takino R, Puetz B, Yoshioka T, Kawato M: Human cerebellar activity reflecting an acquired internal model of a novel tool. Nature, 403,192-195(2000).<\/p>\n

(c) Macmillan Magazines Ltd.<\/p>\n

7. Imamizu H, Kuroda T, Miyauchi S, Yoshioka T, Kawato M: Modular organization of internal models of tools in the human cerebellum. Proc Natl Acad Sci USA., 100,5461-5466 (2003).(c) PNAS.<\/p>\n

8. Wolpert D, Kawato M: Multiple paired forward and inverse models for motor control. Neural Networks 11,1317-1329(1998). (c) Elsevier Science Ltd.<\/p>\n

9. Kawato M: Brain controlled robots. HFSP Journal, 2(3), 136-142 (2008)<\/p>\n

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(1987): A hierarchical neural-network model for control and learning of voluntary movement, Biological Cybernetics, Vol.57, pp.169-185. [2] Kawato, M., Gomi, H. (1992): A computational model of four regions of the cerebellum based on feedback-error-learning, Biological Cybernetics, Vol.68, pp.95-103. [3] Kawato, M., Hayakawa, H., Inui, T. (1993): A forward-inverse optics model of reciprocal connections between visual cortical areas, Network:Computation in Neural systems, Vol.4, pp.415-422. [4] Wolpert, D., Kawato, M. (1998): Multiple paired forward and inverse models for motor control, Neural Networks, […]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-402","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/bicr.atr.jp\/kawato\/en\/index.php?rest_route=\/wp\/v2\/pages\/402","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/bicr.atr.jp\/kawato\/en\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/bicr.atr.jp\/kawato\/en\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/bicr.atr.jp\/kawato\/en\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/bicr.atr.jp\/kawato\/en\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=402"}],"version-history":[{"count":7,"href":"https:\/\/bicr.atr.jp\/kawato\/en\/index.php?rest_route=\/wp\/v2\/pages\/402\/revisions"}],"predecessor-version":[{"id":410,"href":"https:\/\/bicr.atr.jp\/kawato\/en\/index.php?rest_route=\/wp\/v2\/pages\/402\/revisions\/410"}],"wp:attachment":[{"href":"https:\/\/bicr.atr.jp\/kawato\/en\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=402"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}