Amari S, Cichocki A. (2003). Adaptive blind signal and image processing: Learning algorithms and applications.

Bell AJ, Sejnowski TJ. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural computation. 7 [PubMed]

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

Bollerslev T, Engle RF, Nelson DB. (1994). ARCH models Handbook of Econometrics.

Buccigrossi RW, Simoncelli EP. (1999). Image compression via joint statistical characterization in the wavelet domain. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 8 [PubMed]

Cardoso JF. (1997). Infomax and maximum likelihood for blind source separation IEEE Signal Processing Letters. 4

Cardoso JF, Pham DT. (2001). Blind separation of instantaneous mixtures of nonstationary sources IEEE Trans On Signal Processing. 49

Choi S, Cichocki A, Belouchrani A. (2002). Second order nonstationary source separation J VLSI Signal Process. 32

Foldiak P. (1991). Learning invariance from transformation sequences Neural Comput. 3

Hinton G, Welling M, Osindero S. (2003). Learning sparse topographic representations with products of Student-t distributions Advances in neural information processing systems 12. 15

Hoyer PO, Hyvärinen A. (2002). A multi-layer sparse coding network learns contour coding from natural images. Vision research. 42 [PubMed]

Hurri J, Hyvärinen A. (2003). Simple-cell-like receptive fields maximize temporal coherence in natural video. Neural computation. 15 [PubMed]

Hyvärinen A, Hoyer P. (2000). Emergence of phase- and shift-invariant features by decomposition of natural images into independent feature subspaces. Neural computation. 12 [PubMed]

Hyvärinen A, Hoyer PO, Inki M. (2001). Topographic independent component analysis. Neural computation. 13 [PubMed]

Hyvärinen A, Hurri J, Väyrynen J. (2003). Bubbles: a unifying framework for low-level statistical properties of natural image sequences. Journal of the Optical Society of America. A, Optics, image science, and vision. 20 [PubMed]

Karklin Y, Lewicki MS. (2003). Learning higher-order structures in natural images. Network (Bristol, England). 14 [PubMed]

Konig P, Kayser C, Einhauser W, Dummer O, Kording K. (2001). Extracting slow subspaces from natural videos leads to complex cells Artificial Neural Networks. 2130

Kruger N. (1998). Collinearity and parallelism are statistically significant second order relations of complex cell responses Neural Processing Letters. 8

Lecun Y, Orr G, Muller KR, Bottou L. (1998). Efficient backprop Neural networks: Tricks of the trade, LNCS 1524.

Lee TW, Koehler B, Orglmeister R. (1997). Blind separation of nonlinear mixing models IEEE International Workshop on Neural Networks for Signal Processing.

Lewicki MS, Lee TW. (2002). Unsupervised classification, segmentation and de-noising of images using ICA mixture models IEEE Trans Image Proc. 11

ONeill JC. (1999). Discrete TFDs time-frequency analysis software Available online at: http:--tfd.sourceforge.net-.

Oja E, Hyvarinen A, Karunen J. (2001). Independent component analysis.

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

Parra LC, Pearlmutter BA. (1996). A context-sensitive generalization of ICA International Conference on Neural Information Processing.

Roberts S, Everson R. (1999). Non-stationary independent component analysis Proceedings of the 8th International Conference on Artificial Neural Networks.

Romberg JK, Choi H, Baraniuk RG. (2001). Bayesian tree-structured image modeling using wavelet-domain hidden Markov models. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 10 [PubMed]

Schwartz O, Simoncelli EP. (2001). Natural signal statistics and sensory gain control. Nature neuroscience. 4 [PubMed]

Sejnowski TJ, Lewicki MS, Lee TW. (2000). ICA mixture models for unsupervised classification of non-gaussian classes and automatic context switching in blind signal separation IEEE Trans On Pattern Analysis An Machine Int. 22

Simoncelli E. (1997). Statistical models for images: Compression, restoration and synthesis Proceedings of the 31st Asilomar Conference on Signals, Systems and Computers.

Simoncelli EP, Wainwright MJ, Willsky AS. (2000). Random cascades of gaussian scale mixtures and their use in modelingnatural images with application to denoising Proceedings of the 7th International Conference on Image Processing.

Wiskott L, Sejnowski TJ. (2002). Slow feature analysis: unsupervised learning of invariances. Neural computation. 14 [PubMed]

van Hateren JH, van der Schaaf A. (1998). Independent component filters of natural images compared with simple cells in primary visual cortex. Proceedings. Biological sciences. 265 [PubMed]

Osindero S, Welling M, Hinton GE. (2005). Topographic Product Models Applied to Natural Scene Statistics Neural Comput. 18

Schwartz O, Sejnowski TJ, Dayan P. (2006). Soft mixer assignment in a hierarchical generative model of natural scene statistics. Neural computation. 18 [PubMed]

Turner R, Sahani M. (2007). A maximum-likelihood interpretation for slow feature analysis. Neural computation. 19 [PubMed]