Statistics of Natural Stimuli? How Are Complex Cell Properties Adapted to the

[1]  P. Schiller,et al.  Quantitative studies of single-cell properties in monkey striate cortex. III. Spatial frequency. , 1976, Journal of neurophysiology.

[2]  H. B. Barlow,et al.  Possible Principles Underlying the Transformations of Sensory Messages , 2012 .

[3]  Konrad P. Körding,et al.  Extracting Slow Subspaces from Natural Videos Leads to Complex Cells , 2001, ICANN.

[4]  L. Abbott,et al.  Responses of neurons in primary and inferior temporal visual cortices to natural scenes , 1997, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[5]  J. H. Hateren,et al.  Independent component filters of natural images compared with simple cells in primary visual cortex , 1998 .

[6]  E H Adelson,et al.  Spatiotemporal energy models for the perception of motion. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[7]  A. B. Bonds,et al.  Classifying simple and complex cells on the basis of response modulation , 1991, Vision Research.

[8]  J L Gallant,et al.  Sparse coding and decorrelation in primary visual cortex during natural vision. , 2000, Science.

[9]  J. Hertz,et al.  Insensitivity of V1 complex cell responses to small shifts in the retinal image of complex patterns. , 1997, Journal of neurophysiology.

[10]  I. Ohzawa,et al.  Encoding of binocular disparity by complex cells in the cat's visual cortex. , 1996, Journal of neurophysiology.

[11]  Terrence J. Sejnowski,et al.  Slow Feature Analysis: Unsupervised Learning of Invariances , 2002, Neural Computation.

[12]  Aapo Hyvärinen,et al.  Simple-Cell-Like Receptive Fields Maximize Temporal Coherence in Natural Video , 2003, Neural Computation.

[13]  R. L. de Valois,et al.  Relationship between spatial-frequency and orientation tuning of striate-cortex cells. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[14]  P. Schiller,et al.  Quantitative studies of single-cell properties in monkey striate cortex. II. Orientation specificity and ocular dominance. , 1976, Journal of neurophysiology.

[15]  D. Heeger.  Half-squaring in responses of cat striate cells , 1992, Visual Neuroscience.

[16]  E. Rolls,et al.  INVARIANT FACE AND OBJECT RECOGNITION IN THE VISUAL SYSTEM , 1997, Progress in Neurobiology.

[17]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[18]  Suzanna Becker,et al.  Implicit Learning in 3D Object Recognition: The Importance of Temporal Context , 1999, Neural Computation.

[19]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[20]  James V. Stone,et al.  Temporal Constraints on Visual Learning: A Computational Model , 1999, Perception.

[21]  H. Spitzer,et al.  Simple- and complex-cell response dependences on stimulation parameters. , 1985, Journal of neurophysiology.

[22]  Terrence J. Sejnowski,et al.  The “independent components” of natural scenes are edge filters , 1997, Vision Research.

[23]  J. Movshon,et al.  Receptive field organization of complex cells in the cat's striate cortex. , 1978, The Journal of physiology.

[24]  Frances S. Chance,et al.  Complex cells as cortically amplified simple cells , 1999, Nature Neuroscience.

[25]  H. Spitzer,et al.  Complex-cell receptive field models , 1988, Progress in Neurobiology.

[26]  Christoph Kayser,et al.  Temporal Correlations of Orientations in Natural Scenes , 2002, Neurocomputing.

[27]  C. Fyfe,et al.  Finding compact and sparse-distributed representations of visual images , 1995 .

[28]  Aapo Hyvärinen,et al.  Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces , 2000, Neural Computation.