A model of primate visual cortex based on category-specific redundancies in natural images

Connection Science

Volumn 22, Issue 4, 2010, Pages 313-329

  Malmir, Mohsen ^a   Ghidary, S Shiry ^a  

^a AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Categoryspecific redundancies; Hierarchical neural networks; Natural image statistics; Neuronal selectivity; Redundancy reduction; Visual cortex

Indexed keywords

CATEGORY SPECIFICS; HIERARCHICAL NEURAL NETWORKS; NATURAL IMAGE STATISTICS; REDUNDANCY REDUCTIONS; VISUAL CORTEXES;

DISCRETE COSINE TRANSFORMS; REDUNDANCY;

NEURONS;

EID: 78649357574     PISSN: 09540091     EISSN: 13600494     Source Type: Journal    
DOI: 10.1080/09540091.2010.505975     Document Type: Article

References (52)

1. Angelucci, A., and Bullier, J. (2003), 'Reaching Beyond the Classical Receptive Field of V1 Neurons: Horizontal or Feedback Axons?', Journal of Physiology-Paris, 97, 141-154.
2. Atick, J. (1992), 'Could Information Theory Provide an Ecological Theory of Sensory Processing? Network: Computation. Neural Systems, 3, 213-251.
3. Attneave, F. (1954), 'Some Informational Aspects of Visual Perception', Psychological Review, 61, 183-193.
4. Barlow, H.B. (1961), 'Possible Principles Underlying the Transformation of Sensory Messages', in Sensory Communication, ed.WA Rosenblith, pp. 217-234.
5. Bell, A.J., and Sejnowski, T.J. (1997), 'The "Independent Components" of Natural Scenes are Edge Filters', Vision Research, 37, 3327-3338.
6. Caltech, 101 Object Categories, www.vision.caltech.edu/Image_Datasets/Caltech101/.
7. Carandini, M., Heeger, D.J., and Movshon, J.A. (1997), 'Linearity and Normalization in Simple Cells of the Macaque Primary Visual Cortex', Journal of Neuroscience, 17, 8621-8644.
8. Cristianini, N., and Shawe-Taylor, J. (2000), An Introduction to SupportVector Machines and other Kernel-based Learning Methods, Cambridge: Cambridge University Press.
9. Dan, Y., Atick, J.J., and Reid, R.C. (1996), 'Efficient Coding of Natural Scenes in the Lateral Geniculate Nucleus: Experimental Test of a Computational Theory', Journal of Neuroscience, 16, 3351-3362.
10. Desimone, R. (1991), 'Face-selective Cells in the Temporal Cortex of Monkeys', Journal of Cognitive Neuroscience, 3, 1-8.
11. Desimone, R., and Schein, S.J. (1987), 'Visual Properties of Neurons in Area V4 of the Macaque: Sensitivity to Stimulus Form', Journal of Neurophysiology, 57, 835-868.
12. Field, D. (1994), 'What is the Goal of Sensory Coding?' Neural Computation, 6, 559-601.
13. Gallant, J.L., Connor, C.E., Rakshit, S., Lewis, J.W., andVan Essen, D.C. (1996), 'Neural Responses to Polar, Hyperbolic, and Cartesian Gratings in Area V4 of the Macaque Monkey', Journal of Neurophysiology, 76, 2718-2739.
14. Grigorescu, C., Petkov, N., and Westenberg, M.A. (2004), 'Contour and Boundary Detection Improved by Surround Suppression of Texture Edges', Image and Vision Computing, 22, 609-622.
15. Hegdé, J., and Van Essen, D.C. (2000), 'Selectivity for Complex Shapes in Primate Visual Area V2', Journal of Neuroscience, 20, 1-6.
16. Hegdé, J., and Van Essen, D.C. (2003), 'Strategies of Shape Representation in Macaque Visual Area V2', Visual Neuroscience, 20, 313-328.
17. Hegdé, J., and Van Essen, D.C. (2007), 'A Comparative Study of Shape Representation in Macaque Visual Areas V2 and V4', Cerebral Cortex, 17, 1100-1116.
18. Hubel, D.H., and Wiesel, T.N. (1962), 'Receptive Fields, Binocular Interaction and Functional Architecture in the Cat's Visual Cortex', Journal of Physiology, 160, 106-154.
19. Hyvärinen, A., and Hoyer, P.O. (2000), 'Emergence of Phase- and Shift-invariant Features by Decomposition of Natural Images into Independent Feature Subspaces', Neural Computation, 12, 1705-1720.
20. Hyvärinen, A., Hoyer, P., and Inki, M. (2001), 'Topographic Independent Component Analysis', Neural Computation, 13, 1527-1558.
21. Ito, M., and Komatsu, H. (2004), 'Representation of Angles Embedded within Contour Stimuli in Area V2 of Macaque Monkeys', Journal of Neuroscience, 24, 3313-3324.
22. Itti, L., Koch, C., and Braun, J. (2000), 'Revisiting SpatialVision: Towards a Unifying Model', Journal of Optical Society of America, 17, 1899-1917.
23. Jones, J.P., and Palmer, L.A. (1987), 'An Evaluation of the Two-dimensional Gabor Filter Model of Simple Receptive Fields in Cat Striate Cortex', Journal of Neurophysiology, 58, 1233-1258.
24. Karklin, Y., and Lewicki, M.S. (2003), 'Learning Higher-order Structures in Natural Images', Network: Computation. Neural Systems, 14, 483-499.
25. Karklin, Y., and Lewicki, M.S. (2005), 'A Hierarchical Bayesian Model for Learning Nonlinear Statistical Regularities in Nonstationary Natural Signals', Neural Computation, 17, 397-423.
26. Kiani, R., Esteky, H., Mirpour, K., and Tanaka, K. (2007), 'Object Category Structure in Response Patterns of Neuronal Population in Monkey Inferior Temporal Cortex', Journal of Neurophysiology, 97, 4296-4309.
27. Laughlin, S. (1981), 'A Simple Coding Procedure Enhances a Neuron's Information Capacity', Zeitschrift für Naturforschung, 36, 910-912.
28. Logothetis, N.K., Pauls, J., and Poggio, T. (1995), 'Shape Representation in the Inferior Temporal Cortex of Monkeys', Current Biology, 5, 552-563.
29. Malmir, M., and Shiry, S. (2009), 'Class Specific Redundancies in Natural Images: A Theory of Extrastriate Visual Processing', in International Joint Conference on Neural Networks (IJCNN), Atlanta, GA.
30. Maunsell, J.H.R., and Newsome, W.T. (1987), 'Visual Processing in Monkey Extrastriate Cortex', Annual Review Neuroscience, 10, 363-401.
31. Neumann, H., and Sepp, W. (1999), 'Recurrent V1-V2 Interactions in Early Visual Boundary Processing', Biological Cybernetics, 81, 425-444.
32. Olshausen, B.A., and Field, D.J. (1996), 'Emergence of Simple Cell Receptive Field Properties by Learning a Sparse Code for Natural Images', Nature, 381, 607-609.
33. Park, H.J., and Lee, T.W. (2005), 'Unsupervised Learning of Nonlinear Dependencies in Natural Images', International Journal of Imaging Systems. Technology, 15, 34-47.
34. Pasupathy, A., and Connor, C.E. (1999), 'Responses to Contour Features in MacaqueAreaV4, Journal of Neurophysiology, 82, 2490-2502.
35. Pasupathy, A., and Connor, C.E. (2001), 'Shape Representation in Area V4: Position-specific Tuning for Boundary Conformation', Journal of Neurophysiology, 86, 2505-2519.
36. Plebe, A., and Domenella, R.G. (2007), 'Object Recognition by Artificial Cortical Maps', Neural Networks, 20, 763-780.
37. Pollen, D.A., Przybyszewski, A.W., Rubin, M.A., and Foote, W. (2002), 'Spatial Receptive Field Organization of Macaque V4 Neurons', Cerebral Cortex, 12, 601-616.
38. Reynolds, J.H., Chelazzi, L., and Desimone, R. (1999), 'Competitive Mechanisms Subserve Attention in Macaque Areas V2 and V4', Journal of Neuroscience, 19, 1736-1753.
39. RoboCup@Home Competitions Rule Book (2009), www.robocup.org/robocup-home/.
40. Schwartz, E.L., Desimone, R., Albright, T.D., and Gross, C.G. (1983), 'Shape Recognition and InferiorTemporal Neurons', Proceedings of the National Academy of Science, 80, 5776-5778.
41. Schwartz, O., Sejnowski, T.J., and Dayan, P. (2006), 'Soft Mixer Assignment in a Hierarchical Generative Model of Natural Scene Statistics', Neural Computation, 18, 2680-2718.
42. Schwartz, O., and Simoncelli, E.P. (2001), 'Natural Signal Statistics and Sensory Gain Control', Nature Neuroscience, 4, 819-825.
43. Seriès, P., Lorenceau, J., and Frégnac, Y. (2003), 'The "Silent" Surround ofV1 Receptive Fields: Theory and Experiments', Journal of Physiology-Paris, 97, pp. 453-474.
44. Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., and Poggio, T. (2007), 'Robust Object Recognition with Cortex-like Mechanisms', IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, No. 3.
45. Simoncelli, E.P. (1997), 'Statistical Models for Images: Compression, Restoration and Synthesis', in 31st Asilomar Conference on Signals, Systems and Computers.
46. Tanaka, K. (1994), 'Inferotemporal Cortex and Object Vision', Annual Review of Neuroscience, 19, 109-139.
47. Tsunoda, K., Yamane, Y., Nishizaki, M., and Tanifuji, M. (2001), 'Complex Objects are Represented in Macaque Inferotemporal Cortex by the Combination of Feature Columns', Nature Neuroscience, 4, 832-838.
48. Valpola, H., Harva, M., and Karhunen, J. (2004), 'Hierarchical Models of Variance Sources', Signal Processing, 84, 267-282.
49. Wainwright, M.J., and Simoncelli, E.P. (2000), 'Scale Mixtures of Gaussians and the Statistics of Natural Images', Advances in Neural Information Processing Systems, 12, 855-861.
50. Zetzsche, C., Wegmann, B., and Barth, E. (1993), 'Nonlinear Aspects of Primary Vision: Entropy Reduction Beyond Decorrelation', in Proceedings of the SID, Society for Information Display, ed. J. Morreale, pp. 933-936.
51. Zhang, B. et al. (2005), 'Delayed Maturation of Receptive Field Center-Surround Mechanisms in V2', Proceedings of the National Academy of Sciences, 2, 5862-5867.
52. Zoccolan, D., Cox, D., and DiCarlo, J. (2005), 'Multiple Object Response Normalization in Monkey Inferotemporal Cortex', Journal of Neuroscience, 25, 8150-8164.