Statistical model of natural stimuli predicts edge-like pooling of spatial frequency channels in V2

BMC Neuroscience

Volumn 6, Issue , 2005, Pages

  Hyvärinen, Aapo ^a   Gutmann, Michael ^a   Hoyer, Patrik O ^a  

^a UNIVERSITY OF HELSINKI   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; CONTROLLED STUDY; INDEPENDENT COMPONENT ANALYSIS; PREDICTION; QUANTITATIVE ANALYSIS; STATISTICAL MODEL; VISION; VISUAL CORTEX; VISUAL ORIENTATION; VISUAL STIMULATION;

MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); NORMAL DISTRIBUTION; PHOTIC STIMULATION; PREDICTIVE VALUE OF TESTS; VISUAL CORTEX;

EID: 26444552966     PISSN: 14712202     EISSN: 14712202     Source Type: Journal    
DOI: 10.1186/1471-2202-6-12     Document Type: Article

References (31)

1. Chance FS, Nelson SB, Abbott LF: Complex cells as cortically amplified simple cells. Nature Neuroscience 1999, 2(3):277-282.
2. Mechler F, Ringach DL: On the classification of simple and complex cells. Vision Research 2002, 42(8):1017-33.
3. Kagan I, Gur M, Snodderly D: Spatial organization of receptive fields of V1 neurons of alert monkeys: comparison with responses to gratings. J Neurophysiol 2002, 88:2557-2574.
4. Hyvärinen A, Karhunen J, Oja E: Independent Component Analysis 2001 [http://www.cis.hut.fi/projects/ica/book/]. Wiley Interscience
5. Field D: What is the goal of sensory coding? Neural Computation 1994, 6:559-601.
6. Olshausen BA, Field DJ: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 1996, 381:607-609.
7. Bell A, Sejnowski T: The 'Independent Components' of Natural Scenes are Edge Filters. Vision Research 1997, 37:3327-3338.
8. van Hateren JH, van der Schaaf A: Independent component filters of natural images compared with simple cells in primary visual cortex. Proc Royal Society, Ser B 1998, 265:359-366.
9. van Hateren JH, Ruderman DL: Independent component analysis of natural image sequences yields spatiotem poral filters similar to simple cells in primary visual cortex. Proc Royal Society, Ser B 1998, 265:2315-2320.
10. Hoyer PO, Hyvärinen A: Independent Component Analysis Applied to Feature Extraction from Colour and Stereo Images. Network: Computation in Neural Systems 2000, 11(3):191-210.
11. Caywood M, Willmore B, Tolhurst D: Independent Components of Color Natural Scenes Resemble V1 Neurons in Their Spatial and Color Tuning. J of Neurophysiology 2004, 91:2859-2873.
12. Wachtler T, Lee TW, Sejnowski TJ: Chromatic structure of natural scenes. J Opt Soc Am A 2001, 18:65-77.
13. Hyvärinen A, Hoyer PO: Emergence of phase and shift invariant features by decomposition of natural images into independent feature subspaces. Neural Computation 2000, 12(7):1705-1720.
14. Hoyer PO, Hyvärinen A: A multi-layer sparse coding network learns contour coding from natural images. Vision Research 2002, 42(12):1593-1605.
15. Hyvärinen A: Fast and Robust Fixed-Point Algorithms for Independent Component Analysis. IEEE Transactions on Neural Networks 1999, 10(3):626-634.
16. Griffin L, Lillholm M, Nielsen M: Natural image profiles are most likely to be step edges. Vision Research 2004, 44(4):407-421.
17. Mareschal I, Baker CL: A cortical locus for the processing of contrast-defined contours. Nature Neuroscience 1998, 1(2):150-154.
18. Olzak L, Wickens T: Discrimination of complex patterns: orientation information is integrated across spatial scale; spatial-frequency and contrast information are not. Perception 1997, 26:1101-1120.
19. Geisler WS, Perry JS, Super BJ, Gallogly DP: Edge co-occurence in natural images predicts contour grouping performance. Vision Research 2001, 41:711-724.
20. Sigman M, Cecchi GA, Gilbert CD, Magnasco MO: On a common circle: Natural scenes and Gestalt rules. Proceedings of the National Academy of Science, USA 2001, 98:1935-1940.
21. Elder J, Goldberg R: Ecological statistics of Gestalt laws for the perceptual organization of contours. Journal of Vision 2002, 2(4):324-353.
22. Schwartz O, Simoncelli E: Natural signal statistics and sensory gain control. Nature Neuroscience 2001, 4(8):819-825.
23. Paatero P, Tapper U: Positive Matrix Factorization: A Non-negative Factor Model with Optimal Utilization of Error Estimates of Data Values. Environmetrics 1994, 5:111-126.
24. Lee DD, Seung HS: Learning the parts of objects by non-negative matrix factorization. Nature 1999, 401:788-791.
25. Mareschal I, Baker C: Temporal and spatial response to secondorder stimuli in cat area 18. J Neurophysiol 1998, 80:2811-2823.
26. Hurri J, Hyvärinen A: Simple-Cell-Like Receptive Fields Maximize Temporal Coherence in Natural Video. Neural Computation 2003, 15(3):663-691.
27. Hyvärinen A, Hurri J, Väyrynen J: Bubbles: A unifying framework for low-level statistical properties of natural image sequences. J Opt Soc Am A Opt Image Sci Vis 2003, 20(7):1237-1252.
28. Zetzsche C, Krieger G: Nonlinear neurons and high-order statistics: New approaches to human vision and electronic image processing. In Human Vision and Electronic Imaging IV (Proc. SPIE vol. 3644) Edited by: Rogowitz B, Pappas T. SPIE; 1999:2-33.
29. Zetzsche C, Röhrbein F: Nonlinear and extra-classical receptive field properties and the statistics of natural scenes. Network: Computation in Neural Systems 2001, 12:331-350.
30. Polat U, Tyler C: What pattern the eye sees best. Vision Research 1999, 39(5):887-895.
31. Gilbert CD, Wiesel TN: Intrinsic connectivity and receptive field properties in visual cortex. Vision Research 1985, 25(3):365-374.