Abstract
Serial order is an important aspect of human and animal behavior and continues to receive attention. In spite of research over many years, it is not yet completely understood how the brain solves the serial order of motor behavior problem (Rosenbaum 10). However, in recent years there has been rapid progress in the domain of sequence processsing both in the neuroscience literature (electrophysiology on animals and imaging studies on humans) and in computational learning literature.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
R. S. Bapi and K. Doya, (1998). A sequence learning architecture based on cortico-basal ganglionic loops and reinforcement learning. In Proceedings of the 5th International Conference on Neural Information Processing, 1, 260–263.
P. F. Dominey, (1995). Complex sensory-motor sequence learning based on recurrent state representation and reinforcement learning. Biological Cybernetics, 73, 265–274.
J. Elman, (1990). Finding structure in time. Cognitive Science, 14, 179–211.
M. Haruno, D. M. Wolpert, and M. Kawato, (1999). Multiple paired forward-inverse models for human motor learning and control. In M. J. Kearns, S. A. Solla, and D. A. Cohen (Eds.), Advances in Neural Information Processing Systems, 11, MIT Press, Cambridge, MA, USA, pp. 31–37.
J. C. Houk, J. L. Davis, and D. G. Beiser, editors, (1995). Models of information processing in the basal ganglia. MIT Press, Cambridge, MA, USA.
R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton, (1991). Adaptive mixtures of local experts. Neural Computation, 3, 79–87.
M. I. Jordan and R. A. Jacobs (1994). Hierarchiacal mixtures of experts and the EM algorithm. Neural Computation, 6, 181–214.
R. Parr and S. Russell, (1998). Reinforcement learning with hierarchies of machines. In M. I. Jordan, M. J. Kearns and S. A. Solla (Eds.), Advances in Neural Information Processing Systems, 10, MIT Press, Cambridge, MA, USA, pp. 1043–1049.
D. Precup and R. S. Sutton, (1998). Multi-time models for temporally abstract planning. In M. I. Jordan, M. J. Kearns and S. A. Solla (Eds.), Advances in Neural Information Processing Systems, 10, MIT Press, Cambridge, MA, USA, pp. 1050–1056.
D. A. Rosenbaum, (1991). Human Motor Control. Academic Press, San Diego, CA, USA.
K. Shima, H. Mushiake, N. Saito, and J. Tanji, (1996). Role for cells in the presupple-mentary motor area in updating motor plans. Proceedings of the National Academy of Science USA, 93, 8694–8698.
R. Sun and T. Peterson, (1999). Multi-agent reinforcement learning: weighting and partitioning. Neural Networks, 12, 727–753.
J. Tani and S. Nolfi, (1999). Learning to perceive the world as articulated: an approach for hierarchicall earning of sensory-motor systems. Neural Networks, 12, 1131–1141.
J. Tanji, (1994). The supplementary motor area in the cerebral cortex. Neuroscience Research, 19, 251–268.
J. Tanji and K. Shima, (1994). Role for supplementary motor area cells in planning severalmo vements ahead. Nature, 371,29, 413–416.
D. M. Wolpert and M. Kawato, (1998). Multiple paired forward and inverse models for motor control. Neural Networks, 11, 1317–1329.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Bapi, R.S., Doya, K. (2000). Multiple Forward Model Architecture for Sequence Processing. In: Sun, R., Giles, C.L. (eds) Sequence Learning. Lecture Notes in Computer Science(), vol 1828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44565-X_14
Download citation
DOI: https://doi.org/10.1007/3-540-44565-X_14
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41597-8
Online ISBN: 978-3-540-44565-4
eBook Packages: Springer Book Archive