Predictive Hebbian learning

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Predictive Hebbian learning

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Annual Workshop on Computational Learning Theory archive
Proceedings of the eighth annual conference on Computational learning theory table of contents

Santa Cruz, California, United States

Pages: 15 - 18

Year of Publication: 1995

ISBN:0-89791-723-5

Authors

Terrence J. Sejnowski	Howard Hughes Medical Institute, The Salk Institute for Biological Studies, 10010 North Torrey Pines Road, La Jolla, CA and Department of Biology, University of California, San Diego, La Jolla, CA
Peter Dayan	Department of Brain and Cognitive Science, MIT, Cambridge, MA
P. Read Montague	Division of Neuroscience, Baylor College of Medicine, Houston, TX

Sponsors

SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
University of California : University of California

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 15, Downloads (12 Months): 41, Citation Count: 1

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Barto, AG, Sutton, RS and Anderson, (_;W. 1EEE 7ransacho~,s on Systems, MaTl, and Cyber~et~cs, 13, 834 (1983).
	2	Barto, AG, Sutton, RS, Watkins, CJCH, Techmcal Report 89-95, (Computer and Information Science, University of Massachusetts, Amherst, MA, 1989
	3	Bellman, R (1957) D.q~ctmtc Programming. Princeton: Princeton U. Press.
	4	Dayan, P (1994). Computational modelling. Current Olm~to~ z~t Neurobtology, 4, 212-217.
	5	Peter Dayan , Terrence J. Sejnowski, TD(λ) Converges with Probability 1, Machine Learning, v.14 n.3, p.295-301, March 1994 [doi>10.1023/A:1022657612745]
	6	Dickenson, A (1980). CoMemporary Animal Learn,ng Theory, Cambridge, England: Cambridge University Press.
	7	Gallistel, CR (1990) The organzzatto~, of leam~ng. Cambridge, Mass: MIT Press.
	8	Gluck, MA, Thompson, RF (1987) Modeling the neural substrates of associative learning and memory: a computational approach. Psychological Rev. 94, 176-191.
	9	Greenough, WT, and Bailey, CH (1988). The anatomy of a memory: Convergence of results across a diversity of tests. Trends zn Neurosczence, 11, 142-147.
	10	Hammer, M (1994) An identified neuron mediates the unconditioned stimulus in associative olfactory learning in honeybees. Nature 366:59-63.
	11	Hawkins RD, Kandel ER (1984) Is there a cell-biological alphabet for simple forms of learning? Psychological Rev. 91(3):375-91.
	12	Hebb, DO (1949) The orgamzat,on of behavzor. New York: Wiley.
	13	Kalman, RE (1960) A new approach to linear filtering and prediction problems. J. Baszc Eng., Trans ASslIE, Series D 82(1):35-45.
	14	Ljungberg, T, Apicella, P & Schultz, W (1992). Responses of monkey dopamine neurons during learning of behavioral reactions. Journal of Neurophyszology, 67(1), 145-163.
	15	MacKintosh, NJ (1983) ConditioniT~g aT~d Assoc~ahve Learning. Oxford University Press: Oxford, UI(.
	16	Montague, P. R., Dayan, P. and Sejnowski, T. 3., A framework for mesolimbic dopamine systems based on predictive Hebbian learning, Journal of NeuroscleT~ce (submitted for publication).
	17	Quartz, S, Dayan, P, Montague, PR, Sejnowski, TJ (1992) Expectation learning in the brain using diffuse ascending connections. Soc. Neurosc,. Abslr. 18:1210
	18	Pearce JM, Hall G (1980) A model for Pavlovian learning: variations in the effectiveness of conditioned but. not of unconditioned stimuli. Psychologtcal Revtew 87: 532-52.
	19	Rauschecker JP (1991) Mechanisms of visual plasticity: Hebb synapses, NMDA receptors, and beyond. Phystologzcal Reviews 71(2):587-615.
	20	Rescorla, RA & Wagner, AR (1972). A theory of Pavlovian conditioning: The effectiveness of reinforcement and non-reinforcement. In AH Black & WF Prokasy, editors, Classical Condit~o~zng II: Current Research aT~d Theory, pp 64-69. New York, NY: Appleton-Century- Crofts.
	21	Schultz, W, Apicella, P, Ljungberg, T (1993) Responses of monkey dopamine neurons to reward and conditioned stimuli during successive steps of learning a delayed response task. J. Neuroscience 13(3):900-13.
	22	Sutton, RS, Barto, AG (1981). Toward a modern theory of adaptive networks: Expectation and prediction. Psychological Revzew, 88 2, pp 135-170.
	23	Richard S. Sutton, Learning to Predict by the Methods of Temporal Differences, Machine Learning, v.3 n.1, p.9-44, August 1988 [doi>10.1023/A:1022633531479]
	24	Sutton, RS, Barto, AG (1987). A temporM-difference model of classical conditioning. Proceedings of the N, nth Annual CoT~ference of the Cognztwe Science Society. Seattle, WA.
	25	Sutton, RS, Barto, AG (1989). Time-derivative models of Pavlovian reinforcement. In M Gabriel & J Moore, editors, LearT~zT~g and Computahonal Neurosc~ence. Cambridge, MA' MIT Press.
	26	Bernard Widrow , Samuel D. Stearns, Adaptive signal processing, Prentice-Hall, Inc., Upper Saddle River, NJ, 1985