Invariance in visual object recognition requires training: A computational argument

Frontiers in Neuroscience

Volumn 4, Issue MAY, 2010, Pages

  Goris, Robbe L T ^a   Op de Beeck, Hans P ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

Inferior temporal cortex; Invariance; Object recognition; Population coding; Training

Indexed keywords

EID: 84861458587     PISSN: 16624548     EISSN: 1662453X     Source Type: Journal    
DOI: 10.3389/neuro.01.012.2010     Document Type: Review

References (31)

1. Barlow, H. B. (1972). Single units and sensation: a neuron doctrine for perceptual psychology? Perception 1, 371-394.
2. Britten, K. H., Shadlen, M. N., Newsome, W. T., and Movshon, J. A. (1992). The analysis of visual motion: a comparison of neuronal and psychophysical performance. J. Neurosci. 12, 4745-4765.
3. Duda, R. O., Hart, P. E., and Stork, D. G. (2001). Pattern Classification. New York, John Wiley & Sons, Inc.
4. Geisler, W. S. (2003). Ideal observer analysis. In The Visual Neurosciences, L. Chalupa and J. Werner, eds (Boston, MIT press), pp. 825-837.
5. Goris, R. L. T., and Op de Beeck, H. P. (2009). Neural representations that support invariant object recognition. Front. Comput. Neurosci. 3, 1-16. doi:10.3389/neuro.10.003.2009.
6. Goris, R. L. T., Wichmann, F. A., and Henning, G. B. (2009). A neurophysiologically plausible population code model for human contrast discrimination. J. Vis. 9, 1-22.
7. Green, D. M., and Swets, J. A. (1966). Signal Detection Theory and Psychophysics. New York, John Wiley & Sons, Inc.
8. Hung, C. P., Kreiman, G., Poggio, T., and DiCarlo, J. J. (2005). Fast readout of object identity from macaque inferior temporal cortex. Science 310, 863-866.
9. Ito, M., Tamura, H., Fujita, I., and Tanaka, K. (1995). Size and position invariance of neuronal responses in monkey inferotemporal cortex. J. Neurophysiol. 73, 218-226.
10. Jäkel, F., Schölkopf, B., and Wichmann, F. A. (2009). Does cognitive science need kernels? Trends Cogn. Sci. 13, 381-388.
11. Jazayeri, M., and Movshon, J. A. (2006). Optimal representation of sensory information by neural populations. Nat. Neurosci. 9, 690-696.
12. Jin, D. Z., Dragoi, V., Sur, M., and Seung, H. S. (2005). Tilt aftereffect and adaptation-induced changes in orientation tuning in visual cortex. J. Neurophysiol. 94, 4038-4050.
13. Kravitz, D. J., Vinson, L. D., and Baker, C. I. (2008). How position dependent is visual object recognition? Trends Cogn. Sci. 12, 114-122.
14. Li, N., Cox, D. D., Zoccolan, D., and DiCarlo, J. J. (2009). What response properties do individual neurons need to underlie position and clutter "invariant" object recognition? J. Neurophysiol. 102, 360-376.
15. Li, N., and DiCarlo, J. J. (2008). Unsupervised natural experience rapidly alters invariant object representation in visual cortex. Science 321, 1502-1507.
16. Logothetis, N. K., and Pauls, J. (1995). Psychophysical and physiological evidence for viewer-centered object representations in the primate. Cereb. Cortex 5, 270-288.
17. Logothetis, N. K., Pauls, J., Bülthoff, H. H., and Poggio, T. (1994). View-dependent object recognition by monkeys. Curr. Biol. 4, 401-414.
18. Logothetis, N. K., Pauls, J., and Poggio, T. (1995). Shape representation in the inferior temporal cortex of monkeys. Curr. Biol. 5, 552-563.
19. McKone, E. (2009). Holistic processing for faces operates over a wide range of sizes but is strongest at identification rather than conversational distances. Vision. Res. 49, 268-283.
20. Minini, L., and Jeffery, K. J. (2006). Do rats use shape to solve "shape discriminations"? Learn. Mem. 13, 287-297.
21. Op de Beeck, H. P., and Vogels, R. (2000). Spatial sensitivity of macaque inferior temporal neurons. J. Comp. Neurol. 426, 505-518.
22. Peissig, J. J., and Tarr, M. J. (2007). Visual object recognition: do we know more now than we did 20 years ago? Annu. Rev. Psychol. 58, 75-96.
23. Perrett, D. I., Oram, M. W., and Ashbridge, E. (1998). Evidence accumulation in cell populations responsive to faces: an account of generalisation of recognition without mental transformations. Cognition 67, 111-145.
24. Pinto, N., Cox, D. D., and DiCarlo, J. J. (2008). Why is real-world visual object recognition hard? PLoS Comput. Biol. 4, e27.
25. Pouget, A., Dayan, P., and Zemel, R. (2000). Information processing with population codes. Nat. Rev. Neurosci. 1, 125-132.
26. Riesenhuber, M., and Poggio, T. (1999). Hierarchical models of object recognition in cortex. Nat. Neurosci. 2, 1019-1025.
27. Serre, T., Oliva, A., and Poggio, T. (2007). A feedforward architecture accounts for rapid categorization. Proc. Natl. Acad. Sci. U.S.A. 104, 6424-6429.
28. Vogels, R., and Orban, G. A. (1996). Coding of stimulus invariances by inferior temporal neurons. Prog. Brain Res. 112, 195-211.
29. Zoccolan, D., Kouh, M., Poggio, T., and DiCarlo, J. J. (2007). Trade-off between object selectivity and tolerance in monkey inferotemporal cortex. J. Neurosci. 27, 12292-12307.
30. Zoccolan, D., Oertelt, N., DiCarlo, J. J., and Cox, D. D. (2009). A rodent model for the study of invariant visual object recognition. Proc. Natl. Acad. Sci. U.S.A. 106, 8748-8753.
31. Zohary, E., Shadlen, M. N., and Newsome, W. T. (1994). Correlated neuronal discharge rate and its implications for psychophysical performance. Nature 370, 140-143.