Receptive Field Formation in Natural Scene Environments: Comparison of Single-Cell Learning Rules

Neural Computation

Volumn 10, Issue 7, 1998, Pages 1797-1813

  Blais, Brian S ^a   Intrator, N ^a   Shouval, H ^a   Cooper, Leon N ^a  

^a BROWN UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0002828852     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976698300017142     Document Type: Article

References (28)

1. Amari, S., Cichocki, A., & Yang, H. H. (1996). A new learning algorithm for blind signal separation. In G. Tesauro, D. Touretzky, & T. Leen (Eds.), Advances in neural information processing systems, 8 (pp. 757-763).
2. Baddeley, R. J. (1996). An efficient code in V1? Nature (News and Views), 381, 560-561.
3. Bell, A. J., & Sejnowski, T. J. (1995). An information-maximisation approach to blind separation and blind deconvolution. Neural Computation, 7(6), 1129-1159.
4. Bell, A. J., & Sejnowski, T. J. (1997). The independent components of natural scenes are edge filters. Vision Research, 37(23), 3327-3338.
5. Bienenstock, E. L., Cooper, L. N., & Munro, P. W. (1982). Theory for the development of neuron selectivity: Orientation specificity and binocular interaction in visual cortex. Journal of Neuroscience, 2, 32-48.
6. Comon, P. (1994). Independent component analysis, a new concept? Signal Processing, 36, 287-314.
7. Daugman, J. G. (1988). Complete discrete 2D Gabor transforms by neural networks for image analysis and compression. IEEE Transactions on ASSP, 36, 1169-1179.
8. Delfosse, N., & Loubaton, P. (1995). Adaptive blind separation of independent sources: A deflation approach. Signal Processing, 45, 59-83.
9. Diaconis, P., & Freedman, D. (1984). Asymptotics of graphical projection pursuit. Annals of Statistics, 12, 793-815.
10. Field, D. J. (1994). What is the goal of sensory coding? Neural Computation, 6, 559-601.
11. Friedman, J. H. (1987). Exploratory projection pursuit. Journal of the American Statistical Association, 82, 249-266.
12. Friedman, J. H., & Tukey, J. W. (1974). A projection pursuit algorithm for exploratory data analysis. IEEE Transactions on Computers, C(23), 881-889.
13. Huber, P. J. (1985). Projection pursuit (with discussion). Annals of Statistics, 13, 435-475.
14. Hyvarinen, A., & Oja, E. (1996). A fast fixed-point algorithm for independent component analysis. Int. Journal of Neural Systems, 7(6), 671-687.
15. Hyvarinen, A., & Oja, E. (1997). A fast fixed-point algorithm for independent component analysis. Neural Computation, 9(7), 671-687.
16. Intrator, N. (1990). A neural network for feature extraction. In D. S. Touretzky & R. P. Lippmann (Eds.), Advances in neural information processing systems, 2 (pp. 719-726). San Mateo, CA: Morgan Kaufmann.
17. Intrator, N. (1996). Neuronal goals: Efficient coding and coincidence detection. In S. Amari, L. Xu, L. W. Chan, I. King, & K. S. Leung (Eds.), Proceedings of ICONIP Hong Kong: Progress in Neural Information Processing (Vol. 1, pp. 29-34). Berlin: Springer-Verlag.
18. Intrator, N. & Cooper, L. N. (1992). Objective function formulation of the BCM theory of visual cortical plasticity: Statistical connections, stability conditions. Neural Networks, 5, 3-17.
19. Jones, M. C., & Sibson, R. (1987). What is projection pursuit? (with discussion). J. Roy. Statist. Soc., Ser. A, 150, 1-36.
20. Kendall, M., & Stuart, A. (1977). The advanced theory of statistics. New York: Macmillan.
21. Law, C., & Cooper, L. (1994). Formation of receptive fields according to the BCM theory in realistic visual environments. Proceedings National Academy of Sciences, 91, 7797-7801.
22. Levy, W. B., & Baxter, R. A. (1996). Energy efficient neural codes. Neural Computation, 8, 531-543.
23. Oja, E. (1995). The nonlinear PCA learning rule and signal separation - mathematical analysis (Tech. Rep. No. A26). Helsinki: Helsinki University, CS and Inf. Sci. Lab.
24. Olshausen, B. A., & Field, D. J. (1996). Emergence of simple cell receptvie field properties by learning a sparse code for natural images. Nature, 381, 607-609.
25. Press, W., & Lee, C. W. (1997). Searching for optimal value codes: Projection pursuit analysis of the statistical structure in natural scenes. In The neurobiology of computation: Proceedings of the Fifth Annual Computation and Neural Systems Conference (pp. 771-777). New York: Plenum.
26. Ruderman, D. L. (1994). The statistics of natural images. Network, 5(4), 517-548.
27. Shouval, H., & Liu, Y. (1996). Principal component neurons in a realistic visual environment. Network, 7(3), 501-515.
28. Zetzsche, C. (1997). Intrinsic dimensionality: How biological mechanisms exploit the statistical structure of natural images (talk given at the Natural Scene Statistics Meeting, Hancock, MA).