Factor Analysis Using Delta-Rule Wake-Sleep Learning

Neural Computation

Volumn 9, Issue 8, 1997, Pages 1781-1803

  Neal, Radford M ^a   Dayan, Peter ^b  

^a UNIVERSITY OF TORONTO   (Canada)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BIOLOGICAL MODEL; FACTORIAL ANALYSIS; LEARNING; PSYCHOLOGICAL MODEL; SLEEP; STATISTICAL MODEL; WAKEFULNESS;

FACTOR ANALYSIS, STATISTICAL; LEARNING; MODELS, NEUROLOGICAL; MODELS, PSYCHOLOGICAL; MODELS, STATISTICAL; SLEEP; WAKEFULNESS;

EID: 0031573050     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/neco.1997.9.8.1781     Document Type: Article

References (35)

1. Barlow, H. B. (1989). Unsupervised learning. Neural Computation, 1, 295-311.
2. Dayan, P., & Hinton, G. E. (1996). Varieties of Helmholtz machine. Neural Networks, 9, 1385-1403.
3. Dayan, P., Hinton, G. E., Neal, R. M., & Zemel, R. S. (1995). The Helmholtz machine. Neural Computation, 7, 889-904.
4. Dempster, A. P., Laird, N. M, & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Proceedings of the Royal Statistical Society, B39, 1-38.
5. Everitt, B. S. (1984). An introduction to latent variable models. London: Chapman and Hall.
6. Felleman D. J., & Van Essen, D. C. (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1, 1-47.
7. Földiák, P. (1989). Adaptive network for optimal linear feature extraction. In Proceedings of the International Joint Conference on Neural Networks, Washington, DC (Vol. I, pp. 401-405).
8. Grenander, U. (1976-1981). Lectures in pattern theory I, II and III: Pattern analysis, pattern synthesis and regular structures. Berlin: Springer-Verlag.
9. Hasselmo, M. E., & Bower, J. M. (1993). Acetylcholine and memory. Trends in Neurosciences, 16, 218-222.
10. Hebb, D. O. (1949). The organization of behavior: A neuropsychological theory. New York: Wiley.
11. Hinton, G. E., Dayan, P., Frey, B., & Neal, R. M. (1995). The wake-sleep algorithm for self-organizing neural networks. Science, 268, 1158-1160.
12. Hinton, G. E., & Zemel, R. S. (1994). Autoencoders, minimum description length and Helmholtz free energy. In J. D. Cowan, G. Tesauro, & J. Alspector (Eds.), Advances in neural information processing systems, 6 (pp. 3-10). San Mateo, CA: Morgan Kaufmann.
13. Jolliffe, I. T. (1986) Principal component analysis, New York: Springer-Verlag.
14. Jöreskog, K. G. (1967). Some contributions to maximum likelihood factor analysis, Psychometrika, 32, 443-482.
15. Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis, Psychometrika, 34, 183-202.
16. Jöreskog, K. G. (1977). Factor analysis by least-squares and maximum-likelihood methods. In K. Enslein, A. Ralston, & H. S. Wilf (Eds.), Statistical methods for digital computers. New York: Wiley.
17. Linsker, R. (1986). From basic network principles to neural architecture. Proceedings of the National Academy of Sciences, 83, 7508-7512, 8390-8394, 8779-9783.
18. Linsker, R. (1988). Self-organization in a perceptual network. Computer, 21, 105-128.
19. Miller, K. D. (1994). A model for the development of simple cell receptive fields and the ordered arrangement of orientation columns through activity-dependent competition between ON-and OFF-center inputs. Journal of Neuroscience, 14, 409-441.
20. Miller, K. D., Keller, J. B., & Stryker, M. P. (1989). Ocular dominance column development: Analysis and simulation. Science, 245, 605-615.
21. Miller, K. D., & MacKay, D. J. C. (1994). The role of constraints in Hebbian learning. Neural Computation, 6, 100-126.
22. Montague, P. R., & Sejnowski, T. J. (1994). The predictive brain: Temporal coincidence and temporal order in synaptic learning mechanisms. Learning and Memory, 1, 1-33.
23. Mumford, D. (1994). Neuronal architectures for pattern-theoretic problems. In C. Koch & J. Davis (Eds.), Large-scale theories of the cortex (pp. 125-152). Cambridge, MA: MIT Press.
24. Oja, E. (1989). Neural networks, principal components, and subspaces. International Journal of Neural Systems, 1, 61-68.
25. Olshausen, B. A., & Field, D. J. (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381, 607-609.
26. Plumbley, M. D. (1993). Efficient information transfer and anti-Hebbian neural networks. Neural Networks, 6, 823-833.
27. Rao, P. N. R., & Ballard, D. H. (1995). Dynamic model of visual memory predicts neural response properties in the visual cortex (Technical Rep. No. 95.4). Rochester, NY: Department of Computer Science, University of Rochester.
28. Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: The effectiveness of reinforcement and non-reinforcement. In A. H. Black & W. F. Prokasy (Eds.), Classical conditioning II: Current research and theory (pp. 64-69). New York: Appleton-Century-Crofts.
29. Rubin, D. B., & Thayer, D. T. (1982). EM algorithms for ML factor analysis. Psychometrika, 47, 69-76.
30. Sanger, T. D. (1989). Optimal unsupervised learning in a single-layer linear feed-forward neural network. Neural Networks, 2, 459-473.
31. Sutton, R. S., & Barto, A. G. (1981). Toward a modern theory of adaptive networks: Expectation and prediction. Psychological Review, 88, 135-170.
32. von der Malsburg, C. (1973). Self-organization of orientation sensitive cells in the striate cortex. Kybernetic, 14, 85-100.
33. von der Malsburg, C., & Willshaw D. J. (1977). How to label nerve cells so that they can interconnect in an ordered fashion. Proceedings of the National Academy of Sciences, 74, 5176-5178.
34. Willshaw, D. J., & von der Malsburg, C. (1976). How patterned neural connections can be set up by self-organisation. Proceedings of the Royal Society of London B, 194, 431-445.
35. Willshaw, D. J., & von der Malsburg, C. (1979). A marker induction mechanism for the establishment of ordered neural mappings: Its application to the retinotectal problem. Philosophical Transactions of the Royal Society B, 287, 203-243.