A Model for Learning Topographically Organized Parts-Based Representations of Objects in Visual Cortex: Topographic Nonnegative Matrix Factorization

Neural Computation

Volumn 21, Issue 9, 2009, Pages 2605-2633

  Hosoda, Kenji ^a   Watanabe, Masataka ^a   Wersing, Heiko ^b   Körner, Edgar ^b   Tsujino, Hiroshi ^c   Tamura, Hiroshi ^d   Fujita, Ichiro ^d  

^a UNIVERSITY OF TOKYO   (Japan)

^b HONDA RESEARCH INSTITUTE EUROPE GMBH   (Germany)

^c HONDA RESEARCH INSTITUTE JAPAN CO LTD   (Japan)

^d OSAKA UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

CONFORMAL MAPPING; MATRIX ALGEBRA; MATRIX FACTORIZATION; NEURONS; OBJECT RECOGNITION; SELF ORGANIZING MAPS;

BASE FUNCTION; COLUMNAR CLUSTERS; INFERIOR TEMPORAL CORTICES; NONNEGATIVE MATRIX FACTORIZATION; OBJECT REPRESENTATIONS; OBJECTS RECOGNITION; PART BASED; PART-BASED REPRESENTATION; PROPERTY; VISUAL CORTEXES;

HIERARCHICAL SYSTEMS;

ACTION POTENTIAL; ANIMAL; ARTICLE; BRAIN MAPPING; CYTOLOGY; HUMAN; LEARNING; METHODOLOGY; NERVE CELL; ORIENTATION; PATTERN RECOGNITION; PHOTOSTIMULATION; PHYSIOLOGY; PSYCHOLOGICAL MODEL; VISUAL CORTEX; VISUAL SYSTEM;

ACTION POTENTIALS; ANIMALS; BRAIN MAPPING; HUMANS; LEARNING; MODELS, PSYCHOLOGICAL; NEURONS; ORIENTATION; PATTERN RECOGNITION, VISUAL; PHOTIC STIMULATION; VISUAL CORTEX; VISUAL PATHWAYS;

EID: 70349249975     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/neco.2009.03-08-722     Document Type: Article

References (47)

1. Buchsbaum, G., & Bloch, O. (2002). Color categories revealed by non-negative matrix factorization of Munsell color spectra. Vision Research, 42, 559–563.
2. Cho, Y. C., & Choi, S. J. (2005). Nonnegative features of spectro-temporal sounds for classification. Pattern Recognition Letters, 26, 1327–1336.
3. Durbin, R., & Mitchison, G. (1990). A dimension reduction framework for understanding cortical maps. Nature, 343, 644–647.
4. Fujita, I. (2002). The inferior temporal cortex: Architecture, computation, and representation. Journal of Neurocytology, 31, 359–371.
5. Fujita, I., & Fujita, T. (1996). Intrinsic connections in the macaque inferior temporal cortex. Journal of Comparative Neurology, 368, 467–486.
6. Fujita, I., Tanaka, K., Ito, M., & Cheng, K. (1992). Columns for visual features of objects in monkey inferotemporal cortex. Nature, 360, 343–346.
7. Fukushima, K. (1980). Neocognitron: A self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36, 193–202.
8. Gawne, T. J., & Richmond, B. J. (1993). How independent are the messages carried by adjacent inferior temporal cortical neurons? Journal of Neuroscience, 13, 2758–2771.
9. Gochin, P. M., Colombo, M., Dorfman, G. A., Gerstein, G. L., & Gross, C. G. (1994). Neural ensemble coding in inferior temporal cortex. Journal of Neurophysiology, 71, 2325–2337.
10. Gross, C. G. (1994). How inferior temporal cortex became a visual area. Cerebral Cortex, 5, 455–469.
11. Heiler, M., & Schnörr, C. (2006). Learning sparse representations by non-negative matrix factorization and sequential cone programming. Journal of Machine Learning Research, 7, 1385–1407.
12. Hubel, D. H., & Wiesel, T. N. (1962). Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. Journal of Physiology, 160, 106– 154.
13. Hubel, D. H., & Wiesel, T. N. (1963). Shape and arrangement of columns in cat’s striate cortex. Journal of Physiology, 165, 559–568.
14. Hoyer, P. O. (2004). Non-negative matrix factorization with sparseness constraints. Journal of Machine Learning Research, 5, 1457–1469.
15. Hyvärinen, A., Hoyer, P. O., & Inki, M. (2001). Topographic independent component analysis. Neural Computation, 13, 1527–1558.
16. Ito, M., Fujita, I., Tamura, H., & Tanaka, K. (1994). Processing of contrast polarity of visual images in inferotemporal cortex of the macaque monkey. Cerebral Cortex, 5, 499–508.
17. Kohonen, T. (1988). Self-organization and associative memory. Berlin: Springer-Verlag.
18. Koulakov, A. A., & Chklovskii, D. B. (2001). Orientation preference patterns in mammalian visual cortex: A wire length minimization approach. Neuron, 29, 519–527.
19. Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401, 788–791.
20. Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. In T. K. Leen, T. G. Dietterich, & V. Tresp (Eds.), Advances in neural information processing systems, 13 (pp. 556–562). Cambridge, MA: MIT Press.
21. Li, S. Z., Hou, X., Zhang, H., & Cheng, Q. (2001). Learning spatially localized parts-based representations. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), USA (Vol. 1, 207–212). Piscataway, NJ: IEEE Press.
22. Mishkin, M., Ungerleider, E., & Macko, K. A. (1983). Object vision and spatial vision: Two cortical pathways. Trends in Neuroscience, 6, 414–417.
23. Obermayer, K., Ritter, H., & Schulten, K. (1990). A principle for the formation of the spatial structure of cortical feature maps. Proc. Natl. Acad. Sci. USA, 87, 8345– 8349.
24. Pasupathy, A., & Connor, C. E. (2002). Population coding of shape in area V4. Nature Neuroscience, 5, 1332–1338.
25. Pouget, A., Dayan, P., & Zemel, R. (2000). Information processing with population codes. Nature Review Neuroscience, 1, 125–132.
26. Rao, R. P. N., & Ballard, D. (1999). Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2, 79–87.
27. Riesenhuber, M., & Poggio, T. (1999). Hierarchical models of object recognition in cortex. Nature Neuroscience, 2, 1019–1025.
28. Sahani, M., & Dayan, P. (2003). Doubly distributional population codes: Simultaneous representation of uncertainty and multiplicity. Neural Computation, 15, 2255– 2279.
29. Schulz, R., & Reggia, J. A. (2004). Temporally asymmetric learning supports sequence processing in multi-winner self-organizing maps. Neural Computation, 16, 535– 561.
30. Schulz, R., & Reggia, J. A. (2005). Mirror symmetric topographic maps can arise from activity-dependent synaptic changes. Neural Computation, 17, 1059–1083.
31. Serre, T. (2006). Learning a dictionary of shape components in visual cortex: Comparison with neurons, humans and machines. Unpublished doctoral dissertation, Massachusetts Institute of Technology.
32. Serre, T., Kouh, M., Cadieu, C., Knoblich, U., Kreiman, G., & Poggio, T. (2005). Atheory of object recognition: Computations and circuits in the feedforward path of the ventral stream in primate visual cortex (CBCL Paper 259/AI Memo 2005–036). Cambridge, MA: Massachusetts Institute of Technology.
33. Swindale, N. V. (1991). Coverage and the design of striate cortex. Biological Cybernetics, 65, 415–424.
34. Tamura, H., Kaneko, H., & Fujita, I. (2005). Quantitative analysis of functional clustering of neurons in the macaque inferior temporal cortex. Neuroscience Research, 52, 311–322.
35. Tamura, H., Kaneko, H., Kawasaki, K., & Fujita, I. (2004). Presumed inhibitory neurons in the macaque inferior temporal cortex: Visual response properties and functional interactions with adjacent neurons. Journal of Neurophysiology, 91, 2782– 2796.
36. Tanaka, K. (2003). Columns for complex visual object features in the inferotemporal cortex: Clustering of cells with similar but slightly different stimulus selectivities. Cerebral Cortex, 13, 90–99.
37. Tanaka, K., Saito, H., Fukada, Y., & Moriya, M. (1991). Coding visual images of objects in the inferotemporal cortex of the macaque monkey. Journal of Neurophysiology, 66, 170–189.
38. Tanigawa, H., Wang, Q. X., & Fujita, I. (2005). Organization of horizontal axons in the inferior temporal cortex and primary visual cortex. Cerebral Cortex, 15, 1887–1899.
39. Tsunoda, K., Yamane, Y., Nishizaki, M., & Tanifuji, M. (2001). Complex objects are represented in macaque inferotemporal cortex by the combination of feature columns. Nature Neuroscience, 4, 832–838.
40. Wada, K., Kurata, K., & Okada, M. (2004). Self-organization of globally continuous and locally distributed information representation. Neural Networks, 17, 1039– 1049.
41. Wang, G., Tanifuji, M., & Tanaka, K. (1998). Functional architecture in monkey inferotemporal cortex revealed by in vivo optical imaging. Neuroscience Research, 32, 33–46.
42. Wang, Y., Fujita, I., & Murayama, Y. (2000). Neuronal mechanisms of selectivity for object features revealed by blocking inhibition in inferotemporal cortex. Nature Neuroscience, 3, 807–813.
43. Wersing, H., & Körner, E. (2003). Learning optimized features for hierarchical models of invariant object recognition. Neural Computation, 15, 1559–1588.
44. Xu, W., Liu, X., & Gong, Y. (2003). Document-clustering based on non-negative matrix factorization. In Proceedings of SIGIR 03 (pp. 267–273). New York: ACM.
45. Yu, H., Farley, B., Jin, D., & Sur, M. (2005). The coordinated mapping of visual space and response features in visual cortex. Neuron, 47, 267–280.
46. Zemel, R. S., Dayan, P., & Pouget, A. (1998). Probabilistic interpretation of population codes. Neural Computation, 10, 403–430.
47. Zoccolan, D., Kouh, M., Poggio, T., & DiCarlo, J. J. (2007). Trade-off between object selectivity and tolerance in monkey inferotemporal cortex. Journal of Neuroscience, 27, 12292–12307.