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4 (1) (2) () (4) (5) Doya K. What are the computations of the cerebellum, the basal ganglia, and the cerebral cortex. Neural Networks,12, ,1999JNNSJapanese Neural Network Society

5 Kawato, M., et al., Biol. Cybern.,57, , Functional Magnetic Resonance Imaging fmri fmri fmri Nature Imamizu H., et al., Human cerebellar activity reflecting an acquired internal model of a new tool, Nature, 403(6766), , JAN13, Nature Burdet E. et al., The central nervous system stabilizes unstable dynamics by learning optimal impedance, Nature, 414(6862), , NOV22,

, Human cerebellar activity reflecting an acquired internal model of a new tool, Nature, 403(6766), 192-195,

6 (1) 1 Dynamic Brain DB DB psi 185cm 80kg DB (2) 30 DB DB (3) DB DB DB (4) DB 5

7 CREST () () OIST fmri Module Selection and Identification for Control MOSAICMOSAIC fMRI fmri 6

8 BMIBrain Machine Interface fmri BMI fmri MEGMagnetoencephalography NIRSnear-infrared spectroscopic imaging EEGElectroencephalogram ATR 400 MEG NIRS EEG LTDLong Term Depression Kuroda, S., et al., J. Neurosci., 21, , 2001) LTD 18 CREST H18 SCOPE-S DB Computational Brain CBICORP

EEG 3.2.4. LTDLong Term Depression Kuroda, S., et al., J. Neurosci.

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10 DB DB National Geographic Discovery Channel TIME Rise of the Machines 4 1 ( ) fmri fmri MEG NIRS MEG fmri BMI IEEEThe Institute of Electrical and Electronics Engineers, Inc.Humanoid Robots International Conference 2001 OIST Bayesian Brain 2006 Computing Neurons6/267/7 ERATO fmri Stem Cell() 7 9

Machines 4 1 (1999 11 17 ) fmri fmri MEG NIRS MEG fmri BMI 1 4.1.3.

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12 H18 SCOPE-S (4) 1980 ATR 20Kg 2Kg ERATO ATR ICORP ATR BMI fmri MEGMagnetoencephalography NIRSnear-infrared spectroscopic imaging MEG fmri MEG ATR 400 MEGMagnetoencephalography 11

13 BMIBrain Machine Interface ATR HR ATR fmrifunctional Magnetic Resonance Imaging MRI BMIBrain Machine Interface No. Vol/No/P Schaal, S., et al. Wolpert, DM., et al. Wolpert, DM., et al. Schweighofe r, N., et al. Schweighofe r, N., et al. Schaal, S. Doya K. Constructive Incremental Learning from Only Local Information Neural Computation / / Multiple paired forward and inverse models Neural Networks /7/ for motor control 329 Internal models in the cerebellum Trends in /9/ Cognitive Sciences Role of the cerebellum in reaching movements Eur. J /1/86-94 in humans.. Distributed inverse dynamics Neuroscience control Role of the cerebellum in reaching movements Eur. J /1/ in humans.. A neural model of the Neuroscience intermediate cerebellum Is imitation learning the route to humanoid Trends in /6/ robots? Cognitive Sciences 2 What are the computations of the cerebellum, Neural Networks / the basal ganglia, and the cerebral cortex? / Kawato M. Internal models for motor control and Current Opinion / / trajectory planning in Neurobiology Imamizu H., Human Cerebellar activity reflecting an Nature /6766/1 et al. acquired internal model of a new tool Doya K. Complementary roles of basal ganglia and Current Opinion /6/

Constructive Incremental Learning from Only Local Information Neural Computation 1998 10/ /2047-2084 Multiple paired forward and inverse models Neural Networks 1998 11/7/1317-1 for motor control 329

14 21 cerebellum in learning and motor control in Neurobiology 9 Burdet,E., The central nervous system stabilizes unstable Nature /6862/4 et al. dynamics by learning optimal impedance Haruno,M., MOSAIC model for sensorimotor learning and Neural /10/2201- et al. control Computation 2220 Servos P., The neural substrates of biological motion Cerebral Cortex /7/ et al., perception: An fmri study 2 Doya K. Metalearning and neuromodulation Neural Networks /4-5/ Wolpert DM., et al. A unifying computational framework for motor control and social interaction Imamizu H., Modular organization of internal models of et al. tools in the human cerebellum Haruno M., A neural correlate of reward-based behavioral et al., learning in caudate nucleus:a fmri study of a stochastic decision task Tananaka Prediction of immediate and future rewards SC., et al., differentially recruits cortico-basal ganglia loops Cairhness,G Failure to consolidate the consolidation theory., et al., of learning for sensorimotor adaptation task S. Schaal, et Rhythmic arm movement is not discrete al. Samejima Representation of action-specific reward K., et al., values in the striatum 2 Phil. Tras. R. Soc /1431/5 Lon. Ser.B Bio Sci. Proc. Nat. Aca /9/5461- Sci. USA 5466 J. Neuroscience /7/ Nature /8/ Neuroscience J. Neuroscience /40/ Nature /10/ Neuroscience 144 Science /5752/

, The neural substrates of biological motion Cerebral Cortex 2002 12/7/772-78 et al., perception: An fmri study 2 Doya K.

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19 ATR IEICE Fellow APNNA Outstanding Achievement Award Neuroimage AVIRG 18

IEICE Fellow 2005 2006 2007 APNNA Outstanding

20 ICONIP Best Paper Award Schaal, S IEEE Robotics and Automation Society Best Paper Award Neuroscience Research Excellent Paper Award

Best Paper Award 2002 16 2002 2007 2006 Neuroscience

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2015/9 Vol. J98 D No. 9 Shidara [7] t s t V (s t)=e[r t+1 + γr t+2 + γ 2 r t+3 + ] (1) r t t E γ 0 1 V (s t) TD V new(s t 1) V

2015/9 Vol. J98 D No. 9 Shidara [7] t s t V (s t)=e[r t+1 + γr t+2 + γ 2 r t+3 + ] (1) r t t E γ 0 1 V (s t) TD V new(s t 1) V a) b) Modeling the Function of the Ventral Striatum in Reinforcement Learning Based on the Analysis of Neuronal Activity Masanari SHINOTSUKA a), Masahiko MORITA b), and Munetaka SHIDARA TD striosome striosome