Learning spatial invariance with the trace rule in nonuniform distributions

Neural Computation

Volumn 25, Issue 5, 2013, Pages 1261-1276

  Leveillé, Jasmin ^a   Hannagan, Thomas ^b  

^a BOSTON UNIVERSITY   (United States)

^b AIX MARSEILLE UNIVERSITÉ   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; BIOLOGICAL MODEL; HUMAN; LEARNING; LETTER; PHYSIOLOGY; THEORETICAL MODEL; VISION;

ALGORITHMS; HUMANS; LEARNING; MODELS, NEUROLOGICAL; MODELS, THEORETICAL; VISUAL PERCEPTION;

EID: 84877801406     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/NECO_a_00435     Document Type: Letter

References (25)

1. Betsch, B. Y., Einhauser,W., Kording, K. P., & Konig, P. (2004). The world from a cat's perspective: Statistics of natural videos. Biol. Cybern., 90, 41-50.
2. Boureau, Y., Ponce, J., & LeCun, Y. (2010). A theoretical analysis of feature pooling in visual recognition. In Proceedings of the International Conference onMachine Learning (pp. 111-118). New York: ACM Press.
3. Cadieu, C., Kouh, M., Pasupathy, A., Connor, C. E., Riesenhuber, M., & Poggio, T. (2007). A model of V4 shape selectivity and invariance. Journal of Neurophysiology, 98, 1733-1750.
4. Caponnetto, A., Poggio, T., & Smale, S. (2008). On a model of visual cortex: Learning invariance and selectivity from image sequences (Tech. Rep. MIT-CSAIL-TR-2008-030).
5. Cooper, L. N., Intrator, N., & Shouval, H. Z. (2004). Theory of cortical plasticity. Singapore: World Scientific.
6. Földiák, P. (1991). Learning invariance from transformation sequences. Neural Comput., 3, 194-200.
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, 193-201.
8. Hannagan, T., Dandurand, F., & Grainger, J. (2011). Broken symmetries in a location invariant word recognition network. Neural Comput., 23, 251-283.
9. Huang, F. J., Boureau, Y., & LeCun, Y. (2007). Unsupervised learning of invariant feature hierarchies with applications to object recognition. In Proc.ComputerVision and Pattern Recognition Conference. Piscataway, NJ: IEEE.
10. Knoblich, U., Bouvrie, J., & Poggio, T. (2007). Biophysical models of neural computation: Max and tuning circuits. In Proc. 1st WICI International Conference on Web intelligence meets brain informatics (pp. 164-189). Berlin: Springer.
11. LeCun, Y., Bottou, L., & Haffner, P. (1998). Gradient based learning applied to document recognition. Proceedings of the IEEE, 86, 2278-2324.
12. Masquelier, T., Serre, T., Thorpe, S., & Poggio, T. (2007). Learning complex cell invariance from natural videos: A plausibility proof (Tech. Rep. MIT-CSAIL-TR-2007-060). Cambridge, MA: MIT.
13. Maurer, A. (2006). Unsupervised slow-subspace learning from stationary processes. In Proceedings of the 17th International Conference on Algorithmic Learning Theory (pp. 363-377). New York: Springer.
14. Oja, E. (1982). Simplified neuron model as a principal component analyzer. Journal of Mathematical Biology, 15, 267-273.
15. Pinto, N., Cox, D., & DiCarlo, J. (2008). Why is real-world visual object recognition hard. PLoS Computational Biology, 4, e27.
16. Riesenhuber, M., & Poggio, T. (1999). Hierarchical models of object recognition in cortex. Nature Neuroscience, 2, 1019-1025.
17. Rolls, E. T., & Stringer, S. M. (2001). Invariant object recognition in the visual system with error correction and temporal difference learning. Network: Computation in Neural Systems, 12, 111-129.
18. Serre, T.,Oliva, A.,&Poggio, T. (2007).Afeedforward architecture accounts for rapid categorization. Proceedings of the National Academy of Sciences, 104, 6424-6429.
19. Shawe-Taylor, J. (1993). Symmetries and discriminability in feedforward network architectures. IEEE Transactions on Neural Networks, 4, 816-826.
20. Sprekeler, H., Michaelis, C., & Wiskott, L. (2007). Slowness: An objective for spiketiming- dependent plasticity?. PLOS: Computational Biology, 3, 1136-1148.
21. Sutton, R. S., & Barto, A. G. (1981). Toward a modern theory of adaptive networks: Expectation and prediction. Psychological Review, 88, 135-170.
22. Wallis, G., & Rolls, E. T. (1997). Invariant face and object recognition in the visual system. Progress in Neurobiology, 51, 167-194.
23. Webber, C.J.S. (2000). Self-organization of symmetry networks: Transformation invariance from the spontaneous symmetry-breaking mechanism. Neural Comput., 12, 565-596.
24. Wiskott, L., & Sejnowski, T. (2002). Slow feature analysis: Unsupervised learning of invariances. Neural Comput., 14, 715-770.
25. Yu, A. J., Giese, M. A., & Poggio, T. (2002). Biophysiologically plausible implementations of the maximum operation. In T. G. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Advances in neural information processing systems, 14 (pp. 2857-2881). Cambridge, MA: MIT Press.