Representation Learning in Sensory Cortex: A Theory
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[1] Bartlett W. Mel. SEEMORE: Combining Color, Shape, and Texture Histogramming in a Neurally Inspired Approach to Visual Object Recognition , 1997, Neural Computation.
[2] W. M. Keck,et al. Highly Selective Receptive Fields in Mouse Visual Cortex , 2008, The Journal of Neuroscience.
[3] D. C. Essen,et al. Neural responses to polar, hyperbolic, and Cartesian gratings in area V4 of the macaque monkey. , 1996, Journal of neurophysiology.
[4] Joel Z. Leibo,et al. Why The Brain Separates Face Recognition From Object Recognition , 2011, NIPS.
[5] Y. LeCun,et al. Learning methods for generic object recognition with invariance to pose and lighting , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..
[6] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[7] T. Poggio,et al. A network that learns to recognize three-dimensional objects , 1990, Nature.
[8] Joel Z. Leibo,et al. Learning invariant representations and applications to face verification , 2013, NIPS.
[9] S M Anstis,et al. Letter: A chart demonstrating variations in acuity with retinal position. , 1974, Vision research.
[10] Yaser S. Abu-Mostafa,et al. Hints and the VC Dimension , 1993, Neural Computation.
[11] D. Hubel,et al. Uniformity of monkey striate cortex: A parallel relationship between field size, scatter, and magnification factor , 1974, The Journal of comparative neurology.
[12] H. BOUMA,et al. Interaction Effects in Parafoveal Letter Recognition , 1970, Nature.
[13] T. Poggio,et al. Considerations on models of movement detection , 1973, Kybernetik.
[14] Edmund T. Rolls,et al. Invariant Object Recognition in the Visual System with Novel Views of 3D Objects , 2002, Neural Computation.
[15] N. Logothetis,et al. Shape representation in the inferior temporal cortex of monkeys , 1995, Current Biology.
[16] Nancy Kanwisher,et al. A cortical representation of the local visual environment , 1998, Nature.
[17] J. P. Jones,et al. An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. , 1987, Journal of neurophysiology.
[18] M. Tarr,et al. FFA: a flexible fusiform area for subordinate-level visual processing automatized by expertise , 2000, Nature Neuroscience.
[19] Tomaso A. Poggio,et al. Computational role of eccentricity dependent cortical magnification , 2014, ArXiv.
[20] M. Tarr. Rotating objects to recognize them: A case study on the role of viewpoint dependency in the recognition of three-dimensional objects , 1995, Psychonomic bulletin & review.
[21] Terence D. Sanger,et al. Optimal unsupervised learning in a single-layer linear feedforward neural network , 1989, Neural Networks.
[22] Tomaso Poggio,et al. Incorporating prior information in machine learning by creating virtual examples , 1998, Proc. IEEE.
[23] N. Logothetis,et al. View-dependent object recognition by monkeys , 1994, Current Biology.
[24] Tomaso Poggio,et al. Models of object recognition , 2000, Nature Neuroscience.
[25] Andrew Y. Ng,et al. Unsupervised learning models of primary cortical receptive fields and receptive field plasticity , 2011, NIPS.
[26] Frédéric Gosselin,et al. Diagnostic use of scale information for componential and holistic recognition. , 2003 .
[27] Doris Y. Tsao,et al. Functional Compartmentalization and Viewpoint Generalization Within the Macaque Face-Processing System , 2010, Science.
[28] Erkki Oja,et al. Principal components, minor components, and linear neural networks , 1992, Neural Networks.
[29] M. Potter. Meaning in visual search. , 1975, Science.
[30] N. Kanwisher,et al. A Cortical Area Selective for Visual Processing of the Human Body , 2001, Science.
[31] David G. Lowe,et al. Multiclass Object Recognition with Sparse, Localized Features , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[32] 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.
[33] Bruno A. Olshausen,et al. An Unsupervised Algorithm For Learning Lie Group Transformations , 2010, ArXiv.
[34] Stphane Mallat,et al. A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way , 2008 .
[35] N. Logothetis,et al. fMRI of the Face-Processing Network in the Ventral Temporal Lobe of Awake and Anesthetized Macaques , 2011, Neuron.
[36] Charles F Stevens. Preserving properties of object shape by computations in primary visual cortex. , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[37] Denis Fize,et al. Speed of processing in the human visual system , 1996, Nature.
[38] I. Rentschler,et al. Peripheral vision and pattern recognition: a review. , 2011, Journal of vision.
[39] Antonio Torralba,et al. Statistics of natural image categories , 2003, Network.
[40] D. Pelli,et al. The uncrowded window of object recognition , 2008, Nature Neuroscience.
[41] R. Vogels,et al. Spatial sensitivity of macaque inferior temporal neurons , 2000, The Journal of comparative neurology.
[42] Nicolas Pinto,et al. How far can you get with a modern face recognition test set using only simple features? , 2009, CVPR.
[43] J. Maunsell,et al. Anterior inferotemporal neurons of monkeys engaged in object recognition can be highly sensitive to object retinal position. , 2003, Journal of neurophysiology.
[44] Rob Fergus,et al. Visualizing and Understanding Convolutional Networks , 2013, ECCV.
[45] Erkki Oja,et al. Independent component analysis: algorithms and applications , 2000, Neural Networks.
[46] M. Tarr,et al. Becoming a “Greeble” Expert: Exploring Mechanisms for Face Recognition , 1997, Vision Research.
[47] D. Schacter,et al. On the nature of medial temporal lobe contributions to the constructive simulation of future events , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.
[48] R. Malach,et al. Object-related activity revealed by functional magnetic resonance imaging in human occipital cortex. , 1995, Proceedings of the National Academy of Sciences of the United States of America.
[49] P. H. Schiller,et al. Spatial frequency and orientation tuning dynamics in area V1 , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[50] Tomaso A. Poggio,et al. A Canonical Neural Circuit for Cortical Nonlinear Operations , 2008, Neural Computation.
[51] D. Marr,et al. Smallest channel in early human vision. , 1980, Journal of the Optical Society of America.
[52] Thomas Serre,et al. Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[53] C. Gross,et al. Visual topography of V2 in the macaque , 1981, The Journal of comparative neurology.
[54] E. Oja. Simplified neuron model as a principal component analyzer , 1982, Journal of mathematical biology.
[55] D. Ruderman. The statistics of natural images , 1994 .
[56] R. Rosenholtz,et al. A summary statistic representation in peripheral vision explains visual search. , 2009, Journal of vision.
[57] Gerald Penn,et al. Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[58] 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.
[59] Doris Y. Tsao,et al. Faces and objects in macaque cerebral cortex , 2003, Nature Neuroscience.
[60] Thomas Serre,et al. A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex , 2005 .
[61] M. Bar,et al. Scenes Unseen: The Parahippocampal Cortex Intrinsically Subserves Contextual Associations, Not Scenes or Places Per Se , 2008, The Journal of Neuroscience.
[62] R. C. Tees. Review of The organization of behavior: A neuropsychological theory. , 2003 .
[63] Juha Karhunen,et al. Stability of Oja's PCA Subspace Rule , 1994, Neural Computation.
[64] D. V. van Essen,et al. Selectivity for polar, hyperbolic, and Cartesian gratings in macaque visual cortex. , 1993, Science.
[65] David L. Sheinberg,et al. Visual object recognition. , 1996, Annual review of neuroscience.
[66] A. Cowey,et al. Human cortical magnification factor and its relation to visual acuity , 2004, Experimental Brain Research.
[67] H H Bülthoff,et al. Psychophysical support for a two-dimensional view interpolation theory of object recognition. , 1992, Proceedings of the National Academy of Sciences of the United States of America.
[68] S Lehéricy,et al. The visual word form area: spatial and temporal characterization of an initial stage of reading in normal subjects and posterior split-brain patients. , 2000, Brain : a journal of neurology.
[69] Doris Y. Tsao,et al. Mechanisms of face perception. , 2008, Annual review of neuroscience.
[70] Stéphane Mallat,et al. Group Invariant Scattering , 2011, ArXiv.
[71] D. Hubel,et al. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.
[72] D. Heeger. Normalization of cell responses in cat striate cortex , 1992, Visual Neuroscience.
[73] Marc'Aurelio Ranzato,et al. Building high-level features using large scale unsupervised learning , 2011, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[74] Eero P. Simoncelli,et al. Metamers of the ventral stream , 2011, Nature Neuroscience.
[75] A. Heppes. On the determination of probability distributions of more dimensions by their projections , 1956 .
[76] D. Ringach. Spatial structure and symmetry of simple-cell receptive fields in macaque primary visual cortex. , 2002, Journal of neurophysiology.
[77] S. Nelson,et al. Homeostatic plasticity in the developing nervous system , 2004, Nature Reviews Neuroscience.
[78] Dennis Gabor,et al. Theory of communication , 1946 .
[79] Ronen Basri,et al. Recognition by Linear Combinations of Models , 1991, IEEE Trans. Pattern Anal. Mach. Intell..
[80] Peter Földiák,et al. Learning Invariance from Transformation Sequences , 1991, Neural Comput..
[81] D. Levi. Crowding—An essential bottleneck for object recognition: A mini-review , 2008, Vision Research.
[82] Stefano Soatto,et al. Video-based descriptors for object recognition , 2011, Image Vis. Comput..
[83] David I. Perrett,et al. Neurophysiology of shape processing , 1993, Image Vis. Comput..
[84] Lawrence D. Jackel,et al. Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.
[85] D. Levi,et al. The effect of flankers on three tasks in central, peripheral, and amblyopic vision. , 2011, Journal of vision.
[86] C. Gross,et al. Visuotopic organization and extent of V3 and V4 of the macaque , 1988, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[87] Tomaso Poggio,et al. Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning? , 2013, 1311.4158.
[88] J. Hegdé,et al. Selectivity for Complex Shapes in Primate Visual Area V2 , 2000, The Journal of Neuroscience.
[89] D. Donoho,et al. Uncertainty principles and signal recovery , 1989 .
[90] Doris Y. Tsao,et al. A face feature space in the macaque temporal lobe , 2009, Nature Neuroscience.