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Grimes DB, Rao RP (2005) Bilinear sparse coding for invariant vision. Neural Comput 17:47-73 [PubMed]

References and models cited by this paper

References and models that cite this paper

Anderson CH, Van Essen DC (1987) Shifter circuits: a computational strategy for dynamic aspects of visual processing. Proc Natl Acad Sci U S A 84:6297-301 [PubMed]

ATTNEAVE F (1954) Some informational aspects of visual perception. Psychol Rev 61:183-93 [PubMed]

Barlow HB (1961) Possible principles underlying the transformations of sensory messages Sensory Communication, Rosenblith WA, ed. pp.217

Bell AJ, Sejnowski TJ (1997) The "independent components" of natural scenes are edge filters. Vision Res 37:3327-38 [PubMed]

Besag J (1986) On the Statistical Analysis of Dirty Pictures J Roy Stat Soc B 48:259-302

Foldiak P (1991) Learning invariance from transformation sequences Neural Comput 3:194-200

Foldiak P, Young MP (1995) Sparse Coding in the Primate Cortex. The Handbook of Brain Theory and Neural Networks :895-898

Fukushima K, Miyake S, Ito T (1983) Neocognitron: A neural network model for a mechanism of visual pattern recognition IEEE Trans Systems, Man, and Cybernetics 13:826-834

Grimes DB, Rao RPN (2003) A bilinear model for sparse coding Advances in neural information processing systems, Becker S:Thrun S:Obermayer K, ed.

Hinton GE (1987) Learning translation invariant recognition in a massively parallel network PARLE: Parallel architectures and languages Europe , Goos G:Hartmanis J, ed. pp.1

Hinton GE, Ghahramani Z (1997) Generative models for discovering sparse distributed representations. Philos Trans R Soc Lond B Biol Sci 352:1177-90 [Journal] [PubMed]

Lecun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to handwritten zip code recognition Neural Comput 1:541-551

Lewicki MS, Sejnowski TJ (2000) Learning overcomplete representations. Neural Comput 12:337-65 [PubMed]

Olshausen BA, Field DJ (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381:607-9 [Journal] [PubMed]

Rao RP, Ballard DH (1998) Development of localized oriented receptive fields by learning a translation-invariant code for natural images. Network 9:219-34 [PubMed]

Rao RP, Ballard DH (1999) Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat Neurosci 2:79-87 [Journal] [PubMed]

Rao RPN, Ruderman DL (1999) Learning Lie groups for invariant visual perception Advances in neural information processing systems, Kearns MS:Solla S:Cohn D, ed. pp.810

Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323-6 [Journal] [PubMed]

Schwartz O, Simoncelli EP (2001) Natural signal statistics and sensory gain control. Nat Neurosci 4:819-25 [Journal] [PubMed]

Tenenbaum JB, Freeman WT (2000) Separating style and content with bilinear models. Neural Comput 12:1247-83 [PubMed]

Ungerleider LG, Mishkin M (1982) Two cortical visual systems Analysis of Visual Behavior, Ingle DJ, ed. pp.549

Wiskott L (2004) How does our visual system achieve shift and size invariance? Problems in systems neuroscience, van Hemmen JL:Sejnowski TJ, ed.

Wiskott L, Sejnowski TJ (2002) Slow feature analysis: unsupervised learning of invariances. Neural Comput 14:715-70 [Journal] [PubMed]

Dayan P (2006) Images, frames, and connectionist hierarchies. Neural Comput 18:2293-319 [Journal] [PubMed]

Hasler S, Wersing H, K├Ârner E (2007) Combining reconstruction and discrimination with class-specific sparse coding. Neural Comput 19:1897-918 [Journal] [PubMed]

Miao X, Rao RP (2007) Learning the Lie groups of visual invariance. Neural Comput 19:2665-93 [Journal] [PubMed]

Olshausen BA, Field DJ (2005) How close are we to understanding v1? Neural Comput 17:1665-99 [Journal] [PubMed]

Turner R, Sahani M (2007) A maximum-likelihood interpretation for slow feature analysis. Neural Comput 19:1022-38 [Journal] [PubMed]

Weber C, Wermter S (2006) A self-organizing map of sigma-pi units Neurocomputing 70(13-15):2552-2560 [Journal]

   Cortex learning models (Weber at al. 2006, Weber and Triesch, 2006, Weber and Wermter 2006/7) [Model]

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