Spatiochromatic Receptive Field Properties Derived from Information-Theoretic Analyses of Cone Mosaic Responses to Natural Scenes
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Terrence J. Sejnowski | Te-Won Lee | Toshio Inui | Thomas Wachtler | Eizaburo Doi | T. Sejnowski | Te-Won Lee | T. Wachtler | E. Doi | T. Inui
[1] G. F. Cooper,et al. Development of the Brain depends on the Visual Environment , 1970, Nature.
[2] C. Blakemore,et al. Innate and environmental factors in the development of the kitten's visual cortex. , 1975, The Journal of physiology.
[3] C. R. Michael. Color vision mechanisms in monkey striate cortex: dual-opponent cells with concentric receptive fields. , 1978, Journal of neurophysiology.
[4] C. R. Ingling,et al. The relationship between spectral sensitivity and spatial sensitivity for the primate r-g X-channel , 1983, Vision Research.
[5] S. Schein,et al. Density profile of blue-sensitive cones along the horizontal meridian of macaque retina. , 1985, Investigative ophthalmology & visual science.
[6] K. Mullen. The contrast sensitivity of human colour vision to red‐green and blue‐yellow chromatic gratings. , 1985, The Journal of physiology.
[7] D. Baylor,et al. Spectral sensitivity of cones of the monkey Macaca fascicularis. , 1987, The Journal of physiology.
[8] Á. Szél,et al. Identification of the blue‐sensitive cones in the mammalian retina by anti‐visual pigment antibody , 1988, The Journal of comparative neurology.
[9] P. Lennie,et al. Mechanisms of color vision. , 1988, Critical reviews in neurobiology.
[10] D. Hubel,et al. Segregation of form, color, movement, and depth: anatomy, physiology, and perception. , 1988, Science.
[11] P. Lennie,et al. Chromatic mechanisms in striate cortex of macaque , 1990, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[12] V. Billock. The relationship between simple and double opponent cells , 1991, Vision Research.
[13] J. Mollon,et al. The spatial arrangement of cones in the primate fovea , 1992, Nature.
[14] Joseph J. Atick,et al. Convergent Algorithm for Sensory Receptive Field Development , 1993, Neural Computation.
[15] R. L. Valois,et al. A multi-stage color model , 1993, Vision Research.
[16] Ron Gershon,et al. Measurement and Analysis of Object Reflectance Spectra , 1994 .
[17] David J. Field,et al. What Is the Goal of Sensory Coding? , 1994, Neural Computation.
[18] Terrence J. Sejnowski,et al. An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.
[19] K. Mullen,et al. Separating colour and luminance information in the visual system. , 1995, Spatial vision.
[20] Andrzej Cichocki,et al. A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.
[21] Gene H. Golub,et al. Matrix computations (3rd ed.) , 1996 .
[22] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[23] J. Cardoso. Infomax and maximum likelihood for blind source separation , 1997, IEEE Signal Processing Letters.
[24] Terrence J. Sejnowski,et al. The “independent components” of natural scenes are edge filters , 1997, Vision Research.
[25] J. V. van Hateren,et al. Independent component filters of natural images compared with simple cells in primary visual cortex , 1998, Proceedings of the Royal Society of London. Series B: Biological Sciences.
[26] Te-Won Lee,et al. Independent Component Analysis , 1998, Springer US.
[27] J. H. Hateren,et al. Independent component filters of natural images compared with simple cells in primary visual cortex , 1998 .
[28] D. Ruderman,et al. Statistics of cone responses to natural images: implications for visual coding , 1998 .
[29] D. Teller. Spatial and temporal aspects of infant color vision , 1998, Vision Research.
[30] Terrence J. Sejnowski,et al. Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources , 1999, Neural Computation.
[31] Mark A. Girolami,et al. Self-Organising Neural Networks: Independent Component Analysis and Blind Source Separation , 1999 .
[32] Mark Girolami,et al. Self-Organising Neural Networks , 1999 .
[33] P O Hoyer,et al. Independent component analysis applied to feature extraction from colour and stereo images , 2000, Network.
[34] A. Stockman,et al. The spectral sensitivities of the middle- and long-wavelength-sensitive cones derived from measurements in observers of known genotype , 2000, Vision Research.
[35] H. Komatsu,et al. Neural selectivity for hue and saturation of colour in the primary visual cortex of the monkey , 2000, The European journal of neuroscience.
[36] R. L. Valois,et al. Some transformations of color information from lateral geniculate nucleus to striate cortex. , 2000, Proceedings of the National Academy of Sciences of the United States of America.
[37] D. Dacey. Parallel pathways for spectral coding in primate retina. , 2000, Annual review of neuroscience.
[38] L. Finkel,et al. Color-opponent receptive fields derived from independent component analysis of natural images , 2000, Vision Research.
[39] T. W. Lee,et al. Chromatic structure of natural scenes. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.
[40] Bevil R. Conway,et al. Spatial Structure of Cone Inputs to Color Cells in Alert Macaque Primary Visual Cortex (V-1) , 2001, The Journal of Neuroscience.
[41] K. Knoblauch,et al. Variation of chromatic sensitivity across the life span , 2001, Vision Research.
[42] R. Shapley,et al. The spatial transformation of color in the primary visual cortex of the macaque monkey , 2001, Nature Neuroscience.
[43] Erkki Oja,et al. Independent Component Analysis , 2001 .
[44] T. Sejnowski,et al. Color opponency is an efficient representation of spectral properties in natural scenes , 2002, Vision Research.
[45] T. Wachtler,et al. Modeling color percepts of dichromats , 2004, Vision Research.