Learning a dictionary of shape-components in visual cortex: comparison with neurons, humans and machines
暂无分享,去创建一个
[1] V. Mountcastle. Modality and topographic properties of single neurons of cat's somatic sensory cortex. , 1957, Journal of neurophysiology.
[2] Martin A. Giese,et al. Learning Features of Intermediate Complexity for the Recognition of Biological Motion , 2005, ICANN.
[3] E. Rolls. Neurons in the cortex of the temporal lobe and in the amygdala of the monkey with responses selective for faces. , 1984, Human neurobiology.
[4] Bartlett W. Mel,et al. Minimizing Binding Errors Using Learned Conjunctive Features , 2000, Neural Computation.
[5] F. Girosi,et al. A Connection Between GRBF and MLP , 1992 .
[6] Y. Dan,et al. Stimulus Timing-Dependent Plasticity in Cortical Processing of Orientation , 2001, Neuron.
[7] 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 .
[8] Lior Wolf,et al. Perception Strategies in Hierarchical Vision Systems , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[9] T. Poggio,et al. Cognitive neuroscience: Neural mechanisms for the recognition of biological movements , 2003, Nature Reviews Neuroscience.
[10] Michael C. Burl,et al. Finding faces in cluttered scenes using random labeled graph matching , 1995, Proceedings of IEEE International Conference on Computer Vision.
[11] Bruno A. Olshausen,et al. A multiscale dynamic routing circuit for forming size- and position-invariant object representations , 1995, Journal of Computational Neuroscience.
[12] Aapo Hyvärinen,et al. A two-layer sparse coding model learns simple and complex cell receptive fields and topography from natural images , 2001, Vision Research.
[13] K. Rockland,et al. Divergent feedback connections from areas V4 and TEO in the macaque , 1994, Visual Neuroscience.
[14] Peter Dayan,et al. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems , 2001 .
[15] E. Rolls. The orbitofrontal cortex and reward. , 2000, Cerebral cortex.
[16] Isabel Gauthier,et al. Feature learning during the acquisition of perceptual expertise , 1998, Behavioral and Brain Sciences.
[17] J. Daugman. Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.
[18] Minami Ito,et al. Representation of Angles Embedded within Contour Stimuli in Area V2 of Macaque Monkeys , 2004, The Journal of Neuroscience.
[19] Thomas Serre,et al. Object recognition with features inspired by visual cortex , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[20] Peter Meer,et al. Synergism in low level vision , 2002, Object recognition supported by user interaction for service robots.
[21] K. Rockland,et al. Organization of individual cortical axons projecting from area V1 (area 17) to V2 (area 18) in the macaque monkey , 1990, Visual Neuroscience.
[22] Pawan Sinha,et al. Qualitative Representations for Recognition , 2002, Biologically Motivated Computer Vision.
[23] R. Desimone,et al. Clustering of perirhinal neurons with similar properties following visual experience in adult monkeys , 2000, Nature Neuroscience.
[24] Y. Miyashita. Neuronal correlate of visual associative long-term memory in the primate temporal cortex , 1988, Nature.
[25] D Sagi,et al. Where practice makes perfect in texture discrimination: evidence for primary visual cortex plasticity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.
[26] Jonathan D. Cohen,et al. Prefrontal cortex and flexible cognitive control: rules without symbols. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[27] Keiji Tanaka,et al. Inferotemporal cortex and object vision. , 1996, Annual review of neuroscience.
[28] David G. Lowe,et al. Towards a Computational Model for Object Recognition in IT Cortex , 2000, Biologically Motivated Computer Vision.
[29] G Kovács,et al. Cortical correlate of pattern backward masking. , 1995, Proceedings of the National Academy of Sciences of the United States of America.
[30] Leslie G. Ungerleider,et al. Connections of inferior temporal areas TEO and TE with parietal and frontal cortex in macaque monkeys. , 1994, Cerebral cortex.
[31] D H Brainard,et al. The Psychophysics Toolbox. , 1997, Spatial vision.
[32] S. Thorpe,et al. How parallel is visual processing in the ventral pathway? , 2004, Trends in Cognitive Sciences.
[33] P. Goldman-Rakic,et al. Areal segregation of face-processing neurons in prefrontal cortex. , 1997, Science.
[34] Simon J Thorpe,et al. Animals roll around the clock: the rotation invariance of ultrarapid visual processing. , 2006, Journal of vision.
[35] T. Poggio,et al. Neural mechanisms of object recognition , 2002, Current Opinion in Neurobiology.
[36] P. Lennie,et al. Coding of image contrast in central visual pathways of the macaque monkey , 1990, Vision Research.
[37] I. Ohzawa,et al. Organization of suppression in receptive fields of neurons in cat visual cortex. , 1992, Journal of neurophysiology.
[38] Keiji Tanaka,et al. Neuronal selectivities to complex object features in the ventral visual pathway of the macaque cerebral cortex. , 1994, Journal of neurophysiology.
[39] Leslie G. Ungerleider,et al. Connections of inferior temporal areas TE and TEO with medial temporal- lobe structures in infant and adult monkeys , 1991, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[40] Karl J. Friston,et al. How the brain learns to see objects and faces in an impoverished context , 1997, Nature.
[41] Thomas Serre,et al. Modeling feature sharing between object detection and top-down attention , 2005 .
[42] S Marcelja,et al. Mathematical description of the responses of simple cortical cells. , 1980, Journal of the Optical Society of America.
[43] S. Zeki,et al. The Organization of Connections between Areas V5 and V1 in Macaque Monkey Visual Cortex , 1989, The European journal of neuroscience.
[44] P. Fldik,et al. The Speed of Sight , 2001, Journal of Cognitive Neuroscience.
[45] Johan Wagemans,et al. The effect of category learning on the representation of shape: dimensions can be biased but not differentiated. , 2003, Journal of experimental psychology. General.
[46] C. Eriksen,et al. Effects of noise letters upon the identification of a target letter in a nonsearch task , 1974 .
[47] A P Georgopoulos,et al. On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex , 1982, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[48] A. Treisman,et al. Perception of objects in natural scenes: is it really attention free? , 2005, Journal of experimental psychology. Human perception and performance.
[49] S. Zeki,et al. The Organization of Connections between Areas V5 and V2 in Macaque Monkey Visual Cortex , 1989, The European journal of neuroscience.
[50] David J. Freedman,et al. Visual categorization and the primate prefrontal cortex: neurophysiology and behavior. , 2002, Journal of neurophysiology.
[51] Edward H. Adelson,et al. Motion illusions as optimal percepts , 2002, Nature Neuroscience.
[52] Charles F. Stevens. Models are common; good theories are scarce , 2000, Nature Neuroscience.
[53] Nancy Kanwisher,et al. fMRI evidence for objects as the units of attentional selection , 1999, Nature.
[54] Heiko Wersing,et al. Evolutionary optimization of a hierarchical object recognition model , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[55] Josef Syka and Michael M. Merzenich. Plasticity and signal representation in the auditory system , 2005 .
[56] T. Poggio,et al. The Mathematics of Learning: Dealing with Data , 2005, 2005 International Conference on Neural Networks and Brain.
[57] F. Girosi,et al. Networks for approximation and learning , 1990, Proc. IEEE.
[58] M. Merzenich,et al. Representation of the cochlear partition of the superior temporal plane of the macaque monkey. , 1973, Brain research.
[59] Dario L. Ringach,et al. Dynamics of orientation tuning in macaque primary visual cortex , 1997, Nature.
[60] R. Desimone,et al. Stimulus-selective properties of inferior temporal neurons in the macaque , 1984, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[61] Shimon Ullman,et al. Feature hierarchies for object classification , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[62] T. Poggio,et al. A network that learns to recognize three-dimensional objects , 1990, Nature.
[63] Jitendra Malik,et al. When is scene identification just texture recognition? , 2004, Vision Research.
[64] G. Wallis,et al. Learning invariant responses to the natural transformations of objects , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).
[65] David G. Lowe,et al. Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[66] R. Desimone,et al. Visual areas in the temporal cortex of the macaque , 1979, Brain Research.
[67] P. H. Schiller,et al. The effects of V4 and middle temporal (MT) area lesions on visual performance in the rhesus monkey , 1993, Visual Neuroscience.
[68] E. Rolls,et al. A Neurodynamical cortical model of visual attention and invariant object recognition , 2004, Vision Research.
[69] Tomaso Poggio,et al. Intracellular measurements of spatial integration and the MAX operation in complex cells of the cat primary visual cortex. , 2004, Journal of neurophysiology.
[70] R. Vogels,et al. Inferotemporal neurons represent low-dimensional configurations of parameterized shapes , 2001, Nature Neuroscience.
[71] RussLL L. Ds Vnlos,et al. SPATIAL FREQUENCY SELECTIVITY OF CELLS IN MACAQUE VISUAL CORTEX , 2022 .
[72] P M Gochin. Properties of simulated neurons from a model of primate inferior temporal cortex. , 1994, Cerebral cortex.
[73] Jitendra Malik,et al. Shape matching and object recognition using low distortion correspondences , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[74] J. Hawkins,et al. On Intelligence , 2004 .
[75] D. Heeger. Half-squaring in responses of cat striate cells , 1992, Visual Neuroscience.
[76] P. H. Schiller. Effect of lesions in visual cortical area V4 on the recognition of transformed objects , 1995, Nature.
[77] B L Finlay,et al. Quantitative studies of single-cell properties in monkey striate cortex. IV. Corticotectal cells. , 1976, Journal of neurophysiology.
[78] M. Carandini,et al. Summation and division by neurons in primate visual cortex. , 1994, Science.
[79] T. Sato,et al. Interactions of visual stimuli in the receptive fields of inferior temporal neurons in awake macaques , 2004, Experimental Brain Research.
[80] D. Long. Probabilistic Models of the Brain. , 2002 .
[81] V. Lamme,et al. The distinct modes of vision offered by feedforward and recurrent processing , 2000, Trends in Neurosciences.
[82] S. Thorpe,et al. Spike times make sense , 2005, Trends in Neurosciences.
[83] Takeo Kanade,et al. A statistical method for 3D object detection applied to faces and cars , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).
[84] D. Hubel,et al. Receptive fields and functional architecture of monkey striate cortex , 1968, The Journal of physiology.
[85] T. Bonhoeffer,et al. Pairing-Induced Changes of Orientation Maps in Cat Visual Cortex , 2001, Neuron.
[86] P. Schiller,et al. Quantitative studies of single-cell properties in monkey striate cortex. III. Spatial frequency. , 1976, Journal of neurophysiology.
[87] V. Mountcastle. The columnar organization of the neocortex. , 1997, Brain : a journal of neurology.
[88] Joel L. Davis,et al. Large-Scale Neuronal Theories of the Brain , 1994 .
[89] J. Maunsell,et al. The Effect of Perceptual Learning on Neuronal Responses in Monkey Visual Area V4 , 2004, The Journal of Neuroscience.
[90] Trevor Darrell,et al. The pyramid match kernel: discriminative classification with sets of image features , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[91] S. Thorpe,et al. The Time Course of Visual Processing: From Early Perception to Decision-Making , 2001, Journal of Cognitive Neuroscience.
[92] Y. Miyashita. Inferior temporal cortex: where visual perception meets memory. , 1993, Annual review of neuroscience.
[93] Antonio Torralba,et al. Depth Estimation from Image Structure , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[94] D I Perrett,et al. Organization and functions of cells responsive to faces in the temporal cortex. , 1992, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.
[95] A. Parker,et al. Spatial properties of neurons in the monkey striate cortex , 1987, Proceedings of the Royal Society of London. Series B. Biological Sciences.
[96] M. Mishkin,et al. Learning increases stimulus salience in anterior inferior temporal cortex of the macaque. , 2001, Journal of neurophysiology.
[97] Yann LeCun,et al. Learning a similarity metric discriminatively, with application to face verification , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[98] Allan D. Jepson,et al. From Features to Perceptual Categories , 1992, BMVC.
[99] Naomi M. Kenner,et al. How fast can you change your mind? The speed of top-down guidance in visual search , 2004, Vision Research.
[100] D. B. Bender,et al. Visual properties of neurons in inferotemporal cortex of the Macaque. , 1972, Journal of neurophysiology.
[101] Nancy Kanwisher,et al. A cortical representation of the local visual environment , 1998, Nature.
[102] 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.
[103] E. Miller,et al. THE PREFRONTAL CORTEX AND COGNITIVE CONTROL , 2000 .
[104] Ulf Knoblich,et al. Stimulus Simplification and Object Representation: A Modeling Study , 2002 .
[105] Alex Holub,et al. Exploiting Unlabelled Data for Hybrid Object Classification , 2005 .
[106] P. Schiller,et al. Quantitative studies of single-cell properties in monkey striate cortex. I. Spatiotemporal organization of receptive fields. , 1976, Journal of neurophysiology.
[107] E. Miller,et al. Experience-dependent sharpening of visual shape selectivity in inferior temporal cortex. , 2005, Cerebral cortex.
[108] Tomaso A. Poggio,et al. Face recognition with support vector machines: global versus component-based approach , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.
[109] N. Kanwisher,et al. Visual attention: Insights from brain imaging , 2000, Nature Reviews Neuroscience.
[110] I. Ohzawa,et al. Receptive field structure in the visual cortex: does selective stimulation induce plasticity? , 1995, Proceedings of the National Academy of Sciences of the United States of America.
[111] Jacques Gautrais,et al. Rapid Visual Processing using Spike Asynchrony , 1996, NIPS.
[112] J. Maunsell,et al. Physiological correlates of perceptual learning in monkey V1 and V2. , 2002, Journal of neurophysiology.
[113] Y. Amit,et al. An integrated network for invariant visual detection and recognition , 2003, Vision Research.
[114] Y. Miyashita,et al. Neural representation of visual objects: encoding and top-down activation , 2000, Current Opinion in Neurobiology.
[115] Peter Dayan,et al. Neural Models for Part-Whole Hierarchies , 1996, NIPS.
[116] M. Hasselmo,et al. The role of expression and identity in the face-selective responses of neurons in the temporal visual cortex of the monkey , 1989, Behavioural Brain Research.
[117] Federico Girosi,et al. Support Vector Machines: Training and Applications , 1997 .
[118] M. A. Repucci,et al. Responses of V1 neurons to two-dimensional hermite functions. , 2006, Journal of neurophysiology.
[119] D. Heeger. Modeling simple-cell direction selectivity with normalized, half-squared, linear operators. , 1993, Journal of neurophysiology.
[120] K Tanaka,et al. Neuronal mechanisms of object recognition. , 1993, Science.
[121] W. Schultz,et al. Modifications of reward expectation-related neuronal activity during learning in primate orbitofrontal cortex. , 2000, Journal of neurophysiology.
[122] Eero P. Simoncelli,et al. Natural image statistics and neural representation. , 2001, Annual review of neuroscience.
[123] D. C. Essen,et al. Neural responses to polar, hyperbolic, and Cartesian gratings in area V4 of the macaque monkey. , 1996, Journal of neurophysiology.
[124] Eero P. Simoncelli,et al. Natural signal statistics and sensory gain control , 2001, Nature Neuroscience.
[125] E. Miller,et al. Different time courses of learning-related activity in the prefrontal cortex and striatum , 2005, Nature.
[126] Pietro Perona,et al. Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.
[127] R. Buckner,et al. THE COGNITIVE NEUROSCIENCE OF REMEMBERING , 2001 .
[128] David L. Sheinberg,et al. Noticing Familiar Objects in Real World Scenes: The Role of Temporal Cortical Neurons in Natural Vision , 2001, The Journal of Neuroscience.
[129] Sayan Mukherjee,et al. Feature reduction and hierarchy of classifiers for fast object detection in video images , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.
[130] J. A. Horel,et al. Cortical afferents to behaviorally defined regions of the inferior temporal and parahippocampal gyri as demonstrated by WGA‐HRP , 1992, The Journal of comparative neurology.
[131] W. Reichardt,et al. Dynamic response properties of movement detectors: Theoretical analysis and electrophysiological investigation in the visual system of the fly , 1987, Biological Cybernetics.
[132] Idan Segev,et al. On the Transmission of Rate Code in Long Feedforward Networks with Excitatory–Inhibitory Balance , 2003, The Journal of Neuroscience.
[133] Edmund T. Rolls,et al. A Model of Invariant Object Recognition in the Visual System: Learning Rules, Activation Functions, Lateral Inhibition, and Information-Based Performance Measures , 2000, Neural Computation.
[134] D. Hubel,et al. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.
[135] C. Malsburg. Binding in models of perception and brain function , 1995, Current Opinion in Neurobiology.
[136] D. Heeger. Normalization of cell responses in cat striate cortex , 1992, Visual Neuroscience.
[137] Simon J. Thorpe,et al. Ultra-rapid object detection with saccadic eye movements: Visual processing speed revisited , 2006, Vision Research.
[138] C. Connor,et al. Population coding of shape in area V4 , 2002, Nature Neuroscience.
[139] D. Perrett,et al. Time course of neural responses discriminating different views of the face and head. , 1992, Journal of neurophysiology.
[140] J. Maunsell,et al. Visual effects of lesions of cortical area V2 in macaques , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[141] C. Koch,et al. Methods in Neuronal Modeling: From Ions to Networks , 1998 .
[142] Leslie G. Ungerleider,et al. The modular organization of projections from areas V1 and V2 to areas V4 and TEO in macaques , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[143] U Yinon,et al. Evidence for long‐term functional plasticity in the visual cortex of adult cats , 1982, The Journal of physiology.
[144] M. Harries,et al. Viewer-centred and object-centred coding of heads in the macaque temporal cortex , 2004, Experimental Brain Research.
[145] Frédéric Jurie,et al. Creating efficient codebooks for visual recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[146] R. L. de Valois,et al. Cartesian and non-Cartesian responses in LGN, V1, and V2 cells , 2001, Visual Neuroscience.
[147] Michael I. Jordan,et al. The Handbook of Brain Theory and Neural Networks , 2002 .
[148] Paul A. Viola,et al. Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.
[149] James L. McClelland,et al. Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .
[150] Keiji Tanaka,et al. Effects of shape-discrimination training on the selectivity of inferotemporal cells in adult monkeys. , 1998, Journal of neurophysiology.
[151] Y. Miyashita,et al. Neural organization for the long-term memory of paired associates , 1991, Nature.
[152] D. Heeger,et al. Contrast normalization and a linear model for the directional selectivity of simple cells in cat striate cortex , 1997, Visual Neuroscience.
[153] T. Poggio,et al. Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.
[154] Jennifer Louie. A biological model of object recognition with feature learning , 2003 .
[155] E M Callaway,et al. Visual scenes and cortical neurons: what you see is what you get. , 1998, Proceedings of the National Academy of Sciences of the United States of America.
[156] R Van Rullen,et al. Face processing using one spike per neurone. , 1998, Bio Systems.
[157] R E Weller,et al. Qualitative and quantitative features of axons projecting from caudal to rostral inferior temporal cortex of squirrel monkeys , 1995, Visual Neuroscience.
[158] Laurenz Wiskott,et al. Slow feature analysis yields a rich repertoire of complex cell properties. , 2005, Journal of vision.
[159] S. Thorpe,et al. A Limit to the Speed of Processing in Ultra-Rapid Visual Categorization of Novel Natural Scenes , 2001, Journal of Cognitive Neuroscience.
[160] A. Hodgkin,et al. A quantitative description of membrane current and its application to conduction and excitation in nerve , 1952, The Journal of physiology.
[161] S. Hochstein,et al. View from the Top Hierarchies and Reverse Hierarchies in the Visual System , 2002, Neuron.
[162] Mark C. W. van Rossum,et al. Fast Propagation of Firing Rates through Layered Networks of Noisy Neurons , 2002, The Journal of Neuroscience.
[163] Michel Vidal-Naquet,et al. Visual features of intermediate complexity and their use in classification , 2002, Nature Neuroscience.
[164] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[165] Edmund T. Rolls,et al. Position invariant recognition in the visual system with cluttered environments , 2000, Neural Networks.
[166] N. Sigala,et al. Visual categorization shapes feature selectivity in the primate temporal cortex , 2002, Nature.
[167] Yann LeCun,et al. Off-Road Obstacle Avoidance through End-to-End Learning , 2005, NIPS.
[168] Keiji Tanaka,et al. Connections between Anterior Inferotemporal Cortex and Superior Temporal Sulcus Regions in the Macaque Monkey , 2000, The Journal of Neuroscience.
[169] Tomaso A. Poggio,et al. Example-Based Object Detection in Images by Components , 2001, IEEE Trans. Pattern Anal. Mach. Intell..
[170] P A Salin,et al. Corticocortical connections in the visual system: structure and function. , 1995, Physiological reviews.
[171] Shree K. Nayar,et al. Ordinal Measures for Image Correspondence , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[172] R. Desimone,et al. Competitive Mechanisms Subserve Attention in Macaque Areas V2 and V4 , 1999, The Journal of Neuroscience.
[173] K. Miller. Understanding layer 4 of the cortical circuit: a model based on cat V1. , 2003, Cerebral cortex.
[174] M. Riesenhuber,et al. Face processing in humans is compatible with a simple shape–based model of vision , 2004, Proceedings of the Royal Society of London. Series B: Biological Sciences.
[175] 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..
[176] D. G. Albrecht,et al. Spatial frequency selectivity of cells in macaque visual cortex , 1982, Vision Research.
[177] Robert L. Goldstone,et al. The development of features in object concepts , 1998, Behavioral and Brain Sciences.
[178] P. Schiller,et al. Quantitative studies of single-cell properties in monkey striate cortex. II. Orientation specificity and ocular dominance. , 1976, Journal of neurophysiology.
[179] Chuan Yi Tang,et al. A 2.|E|-Bit Distributed Algorithm for the Directed Euler Trail Problem , 1993, Inf. Process. Lett..
[180] Peter Földiák,et al. Learning Invariance from Transformation Sequences , 1991, Neural Comput..
[181] J. Lund,et al. Anatomical substrates for functional columns in macaque monkey primary visual cortex. , 2003, Cerebral cortex.
[182] Pietro Perona,et al. Towards automatic discovery of object categories , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).
[183] Tomaso Poggio,et al. Models of object recognition , 2000, Nature Neuroscience.
[184] D. V. van Essen,et al. Processing of color, form and disparity information in visual areas VP and V2 of ventral extrastriate cortex in the macaque monkey , 1986, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[185] Denis Fize,et al. Speed of processing in the human visual system , 1996, Nature.
[186] Thomas Serre,et al. On the Role of Object-Specific Features for Real World Object Recognition in Biological Vision , 2002, Biologically Motivated Computer Vision.
[187] E. Rolls. Learning mechanisms in the temporal lobe visual cortex , 1995, Behavioural Brain Research.
[188] D. Perrett,et al. Recognition of objects and their component parts: responses of single units in the temporal cortex of the macaque. , 1994, Cerebral cortex.
[189] Laurenz Wiskott,et al. How Does Our Visual System Achieve Shift and Size Invariance , 2004 .
[190] Pietro Perona,et al. Combining generative models and Fisher kernels for object recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[191] P. Lennie. Single Units and Visual Cortical Organization , 1998, Perception.
[192] Tomaso Poggio,et al. Fast Readout of Object Identity from Macaque Inferior Temporal Cortex , 2005, Science.
[193] Antonio Torralba,et al. Statistics of natural image categories , 2003, Network.
[194] Leslie G. Ungerleider,et al. Pathways for motion analysis: Cortical connections of the medial superior temporal and fundus of the superior temporal visual areas in the macaque , 1990, The Journal of comparative neurology.
[195] G. Boynton,et al. Visual Cortex: The Continuing Puzzle of Area V2 , 2004, Current Biology.
[196] Bruno A Olshausen,et al. Timecourse of neural signatures of object recognition. , 2003, Journal of vision.
[197] Leslie G. Ungerleider,et al. Cortical projections of area V2 in the macaque. , 1997, Cerebral cortex.
[198] T. Gawne,et al. Responses of primate visual cortical V4 neurons to simultaneously presented stimuli. , 2002, Journal of neurophysiology.
[199] G LoweDavid,et al. Distinctive Image Features from Scale-Invariant Keypoints , 2004 .
[200] S. Zeki,et al. Modular Connections between Areas V2 and V4 of Macaque Monkey Visual Cortex , 1989, The European journal of neuroscience.
[201] T. Poggio. A theory of how the brain might work. , 1990, Cold Spring Harbor symposia on quantitative biology.
[202] K R Gegenfurtner,et al. Processing of color, form, and motion in macaque area V2 , 1996, Visual Neuroscience.
[203] Hisao Nishijo,et al. Differential characteristics of face neuron responses within the anterior superior temporal sulcus of macaques. , 2005, Journal of neurophysiology.
[204] Eero P. Simoncelli,et al. Computational models of cortical visual processing. , 1996, Proceedings of the National Academy of Sciences of the United States of America.
[205] C. Gross. Brain, Vision, Memory: Tales in the History of Neuroscience , 1998 .
[206] D. Ferster,et al. Neural mechanisms of orientation selectivity in the visual cortex. , 2000, Annual review of neuroscience.
[207] D. J. Felleman,et al. Cortical connections of areas V3 and VP of macaque monkey extrastriate visual cortex , 1997, The Journal of comparative neurology.
[208] J. Leo van Hemmen,et al. Temporal association , 1991 .
[209] Lior Wolf,et al. Empirical Comparison between Hierarchical Fragments Based and Standard Model Based Object Recognition Systems , 2006 .
[210] Nicolas P. Rougier,et al. Learning representations in a gated prefrontal cortex model of dynamic task switching , 2002, Cogn. Sci..
[211] R. Johansson,et al. First spikes in ensembles of human tactile afferents code complex spatial fingertip events , 2004, Nature Neuroscience.
[212] S. Shipp,et al. The functional logic of cortical connections , 1988, Nature.
[213] S. Thorpe,et al. Seeking Categories in the Brain , 2001, Science.
[214] E. Callaway. Local circuits in primary visual cortex of the macaque monkey. , 1998, Annual review of neuroscience.
[215] S. Thorpe,et al. The time course of visual processing: Backward masking and natural scene categorisation , 2005, Vision Research.
[216] A. J. Mistlin,et al. Neurones responsive to faces in the temporal cortex: studies of functional organization, sensitivity to identity and relation to perception. , 1984, Human neurobiology.
[217] R. Desimone,et al. Activity of neurons in anterior inferior temporal cortex during a short- term memory task , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[218] Leslie G. Ungerleider,et al. Object vision and spatial vision: two cortical pathways , 1983, Trends in Neurosciences.
[219] Tomaso A. Poggio,et al. Example-Based Learning for View-Based Human Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[220] F. Attneave. Some informational aspects of visual perception. , 1954, Psychological review.
[221] Leslie G. Ungerleider,et al. Subcortical connections of inferior temporal areas TE and TEO in macaque monkeys , 1993, The Journal of comparative neurology.
[222] Michael S. Lewicki,et al. Efficient auditory coding , 2006, Nature.
[223] Keiji Tanaka. Columns for complex visual object features in the inferotemporal cortex: clustering of cells with similar but slightly different stimulus selectivities. , 2003, Cerebral cortex.
[224] T. Poggio,et al. Are Cortical Models Really Bound by the “Binding Problem”? , 1999, Neuron.
[225] Keiji Tanaka,et al. Optical Imaging of Functional Organization in the Monkey Inferotemporal Cortex , 1996, Science.
[226] M. Tarr,et al. Visual Object Recognition , 1996, ISTCS.
[227] D. Ruderman. The statistics of natural images , 1994 .
[228] D. C. Van Essen,et al. Concurrent processing streams in monkey visual cortex , 1988, Trends in Neurosciences.
[229] J Gautrais,et al. Rate coding versus temporal order coding: a theoretical approach. , 1998, Bio Systems.
[230] P. Schiller,et al. Quantitative studies of single-cell properties in monkey striate cortex. V. Multivariate statistical analyses and models. , 1976, Journal of neurophysiology.
[231] David I. Perrett,et al. Neurophysiology of shape processing , 1993, Image Vis. Comput..
[232] David I. Perrett,et al. Modeling visual recognition from neurobiological constraints , 1994, Neural Networks.
[233] H. Markram. The Blue Brain Project , 2006, Nature Reviews Neuroscience.
[234] S. Thorpe,et al. Dynamics of orientation coding in area V1 of the awake primate , 1993, Visual Neuroscience.
[235] R. Desimone,et al. The representation of stimulus familiarity in anterior inferior temporal cortex. , 1993, Journal of neurophysiology.
[236] Lawrence D. Jackel,et al. Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.
[237] E. Rolls,et al. View-invariant representations of familiar objects by neurons in the inferior temporal visual cortex. , 1998, Cerebral cortex.
[238] S. Grossberg,et al. Context-sensitive binding by the laminar circuits of V1 and V2: A unified model of perceptual grouping, attention, and orientation contrast , 2001 .
[239] Arnaud Delorme,et al. Face identification using one spike per neuron: resistance to image degradations , 2001, Neural Networks.
[240] J A Solomon,et al. Model of visual contrast gain control and pattern masking. , 1997, Journal of the Optical Society of America. A, Optics, image science, and vision.
[241] I. Biederman,et al. On the information extracted from a glance at a scene. , 1974, Journal of experimental psychology.
[242] E. Bullmore,et al. Society for Neuroscience Abstracts , 1997 .
[243] Edmund T. Rolls,et al. Invariant Object Recognition in the Visual System with Novel Views of 3D Objects , 2002, Neural Computation.
[244] J. Bullier. Integrated model of visual processing , 2001, Brain Research Reviews.
[245] S. Nelson,et al. An emergent model of orientation selectivity in cat visual cortical simple cells , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[246] N. Kanwisher,et al. The Fusiform Face Area: A Module in Human Extrastriate Cortex Specialized for Face Perception , 1997, The Journal of Neuroscience.
[247] Kunihiko Fukushima,et al. Cognitron: A self-organizing multilayered neural network , 1975, Biological Cybernetics.
[248] R. Desimone,et al. Visual properties of neurons in a polysensory area in superior temporal sulcus of the macaque. , 1981, Journal of neurophysiology.
[249] Antonio Torralba,et al. Building the gist of a scene: the role of global image features in recognition. , 2006, Progress in brain research.
[250] D C Van Essen,et al. Shifter circuits: a computational strategy for dynamic aspects of visual processing. , 1987, Proceedings of the National Academy of Sciences of the United States of America.
[251] H. Abarbanel,et al. Dynamical model of long-term synaptic plasticity , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[252] N. Kanwisher,et al. Stages of processing in face perception: an MEG study , 2002, Nature Neuroscience.
[253] Christoph von der Malsburg,et al. The Correlation Theory of Brain Function , 1994 .
[254] R. von der Heydt,et al. Coding of Border Ownership in Monkey Visual Cortex , 2000, The Journal of Neuroscience.
[255] N. Kanwisher,et al. A Cortical Area Selective for Visual Processing of the Human Body , 2001, Science.
[256] Thomas Serre,et al. Realistic Modeling of Simple and Complex Cell Tuning in the HMAX Model, and Implications for Invariant Object Recognition in Cortex , 2004 .
[257] K. Rockland. Visual cortical organization at the single axon level: a beginning , 2002, Neuroscience Research.
[258] R. Douglas,et al. A functional microcircuit for cat visual cortex. , 1991, The Journal of physiology.
[259] E. Rolls,et al. Selectivity between faces in the responses of a population of neurons in the cortex in the superior temporal sulcus of the monkey , 1985, Brain Research.
[260] R. Shapley,et al. New perspectives on the mechanisms for orientation selectivity , 1997, Current Opinion in Neurobiology.
[261] A. J. Mistlin,et al. Visual neurones responsive to faces , 1987, Trends in Neurosciences.
[262] M. Potter. Short-term conceptual memory for pictures. , 1976, Journal of experimental psychology. Human learning and memory.
[263] R. Desimone,et al. Selective attention gates visual processing in the extrastriate cortex. , 1985, Science.
[264] Guillaume A. Rousselet,et al. Processing of one, two or four natural scenes in humans: the limits of parallelism , 2004, Vision Research.
[265] Idan Segev,et al. Methods in Neuronal Modeling , 1988 .
[266] M. Tovée,et al. Information encoding and the responses of single neurons in the primate temporal visual cortex. , 1993, Journal of neurophysiology.
[267] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[268] E T Rolls,et al. Neurophysiological mechanisms underlying face processing within and beyond the temporal cortical visual areas. , 1992, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.
[269] Michael W. Spratling. Learning viewpoint invariant perceptual representations from cluttered images , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[270] Antonio Torralba,et al. Sharing features: efficient boosting procedures for multiclass object detection , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..
[271] J. Hegdé,et al. Strategies of shape representation in macaque visual area V2 , 2003, Visual Neuroscience.
[272] Charles E Connor,et al. Underlying principles of visual shape selectivity in posterior inferotemporal cortex , 2004, Nature Neuroscience.
[273] Charles Fredrick Cadieu,et al. Modeling shape representation in visual cortex area V4 , 2005 .
[274] J. K. Hietanen,et al. The effects of lighting conditions on responses of cells selective for face views in the macaque temporal cortex , 2004, Experimental Brain Research.
[275] Mark C. W. van Rossum,et al. Stable Hebbian Learning from Spike Timing-Dependent Plasticity , 2000, The Journal of Neuroscience.
[276] C. Connor,et al. Responses to contour features in macaque area V4. , 1999, Journal of neurophysiology.
[277] Thomas Serre,et al. Component-based face detection , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.
[278] S. Thorpe,et al. Rapid categorization of natural images by rhesus monkeys , 1998, Neuroreport.
[279] Pietro Perona,et al. A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry , 1998, ECCV.
[280] Thomas Serre,et al. Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[281] R. L. Valois,et al. The orientation and direction selectivity of cells in macaque visual cortex , 1982, Vision Research.
[282] George L. Gerstein,et al. Feature-linked synchronization of thalamic relay cell firing induced by feedback from the visual cortex , 1994, Nature.
[283] Cordelia Schmid,et al. A maximum entropy framework for part-based texture and object recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[284] László Györfi,et al. A Probabilistic Theory of Pattern Recognition , 1996, Stochastic Modelling and Applied Probability.
[285] H. Markram,et al. Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs , 1997, Science.
[286] D. Perrett,et al. Visual neurones responsive to faces in the monkey temporal cortex , 2004, Experimental Brain Research.
[287] C. Koch,et al. Visual Selective Behavior Can Be Triggered by a Feed-Forward Process , 2003, Journal of Cognitive Neuroscience.
[288] Brian Leung,et al. Component-based Car Detection in Street Scene Images , 2004 .
[289] Michael S. Landy,et al. Computational models of visual processing , 1991 .
[290] Yann LeCun,et al. Learning processes in an asymmetric threshold network , 1986 .
[291] B. Schiele,et al. Combined Object Categorization and Segmentation With an Implicit Shape Model , 2004 .
[292] J. Hegdé,et al. Selectivity for Complex Shapes in Primate Visual Area V2 , 2000, The Journal of Neuroscience.
[293] H. Sompolinsky,et al. Theory of orientation tuning in visual cortex. , 1995, Proceedings of the National Academy of Sciences of the United States of America.
[294] Konrad Paul Kording,et al. How are complex cell properties adapted to the statistics of natural stimuli? , 2004, Journal of neurophysiology.
[295] A. Georgopoulos,et al. Modular organization of directionally tuned cells in the motor cortex: Is there a short-range order? , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[296] N. Logothetis,et al. Shape representation in the inferior temporal cortex of monkeys , 1995, Current Biology.
[297] D. V. van Essen,et al. A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[298] David Mumford,et al. On the computational architecture of the neocortex , 2004, Biological Cybernetics.
[299] D. V. van Essen,et al. Spatial Attention Effects in Macaque Area V4 , 1997, The Journal of Neuroscience.
[300] Gabriela Csurka,et al. Visual categorization with bags of keypoints , 2002, eccv 2004.
[301] Guillaume A. Rousselet,et al. Parallel processing in high-level categorization of natural images , 2002, Nature Neuroscience.
[302] Y. Miyashita,et al. Neuronal tuning to learned complex forms in vision. , 1994, Neuroreport.
[303] Pietro Perona,et al. Object class recognition by unsupervised scale-invariant learning , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..
[304] Shimon Ullman,et al. Computation of pattern invariance in brain-like structures , 1999, Neural Networks.
[305] D Mumford,et al. On the computational architecture of the neocortex. II. The role of cortico-cortical loops. , 1992, Biological cybernetics.
[306] David J. Freedman,et al. Categorical representation of visual stimuli in the primate prefrontal cortex. , 2001, Science.
[307] M. W. Brown,et al. Neuronal activity related to visual recognition memory: long-term memory and the encoding of recency and familiarity information in the primate anterior and medial inferior temporal and rhinal cortex , 2004, Experimental Brain Research.
[308] D. Perrett,et al. Evidence accumulation in cell populations responsive to faces: an account of generalisation of recognition without mental transformations , 1998, Cognition.
[309] J. Wolfe,et al. Preattentive Object Files: Shapeless Bundles of Basic Features , 1997, Vision Research.
[310] N. Logothetis,et al. The Effect of Learning on the Function of Monkey Extrastriate Visual Cortex , 2004, PLoS biology.
[311] E. Miller,et al. Effects of Visual Experience on the Representation of Objects in the Prefrontal Cortex , 2000, Neuron.
[312] H. Seung,et al. Learning in Spiking Neural Networks by Reinforcement of Stochastic Synaptic Transmission , 2003, Neuron.
[313] Takayuki Ito,et al. Neocognitron: A neural network model for a mechanism of visual pattern recognition , 1983, IEEE Transactions on Systems, Man, and Cybernetics.
[314] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[315] H H Bülthoff,et al. Detection of animals in natural images using far peripheral vision , 2001, The European journal of neuroscience.
[316] Rajesh P. N. Rao,et al. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. , 1999 .
[317] R. Desimone. Face-Selective Cells in the Temporal Cortex of Monkeys , 1991, Journal of Cognitive Neuroscience.
[318] J. Maunsell,et al. Form representation in monkey inferotemporal cortex is virtually unaltered by free viewing , 2000, Nature Neuroscience.
[319] R. Shapley,et al. A neuronal network model of macaque primary visual cortex (V1): orientation selectivity and dynamics in the input layer 4Calpha. , 2000, Proceedings of the National Academy of Sciences of the United States of America.
[320] Y. Frégnac,et al. Visual input evokes transient and strong shunting inhibition in visual cortical neurons , 1998, Nature.
[321] Pietro Perona,et al. Unsupervised Learning of Models for Recognition , 2000, ECCV.
[322] Tieniu Tan,et al. Robust Encoding of Local Ordinal Measures: A General Framework of Iris Recognition , 2004, ECCV Workshop BioAW.
[323] Bartlett W. Mel. SEEMORE: Combining Color, Shape, and Texture Histogramming in a Neurally Inspired Approach to Visual Object Recognition , 1997, Neural Computation.
[324] Minami Ito,et al. Columns for visual features of objects in monkey inferotemporal cortex , 1992, Nature.
[325] J. A. Hirsch. Synaptic physiology and receptive field structure in the early visual pathway of the cat. , 2003, Cerebral cortex.
[326] Leslie G. Ungerleider,et al. ‘What’ and ‘where’ in the human brain , 1994, Current Opinion in Neurobiology.
[327] C. Connor,et al. Shape representation in area V4: position-specific tuning for boundary conformation. , 2001, Journal of neurophysiology.
[328] P S Goldman-Rakic,et al. Functional synergism between putative gamma-aminobutyrate-containing neurons and pyramidal neurons in prefrontal cortex. , 1994, Proceedings of the National Academy of Sciences of the United States of America.
[329] Christoph von der Malsburg,et al. The What and Why of Binding The Modeler’s Perspective , 1999, Neuron.
[330] R. Desimone,et al. A neural mechanism for working and recognition memory in inferior temporal cortex. , 1991, Science.
[331] Martin A. Giese,et al. Biophysiologically Plausible Implementations of the Maximum Operation , 2002, Neural Computation.
[332] Stephen Grossberg,et al. Contour Enhancement, Short Term Memory, and Constancies in Reverberating Neural Networks , 1973 .
[333] G. Orban,et al. Practising orientation identification improves orientation coding in V1 neurons , 2001, Nature.
[334] M. Behrmann,et al. Impact of learning on representation of parts and wholes in monkey inferotemporal cortex , 2002, Nature Neuroscience.
[335] J. Pernier,et al. Early signs of visual categorization for biological and non‐biological stimuli in humans , 2000, Neuroreport.
[336] Leslie G. Ungerleider,et al. Visual topography of area TEO in the macaque , 1991, The Journal of comparative neurology.
[337] David J. Freedman,et al. A Comparison of Primate Prefrontal and Inferior Temporal Cortices during Visual Categorization , 2003, The Journal of Neuroscience.
[338] Lior Wolf,et al. A Unified System For Object Detection, Texture Recognition, and Context Analysis Based on the Standard Model Feature Set , 2005, BMVC.
[339] A. Morel,et al. Segregated thalamocortical pathways to inferior parietal and inferotemporal cortex in macaque monkey , 1992, Visual Neuroscience.
[340] Rajesh P. N. Rao,et al. Probabilistic Models of the Brain: Perception and Neural Function , 2002 .
[341] Robert Desimone,et al. Impaired filtering of distracter stimuli by TE neurons following V4 and TEO lesions in macaques. , 2004, Cerebral cortex.
[342] J. Daugman. Two-dimensional spectral analysis of cortical receptive field profiles , 1980, Vision Research.
[343] Javid Sadr,et al. The Fidelity of Local Ordinal Encoding , 2001, NIPS.
[344] D. V. van Essen,et al. Neuronal responses to static texture patterns in area V1 of the alert macaque monkey. , 1992, Journal of neurophysiology.
[345] T. Poggio,et al. A synaptic mechanism possibly underlying directional selectivity to motion , 1978, Proceedings of the Royal Society of London. Series B. Biological Sciences.
[346] I. Biederman. Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.
[347] Tomaso Poggio,et al. Generalization in vision and motor control , 2004, Nature.
[348] P. Perona,et al. Rapid natural scene categorization in the near absence of attention , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[349] W. Newsome,et al. The Variable Discharge of Cortical Neurons: Implications for Connectivity, Computation, and Information Coding , 1998, The Journal of Neuroscience.
[350] Frank Rosenblatt,et al. PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS , 1963 .
[351] T. Poggio,et al. A feedforward theory of visual cortex accounts for human performance in rapid categorization , 2006 .
[352] R. Vogels. Categorization of complex visual images by rhesus monkeys. Part 2: single‐cell study , 1999, The European journal of neuroscience.
[353] M. Tarr,et al. Activation of the middle fusiform 'face area' increases with expertise in recognizing novel objects , 1999, Nature Neuroscience.
[354] K. Rockland,et al. Specific and columnar projection from area TEO to TE in the macaque inferotemporal cortex. , 1993, Cerebral cortex.
[355] C. Gilbert,et al. Learning to see: experience and attention in primary visual cortex , 2001, Nature Neuroscience.
[356] M. Tovée. Neuronal Processing: How fast is the speed of thought? , 1994, Current Biology.
[357] Thomas Serre,et al. Categorization by Learning and Combining Object Parts , 2001, NIPS.
[358] G. Bi,et al. Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type , 1998, The Journal of Neuroscience.
[359] Terence Sim,et al. The CMU Pose, Illumination, and Expression (PIE) database , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.
[360] Antonio Torralba,et al. Top-down control of visual attention in object detection , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).
[361] 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.
[362] P. H. Schiller,et al. The role of the primate extrastriate area V4 in vision. , 1991, Science.
[363] L. Abbott,et al. Invariant visual responses from attentional gain fields. , 1997, Journal of neurophysiology.
[364] H. Barlow,et al. The mechanism of directionally selective units in rabbit's retina. , 1965, The Journal of physiology.
[365] Antonino Casile,et al. Critical features for the recognition of biological motion. , 2005, Journal of vision.
[366] Arnaud Delorme,et al. Spike-based strategies for rapid processing , 2001, Neural Networks.
[367] Federico Girosi,et al. Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[368] Neil A. Macmillan,et al. Detection Theory: A User's Guide , 1991 .
[369] E. Rolls,et al. The Neurophysiology of Backward Visual Masking: Information Analysis , 1999, Journal of Cognitive Neuroscience.
[370] G. Rousselet,et al. Is it an animal? Is it a human face? Fast processing in upright and inverted natural scenes. , 2003, Journal of vision.
[371] D. Mumford. On the computational architecture of the neocortex , 2004, Biological Cybernetics.
[372] J. Bullier,et al. Functional interactions between areas V1 and V2 in the monkey , 1996, Journal of Physiology-Paris.
[373] M. Sur,et al. Visual behaviour mediated by retinal projections directed to the auditory pathway , 2000, Nature.
[374] S. Grossberg. Contour Enhancement , Short Term Memory , and Constancies in Reverberating Neural Networks , 1973 .
[375] Daniel Kersten,et al. Bayesian models of object perception , 2003, Current Opinion in Neurobiology.
[376] Rolls Et. Neurons in the cortex of the temporal lobe and in the amygdala of the monkey with responses selective for faces. , 1984 .
[377] Tomaso Poggio,et al. Face detection by humans and machines , 2004 .
[378] M. Tovée,et al. Processing speed in the cerebral cortex and the neurophysiology of visual masking , 1994, Proceedings of the Royal Society of London. Series B: Biological Sciences.
[379] I. Biederman. Perceiving Real-World Scenes , 1972, Science.
[380] Jitendra Malik,et al. Blobworld: A System for Region-Based Image Indexing and Retrieval , 1999, VISUAL.
[381] M. Potter. Meaning in visual search. , 1975, Science.
[382] M. Potter,et al. Recognition memory for briefly presented pictures: the time course of rapid forgetting. , 2002, Journal of experimental psychology. Human perception and performance.
[383] D Purves,et al. The distribution of oriented contours in the real world. , 1998, Proceedings of the National Academy of Sciences of the United States of America.
[384] A. Treisman,et al. A feature-integration theory of attention , 1980, Cognitive Psychology.
[385] Tomaso A. Poggio,et al. Pedestrian detection using wavelet templates , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[386] M. Alexander,et al. Principles of Neural Science , 1981 .
[387] D H HUBEL,et al. RECEPTIVE FIELDS AND FUNCTIONAL ARCHITECTURE IN TWO NONSTRIATE VISUAL AREAS (18 AND 19) OF THE CAT. , 1965, Journal of neurophysiology.
[388] E. Miller,et al. The prefontral cortex and cognitive control , 2000, Nature Reviews Neuroscience.
[389] Edmund T. Rolls,et al. Invariant recognition of feature combinations in the visual system , 2002, Biological Cybernetics.
[390] R. Desimone,et al. Prestriate afferents to inferior temporal cortex: an HRP study , 1980, Brain Research.
[391] Peter Ftildidk. Learning constancies for object perception , 2001 .
[392] E Corthout,et al. Timing of activity in early visual cortex as revealed by transcranial magnetic stimulation. , 1999, Neuroreport.
[393] T Poggio,et al. View-based models of 3D object recognition: invariance to imaging transformations. , 1995, Cerebral cortex.
[394] S. Yamane,et al. What facial features activate face neurons in the inferotemporal cortex of the monkey? , 2004, Experimental Brain Research.
[395] N. Logothetis,et al. View-dependent object recognition by monkeys , 1994, Current Biology.
[396] Terrence J. Sejnowski,et al. Slow Feature Analysis: Unsupervised Learning of Invariances , 2002, Neural Computation.
[397] Keiji Tanaka,et al. Functional architecture in monkey inferotemporal cortex revealed by in vivo optical imaging , 1998, Neuroscience Research.
[398] A. J. Mistlin,et al. Visual cells in the temporal cortex sensitive to face view and gaze direction , 1985, Proceedings of the Royal Society of London. Series B. Biological Sciences.
[399] J. Bullier,et al. Feedforward and feedback connections between areas V1 and V2 of the monkey have similar rapid conduction velocities. , 2001, Journal of neurophysiology.
[400] R. Desimone,et al. Responses of Macaque Perirhinal Neurons during and after Visual Stimulus Association Learning , 1999, The Journal of Neuroscience.
[401] T. Gawne. The simultaneous coding of orientation and contrast in the responses of V1 complex cells , 2000, Experimental Brain Research.
[402] Nicole C. Rust,et al. Do We Know What the Early Visual System Does? , 2005, The Journal of Neuroscience.
[403] Powen Ru,et al. Multiresolution spectrotemporal analysis of complex sounds. , 2005, The Journal of the Acoustical Society of America.
[404] 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.
[405] Tomaso Poggio,et al. A New Biologically Motivated Framework for Robust Object Recognition , 2004 .
[406] D. J. Felleman,et al. Distributed hierarchical processing in the primate cerebral cortex. , 1991, Cerebral cortex.
[407] D G Pelli,et al. The VideoToolbox software for visual psychophysics: transforming numbers into movies. , 1997, Spatial vision.
[408] Shimon Ullman,et al. Combining Class-Specific Fragments for Object Classification , 1999, BMVC.
[409] Keiji Tanaka. Mechanisms of visual object recognition: monkey and human studies , 1997, Current Opinion in Neurobiology.