Kenji Doya

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Eiji UchibeDept. of Brain Robot Interface, ATR Computational Neuroscience Labs.Verified email at atr.jp
Mitsuo KawatoATRVerified email at atr.jp
Junichiro YoshimotoFujita Health UniversityVerified email at fujita-hu.ac.jp
Kazuyuki SamejimaProfessor, Brain science institute, Tamagawa UniversityVerified email at lab.tamagawa.ac.jp
Saori C. TanakaDivision of Information Science, NAIST / ATRVerified email at atr.jp
Stefan ElfwingSenior AI Research Scientist, Inify LaboratoriesVerified email at inify.com
Jun MorimotoKyoto University & ATR Computational Neuroscience LabsVerified email at atr.jp
Nicolas SchweighoferProfessor, University of Southern CaliforniaVerified email at usc.edu
Shin IshiiKyoto UniversityVerified email at i.kyoto-u.ac.jp
Hiroyuki NakaharaRIKEN Brain Science InstituteVerified email at brain.riken.jp
Raju S. BapiProfessor, Cognitive Science Lab, IIIT Hyderabad; (Formerly) Professor, SCIS, UoHVerified email at iiit.ac.in
Okihide HikosakaChief, LSR, NEI, NIHVerified email at lsr.nei.nih.gov
Krishna Prasad MiyapuramIndian Institute of Technology GandhinagarVerified email at iitgn.ac.in
Carlos GutierrezSoftBank Corp. / Okinawa Institute of Science and TechnologyVerified email at oist.jp
Genci CapiHosei UniversityVerified email at hosei.ac.jp
Daniel WolpertProfessor of Neuroscience, Columbia UniversityVerified email at columbia.edu
Kenji F. TanakaKeio University School of MedicineVerified email at keio.jp
Masahiko HarunoNICT CINET & Osaka UniversityVerified email at nict.go.jp
akihiro funamizuCold spring harbor laboratoryVerified email at cshl.edu

Kenji Doya

Okinawa Institute of Science and Technology

Verified email at oist.jp - Homepage

Computational Neuroscience Neural Networks Reinforcement Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Sigmoid-weighted linear units for neural network function approximation in reinforcement learning S Elfwing, E Uchibe, K Doya Neural networks 107, 3-11, 2018	1814	2018
A unifying computational framework for motor control and social interaction DM Wolpert, K Doya, M Kawato Philosophical Transactions of the Royal Society of London. Series B …, 2003	1630	2003
Reinforcement learning in continuous time and space K Doya Neural computation 12 (1), 219-245, 2000	1325	2000
Complementary roles of basal ganglia and cerebellum in learning and motor control K Doya Current opinion in neurobiology 10 (6), 732-739, 2000	1249	2000
Representation of action-specific reward values in the striatum K Samejima, Y Ueda, K Doya, M Kimura Science 310 (5752), 1337-1340, 2005	1150	2005
What are the computations of the cerebellum, the basal ganglia and the cerebral cortex? K Doya Neural networks 12 (7-8), 961-974, 1999	1062	1999
Prediction of immediate and future rewards differentially recruits cortico-basal ganglia loops SC Tanaka, K Doya, G Okada, K Ueda, Y Okamoto, S Yamawaki Behavioral economics of preferences, choices, and happiness, 593-616, 2016	1056	2016
Parallel neural networks for learning sequential procedures O Hikosaka, H Nakahara, MK Rand, K Sakai, X Lu, K Nakamura, ... Trends in neurosciences 22 (10), 464-471, 1999	1010	1999
Bayesian brain: Probabilistic approaches to neural coding K Doya MIT press, 2007	940	2007
Metalearning and neuromodulation K Doya Neural networks 15 (4-6), 495-506, 2002	869	2002
Modulators of decision making K Doya Nature neuroscience 11 (4), 410-416, 2008	843	2008
The computational neurobiology of learning and reward ND Daw, K Doya Current opinion in neurobiology 16 (2), 199-204, 2006	702	2006
Multiple model-based reinforcement learning K Doya, K Samejima, K Katagiri, M Kawato Neural computation 14 (6), 1347-1369, 2002	654	2002
Consensus paper: towards a systems-level view of cerebellar function: the interplay between cerebellum, basal ganglia, and cortex D Caligiore, G Pezzulo, G Baldassarre, AC Bostan, PL Strick, K Doya, ... The Cerebellum 16, 203-229, 2017	465	2017
Acquisition of stand-up behavior by a real robot using hierarchical reinforcement learning J Morimoto, K Doya Robotics and Autonomous Systems 36 (1), 37-51, 2001	387	2001
A neural correlate of reward-based behavioral learning in caudate nucleus: a functional magnetic resonance imaging study of a stochastic decision task M Haruno, T Kuroda, K Doya, K Toyama, M Kimura, K Samejima, ... Journal of Neuroscience 24 (7), 1660-1665, 2004	375	2004
Meta-learning in reinforcement learning N Schweighofer, K Doya Neural Networks 16 (1), 5-9, 2003	338	2003
Low-serotonin levels increase delayed reward discounting in humans N Schweighofer, M Bertin, K Shishida, Y Okamoto, SC Tanaka, ... Journal of Neuroscience 28 (17), 4528-4532, 2008	329	2008
Hierarchical Bayesian estimation for MEG inverse problem M Sato, T Yoshioka, S Kajihara, K Toyama, N Goda, K Doya, M Kawato NeuroImage 23 (3), 806-826, 2004	326	2004
Robust reinforcement learning J Morimoto, K Doya Neural computation 17 (2), 335-359, 2005	320	2005

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