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The “independent components” of natural scenes are edge filters

Bell, Anthony J., Sejnowski, Terrence J.·1997·Vision Research
DOI 10.1016/s0042-6989(97)00121-1
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  • The authors developed a method that accounts for the pitfalls of unbalanced cell-type composition and inflated correlations among sparse genes to call multiresolution gene expression programs from single-cell data using an SVD-based approximation of zero-phase component analysis (ZCA) and gene sparsity-dependent thresholding.

    “We developed a method which accounts for these pitfalls to call multiresolution gene expression programs from single-cell data using an SVD-based approximation of zero-phase component analysis (ZCA) and gene sparsity-dependent thresholding99,100.”

    From Single-cell multiregion dissection of Alzheimer’s disease (2024) · cites this paper as [100]

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Research works printed but not linked: 26

  1. Amari, S., Cichocki, A. and Yang, H. H. (1996). A new learning algorithm for blind signal separation, Advances in neural informa- tion processing systems (Vol. 8). Cambridge, MA: MIT Press.
  2. Baddeley, R. (1996). Searching for filters with "interesting" output distributions: an uninteresting direction to explore? Network, in press.
  3. Barlow, H. B. (1994). What is the computational goal of the neocortex? In Koch, C. (Ed.) Large-scale neuronal theories of the brain. Cambridge, MA: MIT Press.
  4. Bell, A. J. (1992). Self-organisation in real neurons: anti-Hebb in Channel space? In Moody, J. et al. (Eds) Advances in neural information processing systems (Vol. 4, pp. 59-66).
  5. Bell, A. J. & Sejnowski, T. J. (1995b). Fast blind separation based on information theory, in Proc. Intern. Syrup. on Nonlinear Theory and Applications, Las Vegas, Dec. 1995.
  6. Touretzky, D. (Ed.), Advances in Neural Information Processing Systems (Vol. 1). Morgan-Kaufmann.
  7. Burr, D. C. & Morrone, M. C. (1990). Feature detection in biological and artificial vision systems. In Blakemore, C. (Ed.), Vision: coding and efficiency. Cambridge, U.K.: Cambridge University Press.
  8. Cardoso, J.-F. & Laheld, B. (1996) Equivariant adaptive source separation, IEEE Trans. on Signal Proc., to appear.
  9. Cichocki, A., Unbehauen, R. & Rummert, E. (1994). Robust learning algorithm for blind separation of signals. Electronics Letters, 3017, 1386-1387.
  10. Cover, T. M. & Thomas, J. A. (1991). Elements of information theory. New York: John Wiley.
  11. Field, D. J. (1987). Relations between the statistics of natural images and the response properties of cortical cells. Journal of the Optical Society of America A, 412, 2370-2393.
  12. Harpur, G. F. & Prager, R. W. (1996). Development of low entropy coding in a recurrent network. Network, in press.
  13. Haykin, S. (Ed.) (1994). Blind deconvolution. New Jersey: Prentice- Hall.
  14. Hubel, D. H. & Wiesel, T. N. (1968). Receptive fields and functional architecture of monkey striate cortex. Journal of Physiology, 195, 215-244.
  15. Hubel, D. H. & Wiesel, T. N. (1974). Uniformity of monkey striate cortex: a parallel relationship between field size, scatter, and magnification factor. Journal of Comparative Neurology, 158, 295- 306.
  16. Huber, P. J. (1985). Projection pursuit. The Annals of Statistics, 13, 435-475.
  17. Karhunen, J., Wang, L. & Jousensalo, J. (1995). Neural estimation of basis vectors in Independent Component Analysis, Proc. ICANN, Paris, 1995.
  18. Karhunen, J., Oja, E., Wang, L., Vigario, R. & Joutsenalo, J. (1996). A class of neural networks for independent component analysis. submitted to IEEE Trans. on Neural Networks.
  19. Law, C. C. & Cooper, L. N. (1994). Formation of receptive fields in realistic visual environments according to the Bienestock, Cooper and Munro (BCM) theory. Proceedings of the National Academy of Sciences USA, 91, 7797-7801.
  20. Lee, T.-W., Bell, A. J. & Lambert, R. (1997). Blind separation of delayed and convolved sources, in Advances in neural information processing systems (Vol. 9). Cambridge, MA: MIT Press.
  21. Marr, D. & Hildreth, E. (1980). Theory of edge detection. Proceedings of the Royal Society of London B, 207, 187-217.
  22. Miller, K. D. (1988). Correlation-based models of neural development. In Gluck, M. & Rumelhart, D. (Eds), Neuroscience and connec- tionist theory (pp. 267-353). Hillsdale, NJ: Lawrence Erlbaum.
  23. Pham, D. T., Garrat, P. & Jutten, C. (1992). Separation of a mixture of independent sources through a maximum likelihood approach, in Proc. EU-SIPCO, 771-774.
  24. Schmidhuber, J., Eldracher, M. & Foltin, B. (1996). Semi-linear predictability minimization produces well-known feature detectors, Neural Computation, in press.
  25. Shouval, H. (1995). Formation and organisation of receptive fields, with an input environment composed of natural scenes, Ph.D. thesis, Dept. of Physics, Brown University.
  26. Torkkola, K. (1996). Blind separation of convolved sources based on information maximisation. Proc. IEEE Workshop on Neural Networks and Signal Processing, Kyota, Japan, Sept. 1996.

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Authors
Bell, A. J., & Sejnowski, T. J.
Venue
Vision Research
Publisher
Elsevier BV
Published
1997-12
Volume
37
Issue
23
Pages
3327-3338
ISSN
0042-6989
DOI
10.1016/s0042-6989(97)00121-1
Cited by
1,592
References
3
Language
en
Licence

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