Predictive coding as a model of biased competition in visual attention

[1]  HighWire Press Philosophical Transactions of the Royal Society of London , 1781, The London Medical Journal.

[2]  J. Deutsch Perception and Communication , 1958, Nature.

[3]  O. L. Zangwill,et al.  Current problems in animal behaviour , 1962 .

[4]  Frank Rosenblatt,et al.  PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS , 1963 .

[5]  J. Deutsch,et al.  Attention: Some theoretical considerations. , 1963 .

[6]  A. Treisman Strategies and models of selective attention. , 1969, Psychological review.

[7]  Walter Schneider,et al.  Controlled and automatic human information processing: II. Perceptual learning, automatic attending and a general theory. , 1977 .

[8]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.

[9]  M. Posner,et al.  Orienting of Attention* , 1980, The Quarterly journal of experimental psychology.

[10]  S. Laughlin,et al.  Predictive coding: a fresh view of inhibition in the retina , 1982, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[11]  S Ullman,et al.  Shifts in selective visual attention: towards the underlying neural circuitry. , 1985, Human neurobiology.

[12]  R. M. Siegel,et al.  Encoding of spatial location by posterior parietal neurons. , 1985, Science.

[13]  R. Desimone,et al.  Selective attention gates visual processing in the extrastriate cortex. , 1985, Science.

[14]  R. H. Phaf,et al.  SLAM: A connectionist model for attention in visual selection tasks , 1990, Cognitive Psychology.

[15]  Colin Blakemore,et al.  Vision: Coding and Efficiency , 1991 .

[16]  S. B. Laughlin,et al.  Vision: Coding efficiency and visual processing , 1991 .

[17]  D Mumford,et al.  On the computational architecture of the neocortex. II. The role of cortico-cortical loops. , 1992, Biological cybernetics.

[18]  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.

[19]  B. C. Motter Focal attention produces spatially selective processing in visual cortical areas V1, V2, and V4 in the presence of competing stimuli. , 1993, Journal of neurophysiology.

[20]  Joel L. Davis,et al.  Large-Scale Neuronal Theories of the Brain , 1994 .

[21]  J. Wolfe,et al.  Guided Search 2.0 A revised model of visual search , 1994, Psychonomic bulletin & review.

[22]  Horace Barlow,et al.  What is the computational goal of the neocortex , 1994 .

[23]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[24]  R. Andersen,et al.  Head position signals used by parietal neurons to encode locations of visual stimuli , 1995, Nature.

[25]  John K. Tsotsos,et al.  Modeling Visual Attention via Selective Tuning , 1995, Artif. Intell..

[26]  C. Malsburg Binding in models of perception and brain function , 1995, Current Opinion in Neurobiology.

[27]  G. Geffen,et al.  Event related potentials during covert orientation of visual attention: effects of cue validity and directionality , 1995, Biological Psychology.

[28]  A. Treisman The binding problem , 1996, Current Opinion in Neurobiology.

[29]  A. Burkhalter,et al.  Different Balance of Excitation and Inhibition in Forward and Feedback Circuits of Rat Visual Cortex , 1996, The Journal of Neuroscience.

[30]  R W Prager,et al.  Development of low entropy coding in a recurrent network. , 1996, Network.

[31]  E. Niebur,et al.  Modeling the Temporal Dynamics of IT Neurons in Visual Search: A Mechanism for Top-Down Selective Attention , 1996, Journal of Cognitive Neuroscience.

[32]  A. Burkhalter,et al.  A Polysynaptic Feedback Circuit in Rat Visual Cortex , 1997, The Journal of Neuroscience.

[33]  S. Luck,et al.  Bridging the Gap between Monkey Neurophysiology and Human Perception: An Ambiguity Resolution Theory of Visual Selective Attention , 1997, Cognitive Psychology.

[34]  L. Abbott,et al.  Invariant visual responses from attentional gain fields. , 1997, Journal of neurophysiology.

[35]  D. V. van Essen,et al.  Spatial Attention Effects in Macaque Area V4 , 1997, The Journal of Neuroscience.

[36]  R. Desimone,et al.  Neural mechanisms of spatial selective attention in areas V1, V2, and V4 of macaque visual cortex. , 1997, Journal of neurophysiology.

[37]  C. Koch,et al.  Constraints on cortical and thalamic projections: the no-strong-loops hypothesis , 1998, Nature.

[38]  A Treisman,et al.  Feature binding, attention and object perception. , 1998, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[39]  S. Thorpe Localized versus distributed representations , 1998 .

[40]  S. Luck,et al.  On the role of selective attention in visual perception. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[41]  J. M. Hupé,et al.  Cortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons , 1998, Nature.

[42]  R. Desimone,et al.  Competitive Mechanisms Subserve Attention in Macaque Areas V2 and V4 , 1999, The Journal of Neuroscience.

[43]  T. Poggio,et al.  Are Cortical Models Really Bound by the “Binding Problem”? , 1999, Neuron.

[44]  Dietmar Heinke,et al.  Connectionist Models in Cognitive Neuroscience , 1999, Perspectives in Neural Computing.

[45]  Stefan Treue,et al.  Feature-based attention influences motion processing gain in macaque visual cortex , 1999, Nature.

[46]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[47]  F. Hamker The role of feedback connections in task-driven visual search , 1999 .

[48]  Carrie J. McAdams,et al.  Effects of Attention on Orientation-Tuning Functions of Single Neurons in Macaque Cortical Area V4 , 1999, The Journal of Neuroscience.

[49]  T. Poggio,et al.  Predicting the visual world: silence is golden , 1999, Nature Neuroscience.

[50]  R. Desimone,et al.  The Role of Neural Mechanisms of Attention in Solving the Binding Problem , 1999, Neuron.

[51]  Rajesh P. N. Rao,et al.  Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. , 1999 .

[52]  Leslie G. Ungerleider,et al.  Microsaccadic eye movements and firing of single cells in the striate cortex of macaque monkeys , 2000, Nature Neuroscience.

[53]  M. Gazzaniga,et al.  The new cognitive neurosciences , 2000 .

[54]  Erratum: Loss of attentional stimulus selection after extrastriate cortical lesions in macaques , 2000, Nature neuroscience.

[55]  Victor A. F. Lamme,et al.  The implementation of visual routines , 2000, Vision Research.

[56]  I. Fujita,et al.  Neuronal mechanisms of selectivity for object features revealed by blocking inhibition in inferotemporal cortex , 2000, Nature Neuroscience.

[57]  Leslie G. Ungerleider,et al.  Mechanisms of visual attention in the human cortex. , 2000, Annual review of neuroscience.

[58]  Shaun P. Vecera,et al.  Toward a Biased Competition Account of Object-Based Segregation and Attention , 2000 .

[59]  S. Grossberg,et al.  Contrast-sensitive perceptual grouping and object-based attention in the laminar circuits of primary visual cortex , 2000, Vision Research.

[60]  Emilio Salinas,et al.  Gain Modulation A Major Computational Principle of the Central Nervous System , 2000, Neuron.

[61]  R. Desimone,et al.  Attention Increases Sensitivity of V4 Neurons , 2000, Neuron.

[62]  J Duncan,et al.  Responses of neurons in macaque area V4 during memory-guided visual search. , 2001, Cerebral cortex.

[63]  Rajesh P. N. Rao,et al.  Predictive Coding , Cortical Feedback , and Spike-Timing Dependent Plasticity , 2001 .

[64]  C. Koch,et al.  Computational modelling of visual attention , 2001, Nature Reviews Neuroscience.

[65]  H Barlow,et al.  Redundancy reduction revisited , 2001, Network.

[66]  John J. Foxe,et al.  Determinants and mechanisms of attentional modulation of neural processing. , 2001, Frontiers in Bioscience.

[67]  Michael W. Spratling,et al.  Dendritic inhibition enhances neural coding properties. , 2001, Cerebral cortex.

[68]  S. Treue Neural correlates of attention in primate visual cortex , 2001, Trends in Neurosciences.

[69]  Carl R Olson,et al.  Object-based vision and attention in primates , 2001, Current Opinion in Neurobiology.

[70]  S. Treue,et al.  Attentional Modulation Strength in Cortical Area MT Depends on Stimulus Contrast , 2002, Neuron.

[71]  Gustavo Deco,et al.  Large-scale neural model for visual attention: integration of experimental single-cell and fMRI data. , 2002, Cerebral cortex.

[72]  Gustavo Deco,et al.  Computational neuroscience of vision , 2002 .

[73]  G. Deco,et al.  The time course of selective visual attention: theory and experiments , 2002, Vision Research.

[74]  Dietmar Heinke,et al.  Modelling visual search experiments: the selective attention for identification model (SAIM) , 2002, Neurocomputing.

[75]  Fred H. Hamker,et al.  How does the ventral pathway contribute to spatial attention and the planning of eye movements , 2002 .

[76]  Michael W. Spratling,et al.  Preintegration Lateral Inhibition Enhances Unsupervised Learning , 2002, Neural Computation.

[77]  J. Duncan,et al.  Filtering of neural signals by focused attention in the monkey prefrontal cortex , 2002, Nature Neuroscience.

[78]  Michael W. Spratling,et al.  Exploring the functional significance of dendritic inhibition in cortical pyramidal cells , 2003, Neurocomputing.

[79]  G. Humphreys,et al.  Attention, spatial representation, and visual neglect: simulating emergent attention and spatial memory in the selective attention for identification model (SAIM). , 2003, Psychological review.

[80]  Tai Sing Lee,et al.  Hierarchical Bayesian inference in the visual cortex. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[81]  R. Desimone,et al.  Interacting Roles of Attention and Visual Salience in V4 , 2003, Neuron.

[82]  S. Treue,et al.  Feature-Based Attention Increases the Selectivity of Population Responses in Primate Visual Cortex , 2004, Current Biology.

[83]  Michael W. Spratling,et al.  A Feedback Model of Visual Attention , 2004, Journal of Cognitive Neuroscience.

[84]  W. Senn,et al.  Top-down dendritic input increases the gain of layer 5 pyramidal neurons. , 2004, Cerebral cortex.

[85]  D. Mumford On the computational architecture of the neocortex , 2004, Biological Cybernetics.

[86]  Rajesh P. N. Rao Hierarchical Bayesian Inference in Networks of Spiking Neurons , 2004, NIPS.

[87]  Laurent Itti,et al.  Automatic foveation for video compression using a neurobiological model of visual attention , 2004, IEEE Transactions on Image Processing.

[88]  J. Reynolds,et al.  Attentional modulation of visual processing. , 2004, Annual review of neuroscience.

[89]  Scott O. Murray,et al.  Perceptual grouping and the interactions between visual cortical areas , 2004, Neural Networks.

[90]  Rajesh P. N. Rao,et al.  CHAPTER 91 – Probabilistic Models of Attention Based on Iconic Representations and Predictive Coding , 2005 .

[91]  G. Boynton Attention and visual perception , 2005, Current Opinion in Neurobiology.

[92]  M. Meister,et al.  Dynamic predictive coding by the retina , 2005, Nature.

[93]  Karl J. Friston,et al.  A theory of cortical responses , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[94]  Rajesh P. N. Rao,et al.  Neurobiology of Attention , 2005 .

[95]  E. Rolls,et al.  Neurodynamics of biased competition and cooperation for attention: a model with spiking neurons. , 2005, Journal of neurophysiology.

[96]  Roger Orpwood Cortical Pyramidal Cells , 2005 .

[97]  R. Orpwood Cortical Pyramidal Cells , 2005 .

[98]  Arun P. Sripati,et al.  Dynamic Gain Changes During Attentional Modulation , 2006, Neural Computation.

[99]  J. Maunsell,et al.  Effects of spatial attention on contrast response functions in macaque area V4. , 2006, Journal of neurophysiology.

[100]  Dana H. Ballard,et al.  Learning receptive fields using predictive feedback , 2006, Journal of Physiology-Paris.

[101]  Fred H Hamker,et al.  Modeling feature-based attention as an active top-down inference process. , 2006, Bio Systems.

[102]  P. Roelfsema Cortical algorithms for perceptual grouping. , 2006, Annual review of neuroscience.

[103]  Gereon R. Fink,et al.  Cue validity modulates the neural correlates of covert endogenous orienting of attention in parietal and frontal cortex , 2006, NeuroImage.

[104]  Laurent Itti,et al.  An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[105]  T. Womelsdorf,et al.  Dynamic shifts of visual receptive fields in cortical area MT by spatial attention , 2006, Nature Neuroscience.

[106]  Xiao-Jing Wang,et al.  An Integrated Microcircuit Model of Attentional Processing in the Neocortex , 2007, The Journal of Neuroscience.

[107]  Karl J. Friston,et al.  Predictive coding: an account of the mirror neuron system , 2007, Cognitive Processing.

[108]  N. Spruston Pyramidal neurons: dendritic structure and synaptic integration , 2008, Nature Reviews Neuroscience.

[109]  Michael W. Spratling Reconciling Predictive Coding and Biased Competition Models of Cortical Function , 2008, Frontiers Comput. Neurosci..

[110]  Michael W. Spratling,et al.  Unsupervised Learning of Overlapping Image Components Using Divisive Input Modulation , 2009, Comput. Intell. Neurosci..

[111]  C. Koch Strategies and Models of Selective Attention , 2010 .