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Publications of Computational Neurobiology Department
(Formerly Neuroinformatics Project, HIS)
English / Japanese
Journal Papers
2007
Callan, E. D., Tsytsarwv, V., Hanakawa, T., Callan, M. A., Katsuhatra M., Fukuyama, H., Turner, R. (in press). Song and speech: Brain regions involved with preception and covert production. NeuroImage.
Elfwing, S., Doya, K., Christensen, H. I. (2007). Evolutionary development of hierarchical learning structures. IEEE Transactions on Evolutionary Computations, 11(2), 249-264.[PDF]
Imamizu, H., Sugimoto, N., Osu, R., Tsutsui, K., Sugiyama, K., Wada, Y., Kawato, M. (in press). Explicit contextual information selectively contributes to predictive switching of internal models. Experimental Brain Research.
Ogasawara, H., Doi, T., Doya, K., Kawato, M. (2007). Nitric oxide regulates input specificity of long-term depression and context dependence of cerebellar learning. PLoS Computational Biology, 3(1), e179.[PDF]
Schweighofer,N., Tanaka, S. C., Doya, K. (2007). Serotonin and the evaluation of future rewards: Theory, experiments, and possible neural mechanisms. Annals of the New York Academy of Sciences, 14, 289-300.[PDF]
Tanaka, K., Khiroung, L., Santamaria, F., Doi, T., Ogasawara, H., Ellis-Davies, C.R., G., Kawato, M., Augustine, J. G. (2007). Ca2+ requirements for cerebellar long-term synaptic depression: role for a postsynaptic leaky integrator. Neuron, 54, 787-800.[PDF]
Wong, K., F., K., Galka, A., Yamashita, O., Ozaki, T. (2007). Modelling non-stationary variance in EEG time series by state space GARCH model. Computers in Biology and Medicine, 36, Issue12, 1327-1335.
2006
Bando, T., Shibata, T., Doya, K., Ishii, S. (2006). Switching particle filters for efficient visual tracking. Robotics and Autonomous Systems, 54, 873-884.[PDF]
Bapi, R. S., Miyapuram, K. P., Graydon, F. X., Doya, K. (2006). fMRI investigation of cortical and subcortical networks in the learning of abstract and effector-specific representations of motor sequences. Neuroimage. 32, 714-727. (Neuroimage Editor’s Choice Award, 2006)[PDF]
Daw, N. D. Doya, K. (2006). The computational neurobiology of learning and reward. Current Opinion in Neurobiology, 16, 199-204.[PDF]
Haruno, M., Kawato, M. (2006). Heterarchical reinforcement-learning model for integration of multiple cortico-striatal loops; fMRI examination in stimulus-action-reward association learning. Neural Networks, 19(8), 1242-1254.
Kawawaki, D., Shibata, T., Goda, N., Doya, K., Kawato, M. (2006). Anterior and superior lateral occipito-temporal cortex responsible for target motion prediction during overt and covert visual pursuit. Neuroscience Research. 54, 112-123. [PDF]
2006 Neuroscience Research Excellent Paper Award
Matsubara, T., Morimoto, J., Nakanishi, J., Sato, M., Doya, K. (2006). Learning CPG-based biped locomotion with a policy gradient method. Robotics and Autonomous Systems, 54,911-920.
Morimoto, J., Doya, K. (in press). Reinforcement learning state estimator. Neural Computation.
Ogasawara, H., Doi, T., Doya, K., Kawato, M. (2006). Nitric oxide regulates input specificity of long-term depression and context dependence of cerebellar learning. PLoS Computational Biology, 3(1), e179. [PDF]
Schweighofer, N., Shishida, K., Cheol, H. E., Okamoto, Y., Tanaka, S., Yamawaki, S., Doya, K. (2006). Humans can adopt optimal discounting strategy under real-time constraints. PLoS Computational Biology, 2(11), e152, 1349-1356. [PDF]
Tanaka, S. C., Samejima, K., Okada, G., Ueda, K., Okamoto, Y., Yamawaki, S., Doya, K. (2006). Brain mechanism of reward prediction under predictable and unpredictable environmental dynamics. Neural Networks. 19, 1233-1241.
2005
Callan, M. A., Callan, E. D., Masaki, S. (2005). When meaningless symbols become letters: Neural activity change in learning new phonograms. NeuroImage, 28, 553-562.[PDF]
Capi, G., Doya, K. (2005). Evolution of neural architecture fitting environmental dynamics. Adaptive Behavior, 13 (1), 53-66.[PDF]
Capi, G., Doya, K. (2005). Evolution of recurrent neural controllers using an extended parallel genetic algorithm. Robotics and Autonomous Systems, 52, 148-159. [PDF]
Doi, T., Kuroda, S., Michikawa, T., Kawato, M. (2005). Inositol 1,4,5-trisphosphate-dependent Ca2+ threshold dynamics detect spike timing in cerebellar Purkinje cells. Journal of Neuroscience, 25 (4), 950-961. [PDF]
Doya, K., Uchibe, E. (2005). The Cyber Rodent project: Exploration of adaptive mechanisms for self-preservation and self-reproduction. Adaptive Behavior, 13 (2), 149-160.
Morimoto, J., Doya, K. (2005). Robust reinforcement learning. Neural Computation, 17, 335-359.
Samejima, K., Ueda, Y., Doya, K., Kimura, M. (2005). Representation of action-specific reward value in the striatum. Science, 310, 1337-1340.
Shibata, T., Tabata, H., Schaal, S., Kawato, M. (2005). A model of smooth pursuit in primates based on learning the target dynamics. Neural Networks, 18 (3), 213-224. [PDF]
Yamagishi, N., Goda, N., Callan, E. D., Anderson, J. S., Kawato, M. (2005). Attentional shifts towards an expected visual target alter the level of alpha-band oscillatory activity in the human calcarine cortex. Cognitive Brain Research, 25, 799-809.
Yamashita, O., Sadato, N., Okada, T., Ozaki, T. (2005). Evaluationg frequency-wise directed connectivity of BOLD signals applying relative power contribution with the linear multivariate time series models. NeuroImage, 25, 478-490. [PDF]
2004
Haruno, M., Kuroda, T., Doya, K., Toyama, K., Kimura, M., Samejima, K., Imamizu, H., Kawato, M. (2004). A neural correlate of reinforcement-based behavioral learning in caudate nucleus: An fMRI study of a stochastic decision task. Journal of Neuroscience, 24 (7), 1660-1665. [PDF]
Miyamoto, H., Morimoto, J., Doya, K., Kawato, M. (2004). Reinforcement learning with via-point representation. Neural Networks, 17, 299-305. [PDF]Sato, M., Yoshioka, T., Kajiwara, S., Toyama, K., Goda, N., Doya, K., Kawato, M. (2004). Hierarchical bayesian estimation for MEG inverse problem. NeuroImage, 23, 806-826. [PDF]
Schweighofer, N., Doya, K., Kuroda, S. (2004). Cerebellar aminergic neuromodulation: Towards a functional understanding. Brain Research Reviews, 44, 103-116. [PDF]
Schweighofer, N., Doya, K., Fukai, H., Vianney, J., Furukawa, T., Kawato, M. (2004). Chaos may enhance information transmission in the inferior olive. Proceedings of the National Academy of Sciences, USA, 101 (13), 4655-4660. [PDF]
Tanaka, S. C., Doya, K., Okada, G., Ueda, K., Okamoto, Y., Yamawaki, S. (2004). Prediction of immediate and future rewards differentially recruits cortico-basal ganglia loops. Nature Neuroscience, 7 (8), 887-893. [doi:10.1038/nn1279] [PDF] [PDF-S]
2003
Samejima K., Doya K., Kawato M. (2003). Inter-module credit assignment in modular reinforcement learning. Neural Networks, 16, 985- 994. [PDF]
Schweighofer N., Doya K. (2003). Meta-learning of reinforcement learning. Neural Networks, 16, 5-9. [PDF]
2002
Doya, K. (2002). Metalearning and neuromodulation. Neural Networks, 15, 495-506. [PDF]
Doya, K., Dayan,P, Hasselmo M. E. (2002). Computational models of neuromodulation. Neural Networks , 15, 475-477.
Doya, K., Samejima, K., Katagiri, K., Kawato, M. (2002). Multiple model-based reinforcement learning. Neural Computation, 14(6), 1347-1369. [PDF]
2001
Doya K., Kimura H., Miyamura A. (2001). Motor control: Neural models and system theory. International Journal of Applied Mathematics and Computer Science, 11, 101-128. [PDF]
Doya, K., Kimura, H., Kawato, M. (2001). Neural mechanisms of learning and control. IEEE Control Systems Magazine, 21(4), 42-54.
Kuroda, S., Yamamoto, K., Miyamoto, H., Doya, K., Kawato, M. (2001). Statistical characteristics of climbing fiber spikes necessary for efficient cerebellar learning. Biological Cybernetics, 84(3), 183-192. [PDF]
Morimoto, J., Doya, K. (2001). Acquisition of stand-up behavior by a real robot using hierarchical reinforcement learning. Robotics and Autonomous Systems, 36, 37-51.
Morimoto, J., Doya, K. (2001). Robust reinforcement learning. V.Tresp et al.(eds.) Advances in Neural Information Processing Systems 13. MIT Press, 1061-1067. [PDF]
Nakahara, H., Doya, K., Hikosaka, O. (2001). Parallel cortico-basal ganglia mechanisms for acquisition and execution of visuo-motor sequences - A computational approach. Journal of Cognitive Neuroscience, 13(5), 626-647. [PDF]
Sato, M. (2001). On-line model selection based on the variational Bayes. Neural Computation, 13(7), 1649-1681.
Schweighofer N., Doya K., Lay F. (2001). Unsupervised learning of granule cell sparse codes enhances cerebellar adaptive control. Neuroscience, 103, 35-50. [PDF]
Yoshimoto, J., Ishii, S., Sato, M. (2001). Application of reinforcement learning based on on-line EM algorithm to balancing of acrobot. Systems and Computers in Japan, 32(5), 12-20.
2000
Bapi R.S., Doya K., Harner A.M. (2000). Evidence for effector independent and dependent representations and their differential time course of acquisition during motor sequence learning. Experimental Brain Research, 132, 149-62.
Doya, K. (2000). Metalearning, neuromodulation, and emotion. G. Hatanao, et al. (eds) Affective Minds, Elsevier Science, B.V., 101-104. [PDF]
Doya, K. (2000). Complementary roles of basal ganglia and cerebellum in learning and motor control. Current Opinion in Neurobiology, 10(6), 732-739. [PDF]
Doya K. (2000). Reinforcement learning in continuous time and space. Neural Computation, 12, 219-245. [PDF]
Doya, K. (2000). A possible role of serotonin in regulating the time scale of reward prediction. Serotonin: From the Molecule to the Clinic, 89.
1999
Doya K. (1999). What are the computations of the cerebellum, the basal ganglia, and the cerebral cortex. Neural Networks, 12, 961-974. [PDF]
Doya K., Sejnowski T.J. (1999). A computational model of avian song learning. Gazzaniga MS, The New Cognitive Neurosciences, MIT Press, 469-482.
Hikosaka O., Nakahara H., Rand M.K., Sakai K., Lu X., Nakamura K., Miyachi S., Doya K. (1999). Parallel neural networks for learning sequential procedures. Trends in Neurosciences, 22, 464-471. [PDF]
Morimoto J., Doya K. (1999). Hierarchical reinforcement learning for motion learning: learning "stand-up" trajectories. Advanced Robotics, 13, 267-268.
Schweighofer N., Doya K., Kawato M. (1999). Electrophysiological properties of inferior olive neurons: A compartmental model. Journal of Neurophysiology, 82, 804-817. [PDF]
Books and Reports
2006
Krishna P. Miyapuram, Raju S. Bapi, Kenji Doya (2006). Chunking Patterns Reflect Effector-dependent Representation of Motor Sequence. In Proceedings The 28th Annual Conference of the Cognitive Science Society, 1835-1837.
2005
Bissmarck, F., Nakahara, H., Doya, K., Hikosaka, O. (2005). Responding to modalities with different latencies. Advances in Neural Information Processing Systems, MIT Press. [PDF]
Bissmarck, F., Franklin, D., Doya, K. (2005). Selective saccades in sequential hand movements. IEICE Technical Report, 105(34), 1-5.
Fujiwara, Y., Sato, M., Yamashita, O., Yoshioka, T., Kawawaki, D., Shibata, T., Doya, K., Toyama, K., Kawato, M. (2005). A method for removal of eye movement artifacts from MEG - Simultaneous current source estimation of eyes and cortical activities from MEG and EOG data. IEICE Technical Report, 43-48
2004
Bissmarck, F., Nakahara, H., Doya, K., Hikosaka, O. (2004). Efficient learning of real-time motor skills by parallel policies. IEICE Technical Report, 104(140), 23-28.
Samejima K., Doya K., Ueda K., Kimura M. (2004). Estimating internal variables and parameters of a learning agent by a particle filter. Advances in Neural Information Processing Systems16, 1335-1342, MIT Press. [PDF]
Tanaka S., Doya K., Okada G., Ueda K., Okamoto Y., Yamawaki S. (2004). Different cortico-basal ganglia loops specialize in reward prediction on different time scales. Advances in Neural Information Processing Systems16, 701-708, MIT Press. [PDF]
Wolpert, M. D., Doya, K, Kawato,M. (2004). A unifying computational framework for motor control and social interaction. In Frith C, Wolpert DM (Eds.) The Neuroscience of Social Interaction. Oxford University Press, Oxford, UK, 305-322.
2003
Bissmarck, F., Nakahara, H., Doya, K., Hikosaka, O. (2003). Parallel network mechanisms for motor sequence acquisition in real time. IEICE Technical Report, 102, 731, 113-118.
2002
Doya, K. (2002). Recurrent neural networks: Supervised Learning. Arbib M, The Handbook of Brain Theory and Neural Networs, Second Edition. [PDF]
2000
Doya, K., Katagiri, K., Wolpert, D. M., Kawato, M. (2000). Recognition and imitation of movement paterns by a multiple predictor-controller architecture. IEICE Technical Report, TL2000(11), 33-40.
Doya, K., Samejima, K., Katagiri, K., Kawato, M. (2000). Multiple model-based reinforcement learning. Kawato Dynamic Brain Project Technical Report, KDB-TR-08, 1-20. [PDF]
Kuroda S., Yamamoto K., Miyamoto H., Doya K., Kawato M. (2000). Statistical characteristics of climbing fiber spikes necessary for efficient cerebellar learning. Kawato Dynamic Brain Project.
Morimoto, J., Doya, K. (2000). Robust reinforcement learning. IEICE Technical Report, NC2000-49, 59-66.
Conference Presentations
2007
Uchibe, E., Doya, K. (2007). Constrained reinforcement learning from intrinsic and extrinsic rewards. 6th EEE International Conference on Development and Learning (ICDL2007).[PDF]
2006
Callan, E. D., Yamashita, O., Tajima, K., Kawato, M. (2006). Classification of single-trial phonetic identification performance using pre-stimulus EEG activity. The organization for human brain mapping, 12th annual meeting (HBM06).
Doya, K. (2006). Reinforcement Learning and the Basal Ganglia. 2006 Japan-Germany Symposium on Computational Neuroscience.
Doya, K. (2006). Short- and long-term reward prediction in cortico-basal ganglia loops . Computational and systems neuroscience 2006 (Cosyne 2006) Workshops.
Haruno, M., Gowrishankar, G., Kawato, M. (2006). Differential neural correlates of force control and muscle co-contraction control revealed by fMRI with on-line EMG feedback. Society for Neuroscience 36th Annual Meeting (Neuroscience 2006).
Hubbard, A., Callan, E. D., Dapretto, M. (2006). How the brain sees what we say: A functional MRI study of speech and beat gesture. The organization for human brain mapping, 12th annual meeting (HBM06).Hubbard, A., Callan, E. D., Dapretto, M. (2006). How the brain sees what we say: A functional MRI study of speech and beat gesture. Joint conference of American association of applied linguistics with Canadian association of applied linguistics.
Miyapuram, K.P., Bapi, R. S., Doya, K. (2006). Chunking patterns reflect effector-dependent representation of motor sequence. The 28th Annual Conference of the Cognitive Science Society, 1835-1837.
Miyapuram, K.P., Bapi, R. S., Pammi, V. S. C. , Doya, K. (2006). Hierarchical chunking during learning of visuomotor sequences. IEEE World Congress on Computational Intelligence .
Miyapuram, K.P., Bapi, R. S., Pammi, V. S. C. , Doya, K. (2004). Chunking phenomenon in complex sequential skill learning in humans. The 11th International Conference on Neural Information Processing (ICONIP 2004), 294-299.
2005
Bando, T., Shibata, T., Doya, K., Ishii, S. (2005). Hard / soft switching particle filters for efficient real-time visual tracking. International Symposium on Artificial Life and Robotics 2005.
Callan, E. D. (2005). Internal models differentially implicated in audiovisual perception of nonnative vowel contrasts. Auditory-Visual Speech Processing 2005 (AVSP 2005), 53-54.
Callan, E. D. (2005). Perceptual identification of difficult second-language phonetic contrasts selectively activates brain regions involved with auditory-articualtory and orosensory mapping. Acoustical Society of Japan 2005 Autumn Meeting, 883-884.
Doi, T., Kuroda, S., Michikawa, S., Doya, K., Kawato, M. (2005). Spontaneous activity of parallel fibers autoregulates the amount of AMPA receptors to elicit cerebellar LTD for supervised learning. Society for Neuroscience 35th Annual Meeting.
Doya K. (2005). Encoding of future rewards in the striatum. Cold Spring Harbor Workshop on Neural Information and Coding, Mangalore, India.
Doya K. (2005). Reward prediction in the striatum and its modulation by serotonin. Seminar at Sloan-Swartz Center for Theoretical Neurobiology, Brandeis University. Waltham, USA.
Doya, K. (2005). Mechanisms and origins of reward-based behaviors: Neurobiological and robotic approaches . Brain-Inspired Information Technology2005 (BrainIT2005).
Hitomi, K., Shibata, T., Nakamura,Y., Ishi, S. (2005). Reinforcement learning of stable trajectory for quasi-passive-dynamic walking. IJCAI Workshop on Modeling Natural Action Selection, 229-234.
Matsubara, T., Morimoto, J., Nakanishi, J., Sato, M., Doya, K. (2005). Learning sensory feedback to CPG with policy gradient for biped locomotion. IEEE International Conference on Robotics and Automation (ICRA2005).
Morimura, T., Doya, K. (2005). Utilizing the natural gradient in temporal difference reinforcement learning with eligibility traces. 2nd International Symposium on Information Geometry and its Application, 256-263.
Samejima, K., Ueda, Y., Doya, K., Kimura, M. (2005). A reinforcement learning model predicts monkey's choice and dorsal striatal activities. Society for Neuroscience 35th Annual Meeting
Schweighofer,N., Shishida, K., Okamoto, Y., Tanaka, S., Yamawaki, S., Doya, K. (2005). Reward value is exponentially discounted at short - time scales and modulated by serotonin in humans. Society for Neuroscience 35th Annual Meeting.
Suzuki, H., Schweighofer, N., Hirata, Y., Fujiwara, K., Katori, Y., Shimokawa, H., Aihara, K., Kawato, M. (2005). Can electrical coupling induce chaos in inferior olive neurons? Experimental evidence. Society for Neuroscience 35th Annual Meeting.
Tanaka, S., Shishida, K., Schweighofer, N., Okamoto, Y., Yamawaki, S., Doya, K. (2005). Serotonin affects temporal credit assignment in delayedstimulus-outcome association learning. Society for Neuroscience 35th Annual Meeting.
Ueda, Y., Samejima, K., Doya, K., Kimura, M. (2005). Distinct groups of striate neurons encode action value, action choice, and reinforcement during free - choice task. Society for Neuroscience 35th Annual Meeting.
Uchibe, E., Doya, K. (2005). Reinforcement learning with multiple heterogeneous modules. The4th International Conference on Development and Learning (ICDL4).
2004Bando, T., Shibata, T., Doya, K., Ishii, S. (2004). Switching particle filters for efficient real-time visual tracking. International Conference on Pattern Recognition 2004. [PDF]
Bissmarck, F., Nakahara, H., Doya, K., Hikosaka, O. (2004). Learning and control of real-time procedural movement. 31st NIPS International Symposium, Multidisciplinary Approaches to Sensorimotor Integration, Old Questions Meet New Concepts.
Doya, K. (2004). How the basal ganglia realize reinforcement learning. Tamagawa－COE International Symposium on Attention and Decision, 70.
Doya K. (2004). Molecular and network mechanisms of reinforcement learning. Annual Meeting of Japanese Physiology Society. Sapporo, Japan.
Doya, K. (2004). Prediction of rewards at different time scales in the basal ganglia. Computational and Neural Systems,California Institute of Technology.
Doya, K. (2004). Cyber rodents: Exploration of adaptive mechanisms for self-preservation and self-reproduction. Neurorobotic models in Neuroscience and Neuroinformatics.
Doya, K. (2004). Metalearning and neuromodulation. 2004 Telluride Workshop on Neuromorphic Engineering.
Doya, K. (2004). Metalearning, neuromodulation and emotion. Laboratory talk (Salk institute).
Doya, K. (2004). Metalearning, neuromodulation and emotion. Laboratory talk (Sony CSL Paris).
Doya K. (2004). Parallel networks for reward prediction at different time scales. The Mechanism of Brain and Mind Workshop. Yuzawa, Japan.
Doya K. (2004). Designing the reward system. 14th Annual Meeting, Japanese Neural Network Society.Kyoto, Japan.
Doya K. (2004). Reward prediction in the striatum and its modulation by serotonin. NIPS 2004 Workshop on Reinforcement Learning and the Brain: Beyond the Dopamine System. Whistler, Canada.
Doya, K. (2004). Competition and cooperation of multiple learning modules. 31st NIPS International Symposium, Multidisciplinary Approaches to Sensorimotor Integration, Old Questions Meet New Concepts.
Elfwing S., Uchibe E., Doya K., Christensen H. I. (2004). Multi-Agent Reinforcement Learning: Using Macro Actions to Learn a Mating Task. IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, Japan.
Haruno, M., Kuroda, T., Doya, K., Toyama, K., Kimura, M., Samejima, K., Imamizu, H., Kawato, M. (2004). Computationall-model-based imaging studies of decision learning. Tamagawa-COE International Symposium on Attention and Decision.
Ito M., Doya K., Shirao T., Sekino Y. (2004). Ibotenic acid lesions of the supramammillary nucleus decreased c-Fos expression in the hippocampus of rats exploring in an open field. Annual Meeting of Japanese Physiology Society, Sapporo, Japan.
Ito, M., Doya, K., Shirao, T., Sekino, Y. (2004). Fos imaging reveals that the supramammillary nucleus enhances hippocampal activity of rats placed in a novel open field. Society for Neuroscience 34th Annual Meeting, 96.
Kawato, M., Doya, K., Wolpert, D. (2004). MOAIC; Experimental supports and cognitive implications. International Workshop on "Neural, Computational and Cognitive Mecamisms of Mentalizing.
Kawawaki, D., Shibata, T., Goda, N., Doya, K., Kawato, M. (2004). Anterior and superior lateral occipito-temporal cortex responsible for target motion prediction during overt and covert visual pursuit. Society for Neuroscience 34th Annual Meeting, 64.
Klein, M., Kamp, H., Palm, G., Doya, K. (2004). Expressing and understanding desires in language games. Ninth International Conference on the Simulation and Synthesis of Living Systems (ALIFE9), 170-175.Klein, M., Doya, K. (2004). Learning to predict the effects of complex utterances. Ninth Neural Computation and Psychology Workshop, modelling language, cognition and action, 41.
Klein, M., Doya, K. (2004). How the brain expresses and understands intentions. 1st International Workshop: "Exploring Social Brain", 21st Century COE Program.
Mori T., Nakamura,Y., Sato, M., Ishii, S. (2004). Reinforcement learning for CPG-driven biped robot. The Nineteenth National Conference on Artificial Intelligence（AAAI2004）.
Morimura T., Matsuyama K., Hayashi T., Samejima K., Doya K. (2004). Validation of decision making models by sequential Monte Carlo method applied to human decision data. The Mechanism of Brain and Mind Workshop, Yuzawa, Japan.
Sato, M., Yoshioka, T., Kajiwara, S., Toyama, K., Goda, N., Doya, K., Kawato, M. (2004). Hierarchical variational Bayesian method for MEG. BIOMAG2004, 609.
Schweighofer,N., Tanaka, S., Asahi, S., Okamoto, Y., Doya, K., Yamawaki, S. (2004). An fMRI study of the delay discounting of reward after tryptophan depletion and loading. 1: decision-making. Society for Neuroscience 34th Annual Meeting, 97.
Shibata, T., Suhara, Y., Oga, T., Ueki, Y., Mima, T., Ishii, S. (2004). Application of multivariate autoregressive modeling for analyzing the interaction between EEG and EMG in humans. International Congress Series, 1270C(3), 249-253.
Sugimoto, N, Doya, K., Kawato, M. (2004). Cooperation by estimating other's internal state. Ninth Neural Computation and Psychology Workshop, modelling language, cognition and action.
Tanaka, S., Schweighofer,N., Asahi, S., Okamoto, Y., Yamawaki, S., Doya, K. (2004). An fMRI study of the delay discounting of reward after tryptophan depletion and loading. 2: reward expectation. Society for Neuroscience 34th Annual Meeting, 98.
Uchibe, E., Doya, K. (2004). Competitive-cooperative-concurrent reinforcement learning with importance sampling. The Eighth International Conference on the Simulation of Adaptive Behavior, 287-296.
Uchibe, E., Capi, G., Elfwing, S., Eriksson, A., Suzuyama, H, Doya, K. (2004). Cyber rodent project. Symposium: Towards Artificial Rodents.
Uchibe E., Doya K. (2004). Selection from multiple reward functions for cooperative-competitive-concurrent reinforcement learning. Towards Artificial Rodents. Laboratoire d'Informatique de Paris 6. Paris, France.
Yoshioka, T., Sato, M., Kajiwara, S., Toyama, K., Goda, N., Doya, K., Kawato, M. (2004). An analysis of MEG data by hierarchical variational Bayesian method. BIOMAG2004, 611.
2003Capi G., Doya K. (2003). Evolving recurrent neural controllers for sequential tasks - a parallel implementation. Congress on Evolutionary Computation, 1, 514-519.
Capi G., Uchibe E., Doya K. (2003). Selection of neural architecture and the environment complexity. Dynamic Systems Approach for Embodiment and Sociality From Ecological Psychology to Robotics, 6, 311-317. Advanced Knowledge International.
Daniel M Wolpert, D. M., Doya, K., Kawato, M. (2003). A unifying computational framework for motor control and social interaction.. Philosophical Transactions of the Royal Society, 358, 593-602.
Doya K. (2003). A computational theory of neuromodulation. International Symposium "New Horizons in Molecular Sciences and Systems: An Integrated Approach, " 50.
Doya K. (2003). Enjoy now or strive for future: Neural mechanisms of reward prediction at different time scales. Summer Program 2003 Progarm and Abstracts, 36.
Doya K. (2003). Cyber Rodents: Self-preserving, self-reproducing robotic colony. ATR Up to Date, Summer 2003, 12-13.
Doya K., Sugimoto N., Wolpert D.M., Kawato M. (2003). Selecting optimal behaviors based on contexts. International Symposium on Emergent Mechanisms of Communication, Awaji, 19-23. [PDF]
Elfwing S., Uchibe E., Doya K. (2003). An evolutionary approach to automatic construction of the structure in hierarchical reinforcement learning. Genetic and Evolutionary Computation - GECCO 2003 Proceedings, Part 1, Chicago, IL, Springer, GECCO 2003, LNCS 2723, 507-509. [PDF]
Eriksson A., Capi G., Doya K. (2003). Evolution of meta-parameters in reinforcement learning algorithm. IEEE/RSJ IROS. [PDF]Oba, S., Sato, M., Ishii, S. (2003) Prior Hyperparameters in Bayesian PCA. ICANN/ICONIP 2003, 123-131.
Samejima K., Ueda Y., Doya K., Kimura M. (2003). Activity of striate projection neurons encodes action-selective reward expectations. Society for Neuroscience 33rd Annual Meeting, 78.
Tanaka S., Doya K., Okada G., Ueda K., Okamoto Y., Yamawaki S. (2003). Prediction of immediate and future rewards differentially recruits cortico-basal ganglia loops. Society for Neuroscience 33rd Annual Meeting, 58.
Ueda Y., Samejima K., Doya K., Kimura M. (2003). Reward value dependent striate neuron activity of monkey performing trial and error behavioral decision task. Neuroscience Research, Vol. 46 Suppl. 1 S1-S220, S50.
Wolpert D.M., Doya K., Kawato M. (2003). A unifying computational framework for motor control and social interaction. Philosophical Transactions of the Royal Society, 358, 593-602.Yoshimoto, J., Ishii, S., Sato, M. (2003) System identification based on on-line variational Bayes method and its application to reinforcement learning, Artificial Neural Networks and Neural Information Processing ICANN/ICONIP 2003, Lecture Notes in Computer Science 2714, 123-131, Springer Verlag. [PDF]
2002
Asahi, S., Okamoto, Y., Okada, G., Morinobu, S., Yamawaki, S., Doya, K. (2002). Relationship between brain activation during GO/NOGO task and impulsiveness: A fMRI study 32nd Annual Meeting, Society for Neuroscience.
Capi G., Uchibe E., Doya K. (2002). Selection of neural architecture and the environment complexity. The 3rd Inernational Symposium on Human and Artificial Intelligence Systems: Dynamic Systems Approach for Embodiment and Socialit, Fukui, Japan, 231-237. [PDF]
Nakamura,Y., Sato, M., Ishii,S. (2002). Reinforcement Learning for Biped Robot. 2nd International Symposium on Adaptive Motion of Animals and Machines (AMAM2003), 36.Okada, G.,Okamoto,Y., Ueda, K., Morinobu, S., Yamawaki, S.,, Doya, K.(2002). Selection between small, immediate rewards and large, delayed rewards in prediction of future reward: A functional magnetic resonance imaging study. 8th International Conference on Functional Mapping of the Human Brain, 968.
Oba,S., Sato,M., Takemasa,I., Monden,M., Matsubara,K., Ishii,S. (2002). Missing value estimation using mixture of PCAs. ICANN 2002, 492-497.
Sato, M., Nakamura,Y., Ishii,S. (2002). Reinforcement Learning for Biped Locomotion. ICANN 2002, 777-782.
Sato, M., Oba,S. (2002). Incremental Sparse Kernel Machine. ICANN 2002, 700-706.Schweighofer, N., Doya, K. (2002). A biologically plausible computational model of meta-learning in reinforcement learning, Society for Neuroscience 32nd Annual Meeting.
Tanaka, S., Doya, K., Okada, G., Ueda, Y., Okamoto, Y., Yamawaki, S. (2002). Functional MRI study of short-term and long-term prediction of reward. 8th International Conference on Functional Mapping of the Human Brain, 1062.
Ueda, Y., Samejima, K., Doya, K., Kimura, M. (2002). Reward value-dependent striate neuron activity of monkey performing trial-and-error behavioral decision task. Society for Neuroscience 32nd Annual Meeting.
Yoshimoto, J., Ishii, S., Sato, M. (2002) Hierarchical model selection for NGnet based on variational method. International Conference on Artificial Neural Networks 2002 (ICANN 2002) , Lecture Notes in Computer Science 2415, 661-666, Springer Verlag. [PDF]
2001
Bapi, R. S., Doya, K. (2001). Multiple forward model architecture for sequence Processing. R. Sun and C. L. Giles (eds) Sequence Learning, Springer, 309-320.
Doya K. (2001). Metalearning and neuromodulation. CREST Workshop on Metalearning and Neuromodulation, Seika, Kyoto, 6.
Doya, K. (2001). Robotic neuroscience: A synthetic approach to the brain. Neuroscience Research Supplement, 24(1), S16.
Doya, K. (2001). Regulatory roles of serotonin and norepinephrine in reinforcement learning. 9th International Catecholamine Symposium, 36,S19-4.
Doya, K. (2001). Specialization of cerebellum, basal ganglia, and cerebral cortex in supervised, reinforcement, and unsupervised learning. Stockholm Workshop on Computational Neuroscience, 13.
Doya, K., Kimura, H., Kawato, M. (2001). Neural mechanisms of learning and control. IEEE Control Systems Magazine, 21(4), 42-54.
Doya K., Kimura H., Miyamura A. (2001). Motor control: Neural models and system theory. International Journal of Applied Mathematics and Computer Science, 11, 101-128.
Doya K., Okada G., Ueda K., Okamoto Y., Yamawaki S. (2001). Prediction of short- and long-term reward: A functional MRI study with a Markov decision problem. Society for Neuroscience 31th Annual Meeting, San Diego, USA.
Doya K., Samejima K., Katagiri K., Kawato M. (2001). Task decomposition and imitation by MOSAIC architecture. HFSP Arundel Meeting / Wolpert Group, Arundel, Canada.Haruno M., Kuroda T., Doya K., Toyama K., Kimura M., Samejima K., Imamizu H., Kawato M. (2001). fMRI study of human brain activity during reinforcement learning. Society for Neuroscience 31th Annual Meeting, San Diego, USA.
Matsumoto, N., Okada, M., Doya, K., Sugase, Y., Yamane, S., Kawano, K (2001). Dynamics of the face-responsive neurons in the temporal cortex. Neuroscience Research Supplement, 24(1), S73.Miyapuram K.P., Bapi R.S., Samejima K., Doya K. (2001). fMRI investigation of the learning of visuo-motor sequences. Society for Neuroscience 31th Annual Meeting, San Diego, USA
Okada G., Okamoto Y., Ueda K., Yamashita H., Kagaya A., Morinobu S., Yamawaki S., Doya K. (2001). Localization of brain activity in prediction of future reward using fMRI and MEG. Society for Neuroscience 31th Annual Meeting, San Diego, USA.
Tabata H., Shibata T., Taguchi S., Doya K., Kawato M. (2001). A simulation study on smooth pursuit and ocular following responses based on an MST neural-field model. Society for Neuroscience 31th Annual Meeting, San Diego, USA.
2000
Bapi, R. S., Graydon, F. X.,Doya, K. (2000). Time course of learning of motor sequence representation. Society for Neuroscience 30th Annual Meeting, 26, 707.
Doya, K. (2000). Metalearning, neuromodulation and emotion. Humanoid Challenge, JST Inter-field Exchange Forum, 87-88.
Doya, K. (2000). Possible roles of neuromodulators in the regulation of learning processes. Society for Neuroscience 30th Annual Meeting, 26, 2103.
Kimura, H., Doya, K. (2000). Motor control: Neural models and system theory. 14th International Symposium on Mathematical Theory and Networks and Systems, 232.
Morimoto, J., Doya, K. (2000). Acquisition of stand-up behavior by a real robot using hierarchical reinforcement learning. 17th International Conference on Machine Learning, 1(1), 623-630.
Morimoto, J., Doya, K. (2000). Robust reinforcement learning. Neural Information Processing Systems 2000, 77.
Nagayuki Y., Ishii S., Doya K. (2000). Multi-agent reinforcement learning: an approach based on the other agent's internal model. Fourth International Conference on Multi-Agent Systems, 215-221.Nagayuki Y., Ishii S., Ito M., Shimohara K., Doya K. (2000). A multi-agent reinforcement learning method with the estimation of the other agent's actions. Fifth International Symposium on Artifical Life and Robotics, 1, 255-259. [PDF]
Ohba, S., Ishii, S., Sato, M. (2000). Variational Bayes method for Mixture of Principal Component Analyzers. 7th International Conference on Neural Information Processing (ICONIP-2000), 2, 1416-1421.
Samejima, K., Ueda, Y., Kimura, M., Doya, K., Schweighofer, N. (2000). Information coding of the striatal neurons during seqential movement. Society for Neuroscience 30th Annual Meeting, 26, 681.
Sato, M. (2000). On-line Bayesian Learning and Model Selection. 7th International Conference on Neural Information Processing, 1, 470-475.
Sato, M. (2000). Convergence of On-line EM Algorithm. 7th International Conference on Neural Information Processing, 1, 476-481.
1999
Bapi R.S., Doya K. (1999). MFM: Multiple forward model architecture for sequence processing. IJCAI'99 Workshop on Sequence Learning, Stockholm, Sweden.
Bapi R.S., Doya K., Harner A.M. (1999). Visual and motor representations for sequence learning. Japan Science and Technology Corporation.
Doya K. (1999). Multiple representation and algorithms for sequence learning. 2nd International Conference on Cognitive Science, Tokyo, 17-19. [PDF]
Doya K. (1999). Metalearning, neuromodulation and emotion. 13th Toyota Conference on Affective Minds, Mikkabi, Japan, 46-47. [PDF]
Koike Y., Doya K. (1999). Multi state estimation reinforcement learning for driving model. IEEE International Conference on System, Man and Cybernetics, Tokyo, V, 504-509.
Okada M., Toya K., Kimoto T., Doya K. (1999). Retrieval dynamics of associative memory model can explain temporal dynamics of face-responsive neurons in the IT cortex. Society for Neuroscience 29th Annual Meeting, Miami Beach, Florida, USA.