Kenji Doya's Publications by Research Topics

Metalearning, Neuromodulation and Emotion

• Doya, K. (1999). Metalearning, neuromodulation and emotion, The 13th Toyota Conference on Affective Minds, 46-47. [pdf]

Computations in Cerebellum, Basal Ganglia and Cerebral Cortex

• Doya, K. (1999). What are the computations in the cerebellum, the basal ganglia, and the cerebral cortex. Neural Networks, 12, 961-974. [ps]
• Doya, K. (1998). Integration of cortical, cerebellar and basal ganglionic modules specialized in unsupervised, supervised and reinforcement learning. International Basal Ganglia Society 6th Triennial Meeting, page 27. [text]
• Doya, K. (1997). How basal ganglia, cerebellum and cerebral motor areas work together in sequential control tasks. Neural Control of Movement, 7th Annual Meeting Abstracts, page 28. [text]
• Doya, K. (1996). An integrated model of basal ganglia and cerebellum in sequential control tasks. Society for Neuroscience Abstracts, volume 22, page 2029.

Reinforcenemt Learning Model of the Basal Ganglia

• Hikosaka, O., Nakahara, H., Rand, M. K., Sakai, K., Lu, X., Nakamura, K., Miyachi, S., and Doya, K. (1999). Parallel neural networks for learning sequential procedures. Trends in Neurosciences, 22, 464-471.
• Bapi, R. S., Doya, K., and Harner, A. M. (1999). Visual and motor representations for sequence learning. Kawato Dynamic Brain Project Technical Report, TR-03.
• Nakahara, H., Doya, K., and Hikosaka, O. (1998). Benefit of multiple representaitons for motor sequence control in the basal ganglia loops. Technical Report, Information Synthesis Group, RIKEN Brain Science Institute. [ps]
• Bapi, R. S. and Doya, K. (1999). MFM: Multiple forward model architecture for sequence processing. IJCAI'99 Workshop on Sequence Learning, Stockholm, Sweden.
• Bapi, R. S. and Doya, K. (1998). A sequence learning architecture based on cortico-basal ganglionic loops and reinforcement learning. The 5th International Conference on Neural Information Processing, volume 1, pages 260-263.
• Bapi, R. S. and Doya, K. (1998). Evidence for effector independent and dependent components in motor sequence learning. Society for Neuroscience Abstracts, volume 24, page 167.
• Nakahara, H., Doya, K., Hikosaka, O., and Nagano, S. (1997). Reinforcement learning with multiple representations in the basal ganglia loops for sequential motor control. International Joint Conference on Neural Networks, pages 1553-1558. [ps]
• Nakahara, H., Doya, K., and Hikosaka, O. (1998). Benefit of multiple representations in parallel cortico-basal ganglia mechanisms for acquisition and execution of visuo-motor sequences. International Basal Ganglia Society 6th Triennial Meeting, page 29.
• Nakahara, H., Doya, K., Hikosaka, O., and Nagano, S. (1997). Multiple representations in the basal ganglia loops for acquisition and execution of sequential motor control. Society for Neuroscience Abstracts, volume 23, page 778.

Reinforcement Learning in Continous Time and Space

• Doya, K. (1999). Reinforcement learning in continuous time and space. Neural Computation, 12, 243-269. [ps]
• Morimoto, J. and Doya, K. (1998). Reinforcement learning of dynamic motor sequence: Learning to stand up. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, volume 3, pages 1721-1726. [ps]
• Morimoto, J. and Doya, K. (1998). Hierarchical reinforcement learning of low-dimensional subgoals and high-dimensional trajectories. The 5th International Conference on Neural Information Processing, volume 2, pages 850-853. [ps]
• Doya, K. (1997). Efficient nonlinear control with actor-tutor architecture. M. C. Mozer, M. I. Jordan, T. P., editor, Advances in Neural Information Processing Systems 9, pages 1012-1018. MIT Press. [ps]
• Doya, K. (1996). Temporal difference learning in continuous time and space. Touretzky, D. S., Mozer, M. C., and Hasselmo, M. E., editors, Advances in Neural Information Processing Systems 8, pages 1073-1079. MIT Press. [ps]
• Doya, K. and Yoshizawa, S. (1992). Adaptive synchronization of neural and physical oscillators. Moody, J. E., Hanson, S. J., and Lippmann, R. P., editors, Advances in Neural Information Processing Systems 4, pages 109-116. Morgan Kaufmann. [ps]
• Doya, K. (1990). A robot that learns walking patters. Journal of the Robotic Society of Japan, 8:357. (in Japanese).

Song Learning in the Bird's Brain

• Doya, K., Sejnowski, T.J. (1999) A computational model of avian song learning. M. S. Gazzaniga, editor: The Cognitive Neurosciences, 2nd edition, MIT Press.
• Doya, K. and Sejnowski, T. J. (1998). A computational model of birdsong learning by auditory experience and auditory feedback. Brugge, J. and Poon, P., editors, Central Auditory Processing and Neural Modeling, pages 77-88. Plenum Publishing.
• Doya, K. and Sejnowski, T. J. (1995). A novel reinforcement model of birdsong vocalization learning. Tesauro, G., Touretzky, D. S., and Leen, T. K., editors, Advances in Neural Information Processing Systems 7, pages 101-108. MIT Press. [ps]
• Doya, K. and Sejnowski, T. J. (1994). A computational model of song learning in the anterior forebrain pathway of the birdsong control system. Society for Neuroscience Abstract, volume 20, page 166.

Single Neuron Dynamics: from STG to IO

• Schweighofer, N., Doya, K., and Kawato, M. (1999). Electrophysiological properties of inferior olive neurons: A compartmental model. Journal of Neurophysiology, 82, 804-817.
• Schweighofer, N., Doya, K., and Kawato, M. (1998). A model of the electrophysiological properties of the inferior olive neurons. The 5th International Conference on Neural Information Processing, volume 3, pages 1525-1528.
• Schweighofer, N., Doya, K., and Kawato, M. (1998). A model of the electrophysiological properties of the inferior olive neurons. Society for Neuroscience Abstracts, volume 24, page 667.
• Doya, K. and Selverston, A. I. (1994). Dimension reduction of biological neuron models by artificial neural networks. Neural Computation, 6:696-717.
• Doya, K., Selverston, A. I., and Rowat, P. F. (1994). A Hodgkin-Huxley type neuron model that learns slow non-spike oscillation. Cowan, J. D., Tesauro, G., and Alspector, J., editors, Advances in Neural Information Processing Systems 6, pages 566-573. Morgan Kaufmann. [ps]
• Doya, K. and Selverston, A. I. (1993). A learning algorithm for Hodgkin-Huxley type neuron models. Proceedings of 1993 International Joint Conference on Neural Networks, pages 1108-1111.

Dyanmics of Recurrent Neural Netowrks

• Okada, M., Toya, K., Kimoto, T., and Doya, K. (1999). Retrieval dynamics of associative memory model can explain temporal dynamics of face-responsive neurons in the IT cortex. 29th Annual Meeting Society for Neuroscience, Miami Beach, Florida, USA.
• Nakahara, H. and Doya, K. (1998). Near saddle-node bifurcation behavior as dynamics in working memory for goal-directed behavior. Neural Computation, 10:113-132.
• Nakahara, H. and Doya, K. (1996). Dynamics of attention as near saddle-node bifurcation behavior. Touretzky, D. S., Mozer, M. C., and Hasselmo, M. E., editors, Advances in Neural Information Processing Systems 8, pages 38-44. MIT Press.
• Doya, K. (1995). Recurrent networks: Supervised learning. Arbib, M., editor, The Handbook of Brain Theory and Neural Networks. MIT Press. 796-800.
• Doya, K. (1992). Bifurcations in the learning of recurrent neural networks. Proceedings of 1992 IEEE International Symposium on Circuits and Systems, pages 2777-2780. [ps]
• Doya, K. (1991). A study of learning algorithms for continuous-time recurrent neural networks. Ph.D. thesis, University of Tokyo.
• Doya, K. and Yoshizawa, S. (1991). Neural network model of temporal pattern memory. Systems and Computers in Japan, 22:61-69.
• Doya, K. and Yoshizawa, S. (1990). Memorizing hierarchical temporal patterns in analog neuron networks. Proceedings of 1990 International Joint Conference on Neural Networks, San Diego, pages III:299-304.
• Doya, K. (1990). Learning temporal patterns in recurrent neural networks. Proceedings of 1990 IEEE System, Man and Cybernetics Conference, pages 170-172.
• Doya, K. and Yoshizawa, S. (1989). Adaptive neural oscillator using continuous-time back-propagation learning. Neural Networks, 2:375-386.
• Doya, K. and Yoshizawa, S. (1989). Memorizing oscillatory patterns in the analog neuron network. Proceedings of 1989 International Joint Conference on Neural Networks, Washington, D.C., pages I:27-32.

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