Dr. Mitsuo KAWATO

日本語

Revised: May 15, 2013　

Director of ATR Brain Information Communication Research Laboratory Group

Research Supervisor of JST, Decoding and controlling brain information (Formal Site / Own Site)　

Visiting Professors of
Nara Institute of Science and Technology, Computational Neuroscience Laboratory
Kyoto University, Graduate School of Informatics
Toyama Prefectural University
Kanazawa Institute of Technology, Human Information System Laboratories
Osaka University, Graduate School of Frontier Biosciences
National Institute for Physiological Sciences
National Institute of Informatics
The University of Tokyo, Graduate School of Information Science and Technology,
Tokyo Institute of Technology, Precision and Intelligence Laboratory
Tamagawa University, Brain Science Institute

Computational Study of the Brain: From Sensory-Motor Integration to Communication

Computational Neuroscience
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.
Internal Models in the Cerebellum
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).
Robot Learning by watching
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.

1. Kawato M: From " Understanding the brain by creating the brain" toward Manipulative Neuroscience." Philosophical Transactions of the Royal Society B (2007)
2. Kawato M: Internal models for motor control and trajectory planning. Current Opinion in Neurobiology, 9,718-727(1999). (c) Elsevier Science Ltd.
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).
4. Gomi H, Kawato M: Equilibrium-point control hypothesis examined by measured arm-stiffness during multi-joint movement. Science 272,117-120(1996).
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.
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).
(c) Macmillan Magazines Ltd.
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.
8. Wolpert D, Kawato M: Multiple paired forward and inverse models for motor control. Neural Networks 11,1317-1329(1998). (c) Elsevier Science Ltd.

Curriculum Vitae

Date of Birth
November 12, 1953

Education
He received the B.S. degree in physics from Tokyo University in 1976 and the M.E. and Ph.D. degrees in biophysical engineering from Osaka University in 1978 and 1981, respectively.

Professional Positions
From 1981 to 1988, he was a faculty member and lecturer at Osaka University. From 1988, he was a senior researcher and then a supervisor in ATR Auditory and Visual Perception Research Laboratories. In 1992, he became department head of Department 3, ATR Human Information Processing Research Laboratories. In 2003, he became Director of ATR Computational Neuroscience Laboratories. Since 2004, he has been an ATR Fellow. In 2010, he became Director of ATR Brain Information Communication Research Laboratory Group. In 2008, he was appointed to the position of Research Supervisor of PRESTO, JST. And From 1996 to 2001, he was appointed director of the Kawato Dynamic Brain Project, ERATO, JST. Then from 2004 to 2009, he was appointed to the position of Research Supervisor of the Computational Brain Project, ICORP, JST. He is now concurrently working as a visiting professor at Kanazawa Institute of Technology, Nara Institute of Science and Technology, Osaka University, the National Institute for Physiological Sciences, Kyoto Prefectural University of Medicine, National Institute of Informatics and Graduate School of The University of Tokyo. He has been appointed Toyama Prefectural University as a specially appointed visiting professor.
He is a governing board member of the Japanese Society of Neuroscience and a Member of American Physiologica Society.

Awards
1986   Toyama award of The Toyama Foundation
1988   Grant-in-aid award of Brain Science Foundation
1991   Excellent paper award and Yonezawa Founder's Medal Memorial Special Award of
            The Institute of Electronics, Information and Communication Engineers
1992   Sawaragi memorial paper award of Society of Instrument and Control Engineers
1992   Outstanding Research Award of the International Neural Network Society
1993   Persons of scientific and technological research merits of commendation by
            the Ministry of state for Science and Technology
1993   Paper award of Japanese Neural Networks Society
1993   Research award of Japanese Neural Networks Society
1993   Osaka Science Prize
1994   Paper award of Japanese Neural Networks Society
1994   Research award of Japanese Neural Networks Society
1996   10th Tsukahara Naka-akira Memorial Award
1997   Paper award of Japanese Neural Networks Society
1997   The Okawa Publications Prize
1998   Tomoda paper award of the Society of Instrument and Control Engineers
2001   Tokizane Toshihiko memorial award
2001   Visual Science Festa Superior award
2004 IEICE Fellow (The Intstitute of Electronics, Information and Communication Engineers)
2005 58th Chunichi Cultural Award
2005 Shida Rinzaburo Award
2006 The Asahi Prize
2007 Paper award of Japanese Neural Networks Society
2007 APNNA Outstanding Achievement Award
2008 INNS Gabor Award
2008 Funai Achievement Award 2008
2009 Info-Communications Promotion Month Ministerial Commendations at 2009
2009 The OKAWA Prize
2012　2nd Tateisi Prize, Grand Award
2012　College of Fellows (International Neural Network Society)
2013 Awarded Japan's Purple Ribbon Medal