What is the optimal architecture for visual information routing?

Neural Computation

Volumn 19, Issue 12, 2007, Pages 3293-3309

  Wolfrum, Philipp ^a   Von Der Malsburg, Christoph ^a,b  

^a FRANKFURT INSTITUTE FOR ADVANCED STUDIES   (Germany)

^b University of Southern California ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ANIMAL; ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER SIMULATION; GENICULATE BODY; HUMAN; NERVE CELL NETWORK; PHYSIOLOGY; RETINA; SPECIES DIFFERENCE; VISION; VISUAL CORTEX; VISUAL SYSTEM;

ALGORITHMS; ANIMALS; COMPUTER SIMULATION; GENICULATE BODIES; HUMANS; NERVE NET; NEURAL NETWORKS (COMPUTER); RETINA; SPECIES SPECIFICITY; VISUAL CORTEX; VISUAL PATHWAYS; VISUAL PERCEPTION;

EID: 36849047377     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/neco.2007.19.12.3293     Document Type: Article

References (27)

1. Connor, C. E., Gallant, J. L., Preddie, D. C., & van Essen, D. C. (1993). Responses in area v4 depend on the spatial relationship between stimulus and attention. Journal of Neurophysiology, 75, 1306-1308.
2. Cooley, J. W., & Connor, J. W. C. E. (1965). An algorithm for the machine calculation of complex Fourier series. Mathematics of Computation, 19, 297-301.
3. Cox, D., Meier, P., Oertelt, N., & DiCarlo, J. J. (2005). "Breaking" position-invariant object recognition. Nature Neuroscience, 8(9), 1145-1147.
4. Dill, M., & Fahle, M. (1998). Limited translation invariance of human visual pattern recognition. Perception and Psychophysics, 60(1), 65-81.
5. Dougherty, R. F., Koch, V. M., Brewer, A. A., Fischer, B., Modersitzki, J., & Wandell, B. A. (2003). Visual field representations and locations of visual areas Vl/2/3 in human visual cortex, journal of Vision, 3, 586-598.
6. Fukuda, Y., Sugimoto, T., & Shirokawa, T. (1982). Strain differences in quantitative analysis of rat optic nerve. Experimental Neurology, 75, 525-532.
7. Hughes, A., & Wässle, H. (1976). The cat optic nerve: Fibre total count and diameter spectrum. Journal of Comparative Neurology, 169, 171-184.
8. Lades, M., Vorbrüggen, J., Buhmann, J., Lange, J., von der Malsburg, C., Würtz, R., & Konen, W. (1993). Distortion invariant object recognition in the dynamic link architecture. IEEE Transactions on Computers, 42, 300-311.
9. Lücke, J. (2005). Dynamics of cortical columns-sensitive decision making. In Proc. ICANN, LNCS 3696 (pp. 25-30). New York: Springer.
10. Lücke, J., & von der Malsburg, C. (2004). Rapid processing and unsupervised learning in a model of the cortical macrocolumn. Neural Computation, 16, 501-533.
11. Moran, J., & Desimone, R. (1985). Selective attention gates visual processing in the extrastriate cortex. Science, 229, 782-784.
12. Murray, S. O., Boyaci, H., & Kersten, D. (2006). The representation of perceived angular size in human primary visual cortex. Nature Neuroscience, 9, 429-434.
13. Obermayer, K., & Blasdel, G. G. (1997). Singularities in primate orientation maps. Neural Computation, 9, 555-575.
14. Olshausen, B. A., Anderson, C. H., & van Essen, D. C. (1993). A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information, journal of Neuroscience, 23(11), 4700-4719.
15. Olshausen, B. A., & Field, D. J. (1997). Sparse coding with an overcomplete basis set: A strategy employed by V1? Vision Research, 37, 3311-3325.
16. Oram, M. W., & Perret, D. I. (1994). Modeling visual recognition from neurobiological constraints. Neural Networks, 7, 945-972.
17. Perkel, D. J., Bullier, J., & Kennedy, H. (1986). Topography of the afferent connectivity of area 17 in the macaque monkey: A double-labelling study. Journal of Comparative Neurology, 253, 374-402.
18. Pitts, W., & McCulloch, W. S. (1947). How we know universals: The perception of auditory and visual forms. Bulletin of Matliematical Biophysics, 9, 127-147.
19. Postma, E. O., van den Herik, H. J., & Hudson, P. T. W. (1997). Scan: A scalable neural model of covert attention. Neural Networks 10(6), 993-1015.
20. Potts, A. M., Hodges, D., Shelman, C. B., Fritz, K. J., Levy, N. S., & Mangnall, Y. (1972). Morphology of the primate optic nerve. I. Method and total fiber count. Investigative Ophthalmology and Visual Science, 11, 980-988.
21. Rager, G., & Rager, U. (1978). Systems-matching by degeneration I. A quantitative electron microscopic study of the generation and degeneration of retinal ganglion cells in the chicken. Experimental Brain Research, 33,65-78.
22. Schwartz, E. L. (1977). Spatial mapping in primate sensory projection: Analytic structure and relevance to perception. Biological Cybernetics, 25, 181-194.
23. Tanaka, K., Fujita, I., Kobatake, E., Cheng, K., & Ito, M. (1993). Serial processing of visual object-features in the posterior and anterior parts of the inferotemporal cortex. In T. Ono, L. R. Squire, M. E. Raichle, D. Perrett, & M. Fukuda (Eds.), Brain mechanisms of perception and memory: From neuron to behaviour (pp. 34-46). New York: Oxford University Press.
24. Tanigawa, H., Wang, Q., & Fujita, I. (2005). Organization of horizontal axons in the inferior temporal cortex and primary visual cortex of the macaque monkey. Cerebral Cortex, 15, 1887-1899.
25. van Essen, D. C., Olshausen, B., Anderson, C. H., & Gallant, J. (1991). Pattern recognition, attention, and information bottlenecks in the primate visual system. In B. Mathur & C. Koch (Eds.), Proceedings of the SPIE Conference on Visual Information Processing: From Neurons to Chips (Vol. 1473, pp. 17-28.). Bellingham, WA: SPIE.
26. Wundrich, I. J., von der Malsburg, C., & Würtz, R. P. (2004). Image representation by complex cell responses. Neural Computation, 16(12), 2563-2575.
27. Zhu, J., & von der Malsburg, C. (2004). Maplets for correspondence-based object recognition. Neural Networks, 27(8-9), 1311-1326.