Trade-off between curvature tuning and position invariance in visual area V4
暂无分享,去创建一个
[1] 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.
[2] P Kuyper,et al. Triggered correlation. , 1968, IEEE transactions on bio-medical engineering.
[3] D. Hubel,et al. Receptive fields and functional architecture of monkey striate cortex , 1968, The Journal of physiology.
[4] R. Shapley,et al. A method of nonlinear analysis in the frequency domain. , 1980, Biophysical journal.
[5] 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.
[6] S. Zucker,et al. Endstopped neurons in the visual cortex as a substrate for calculating curvature , 1987, Nature.
[7] Terrence J. Sejnowski,et al. Network model of shape-from-shading: neural function arises from both receptive and projective fields , 1988, Nature.
[8] William Bialek,et al. Real-time performance of a movement-sensitive neuron in the blowfly visual system: coding and information transfer in short spike sequences , 1988, Proceedings of the Royal Society of London. Series B. Biological Sciences.
[9] I. Ohzawa,et al. Spatiotemporal organization of simple-cell receptive fields in the cat's striate cortex. II. Linearity of temporal and spatial summation. , 1993, Journal of neurophysiology.
[10] D. C. Essen,et al. Neural responses to polar, hyperbolic, and Cartesian gratings in area V4 of the macaque monkey. , 1996, Journal of neurophysiology.
[11] R. Desimone,et al. Competitive Mechanisms Subserve Attention in Macaque Areas V2 and V4 , 1999, The Journal of Neuroscience.
[12] Michael J. Berry,et al. The Neural Code of the Retina , 1999, Neuron.
[13] Shimon Ullman,et al. Computation of pattern invariance in brain-like structures , 1999, Neural Networks.
[14] T. Poggio,et al. Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.
[15] C. Connor,et al. Responses to contour features in macaque area V4. , 1999, Journal of neurophysiology.
[16] I. Ohzawa,et al. Functional Micro-Organization of Primary Visual Cortex: Receptive Field Analysis of Nearby Neurons , 1999, The Journal of Neuroscience.
[17] Michael N. Shadlen,et al. Synchrony Unbound A Critical Evaluation of the Temporal Binding Hypothesis , 1999, Neuron.
[18] J L Gallant,et al. Sparse coding and decorrelation in primary visual cortex during natural vision. , 2000, Science.
[19] William Bialek,et al. Synergy in a Neural Code , 2000, Neural Computation.
[20] C. Connor,et al. Shape representation in area V4: position-specific tuning for boundary conformation. , 2001, Journal of neurophysiology.
[21] C. Connor,et al. Population coding of shape in area V4 , 2002, Nature Neuroscience.
[22] C. Gross. Genealogy of the “Grandmother Cell” , 2002, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.
[23] T. Gawne,et al. Responses of primate visual cortical V4 neurons to simultaneously presented stimuli. , 2002, Journal of neurophysiology.
[24] J. Touryan,et al. Isolation of Relevant Visual Features from Random Stimuli for Cortical Complex Cells , 2002, The Journal of Neuroscience.
[25] William Bialek,et al. Analyzing Neural Responses to Natural Signals: Maximally Informative Dimensions , 2002, Neural Computation.
[26] Ohad Ben-Shahar,et al. Geometrical Computations Explain Projection Patterns of Long-Range Horizontal Connections in Visual Cortex , 2004, Neural Computation.
[27] W. Bialek,et al. Features and dimensions: Motion estimation in fly vision , 2005, q-bio/0505003.
[28] Jeffrey S. Johnson,et al. The recognition of partially visible natural objects in the presence and absence of their occluders , 2005, Vision Research.
[29] Eero P. Simoncelli,et al. Spatiotemporal Elements of Macaque V1 Receptive Fields , 2005, Neuron.
[30] Eero P. Simoncelli,et al. Spike-triggered neural characterization. , 2006, Journal of vision.
[31] Kenneth D. Miller,et al. Adaptive filtering enhances information transmission in visual cortex , 2006, Nature.
[32] J. Gallant,et al. Spectral receptive field properties explain shape selectivity in area V4. , 2006, Journal of neurophysiology.
[33] T. Poggio,et al. A model of V4 shape selectivity and invariance. , 2007, Journal of neurophysiology.
[34] Thomas Serre,et al. Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[35] Anitha Pasupathy,et al. Transformation of shape information in the ventral pathway , 2007, Current Opinion in Neurobiology.
[36] D. C. Essen,et al. Neurons in monkey visual area V2 encode combinations of orientations , 2007, Nature Neuroscience.
[37] Tomaso Poggio,et al. Trade-Off between Object Selectivity and Tolerance in Monkey Inferotemporal Cortex , 2007, The Journal of Neuroscience.
[38] T. Sharpee. Comparison of information and variance maximization strategies for characterizing neural feature selectivity , 2007, Statistics in medicine.
[39] B. C. Motter. Central V4 Receptive Fields Are Scaled by the V1 Cortical Magnification and Correspond to a Constant-Sized Sampling of the V1 Surface , 2009, The Journal of Neuroscience.
[40] T. Sharpee,et al. Estimating linear–nonlinear models using Rényi divergences , 2009, Network.
[41] Lorenzo Rosasco,et al. On Invariance in Hierarchical Models , 2009, NIPS.
[42] Nicole C. Rust,et al. Selectivity and Tolerance (“Invariance”) Both Increase as Visual Information Propagates from Cortical Area V4 to IT , 2010, The Journal of Neuroscience.
[43] C. Connor,et al. Neural representations for object perception: structure, category, and adaptive coding. , 2011, Annual review of neuroscience.
[44] Wu Li,et al. Adaptive shape processing in primary visual cortex , 2011, Proceedings of the National Academy of Sciences.
[45] Bevil R. Conway,et al. Toward a Unified Theory of Visual Area V 4 , 2012 .
[46] Steven W. Zucker,et al. Shape-from-Shading and Cortical Computation: a new formulation , 2012 .
[47] James J. DiCarlo,et al. How Does the Brain Solve Visual Object Recognition? , 2012, Neuron.
[48] R. Quiroga. Concept cells: the building blocks of declarative memory functions , 2012, Nature Reviews Neuroscience.
[49] Michael Eickenberg,et al. Characterizing Responses of Translation-Invariant Neurons to Natural Stimuli: Maximally Informative Invariant Dimensions , 2012, Neural Computation.
[50] Bevil R. Conway,et al. Toward a Unified Theory of Visual Area V4 , 2012, Neuron.