Dr. Mitsuo KAWATO
日本語
Revised: August 20, 2015 

The paper co-authored by Erhan Oztop and Michael A. Arbib, titled "Mirror neurons: Functions, mechanisms and models", is one of the most downloaded paper in the last 90 days among the papers published in Neuroscience Letters according to SciVerse (sciencedirect.com) as September 9, 2013.
http://www.journals.elsevier.com/neuroscience-letters/most-downloaded-articles/



Dire
ctor of ATR Brain Information Communication Research Laboratory Group, ATR Fwllow ATR_Logo.jpg
Research Supervisor of 
JST, Decoding and controlling brain information (Formal Site / Own Site) 


Center for Information and Neural Networks

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

Tokyo Institute of Technology, Precision and Intelligence Laboratory
Tamagawa University, Brain Science Institute

Deputy Director of Center for Information and Neural Networks (CiNet)



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. In 2013, he was jointly appointed as a Research Leader of BMI Technology/Field 3, SRPBS, Japanese MEXT. In 2015, he was jointly appointed as a Field reader of Brain research, Center for Novel Science Initiative, National Institutes of Natural Sciences, Japan.

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.
In addition, he belongs to the following Societies and so on;

  • The Board of Directors of Japan Neuroscience Society
  • The specially appointed Director of Japanese Neural Network Society
  • The Councilor of Union of Brain Science Associations in Japan
  • The Vice chair of Future plans committee of Union of Brain Science Associations in Japan
  • Foreign member of North America Society for Neuroscience
  • IEICE (The Institute of Electronics, Information and Communication Engineers) Fellow
  • Governing board member of Science Council of Japan
  • President of the 33rd Annual Meeting of the Japan Neuroscience Society
  • Director of TOYOBO Biofoundation

  • 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          


    Publications


    ATR Publication and Citation Statistics



    Links


    阪 大講義2014

    阪 大講義2012

    阪大講義2009


    NAIST講義2009


    東大医講義

     

    My favorites

        picture put angels soft