Reconciling Predictive Coding and Biased Competition Models of Cortical Function

A simple variation of the standard biased competition model is shown, via some trivial mathematical manipulations, to be identical to predictive coding. Specifically, it is shown that a particular implementation of the biased competition model, in which nodes compete via inhibition that targets the inputs to a cortical region, is mathematically equivalent to the linear predictive coding model. This observation demonstrates that these two important and influential rival theories of cortical function are minor variations on the same underlying mathematical model.

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

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

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

[4]  J. Hegdé,et al.  Temporal dynamics of 2 D and 3 D shape representation in macaque visual area V 4 , 2006 .

[5]  R. Goebel,et al.  The role of feedback in shaping neural representations in cat visual cortex , 2002, Proceedings of the National Academy of Sciences of the United States of America.

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

[7]  F. Hamker A dynamic model of how feature cues guide spatial attention , 2004, Vision Research.

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

[9]  Victor A. F. Lamme,et al.  Feedforward, horizontal, and feedback processing in the visual cortex , 1998, Current Opinion in Neurobiology.

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

[11]  Michael W. Spratling,et al.  The role of feedback in the determination of figure and ground: a combined behavioral and modeling study , 2007 .

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

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

[14]  Michael W. Spratling,et al.  Pre-synaptic lateral inhibition provides a better architecture for self-organizing neural networks. , 1999, Network.

[15]  D. Mumford,et al.  The role of the primary visual cortex in higher level vision , 1998, Vision Research.

[16]  Tai Sing Lee,et al.  Computations in the early visual cortex , 2003, Journal of Physiology-Paris.

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

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

[19]  Paul Schrater,et al.  Shape perception reduces activity in human primary visual cortex , 2002, Proceedings of the National Academy of Sciences of the United States of America.

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

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

[22]  D. J. Felleman,et al.  Distributed hierarchical processing in the primate cerebral cortex. , 1991, Cerebral cortex.

[23]  David J. Field,et al.  How Close Are We to Understanding V1? , 2005, Neural Computation.

[24]  H. Barbas,et al.  Cortical structure predicts the pattern of corticocortical connections. , 1997, Cerebral cortex.

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

[26]  H. Kennedy,et al.  Laminar Distribution of Neurons in Extrastriate Areas Projecting to Visual Areas V1 and V4 Correlates with the Hierarchical Rank and Indicates the Operation of a Distance Rule , 2000, The Journal of Neuroscience.

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

[28]  F. Hamker The reentry hypothesis: the putative interaction of the frontal eye field, ventrolateral prefrontal cortex, and areas V4, IT for attention and eye movement. , 2005, Cerebral cortex.

[29]  Rodney J. Douglas,et al.  Attentional Recruitment of Inter-Areal Recurrent Networks for Selective Gain Control , 2002, Neural Computation.

[30]  J. Budd Extrastriate feedback to primary visual cortex in primates: a quantitative analysis of connectivity , 1998, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[31]  Karl J. Friston,et al.  Extra-classical receptive field effects measured in striate cortex with fMRI , 2007, NeuroImage.

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

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

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

[35]  Bruno A. Olshausen,et al.  Principles of Image Representation in Visual Cortex , 2003 .

[36]  Heiko Neumann,et al.  A neural model of feature attention in motion perception , 2007, Biosyst..

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

[38]  Heiko Neumann,et al.  Disambiguating Visual Motion Through Contextual Feedback Modulation , 2004, Neural Computation.

[39]  J. Hegdé,et al.  Temporal dynamics of 2D and 3D shape representation in macaque visual area V4 , 2006, Visual Neuroscience.

[40]  K. Obermayer,et al.  The Role of Feedback in Shaping the Extra-Classical Receptive Field of Cortical Neurons: A Recurrent Network Model , 2006, The Journal of Neuroscience.

[41]  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.

[42]  Victor A. F. Lamme,et al.  Contextual Modulation in Primary Visual Cortex , 1996, The Journal of Neuroscience.

[43]  R. Desimone,et al.  Neural mechanisms of selective visual attention. , 1995, Annual review of neuroscience.

[44]  Andreas Burkhalter,et al.  Microcircuitry of forward and feedback connections within rat visual cortex , 1996, The Journal of comparative neurology.

[45]  Antonio Torralba,et al.  Building the gist of a scene: the role of global image features in recognition. , 2006, Progress in brain research.

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

[47]  R VanRullen,et al.  Is it a Bird? Is it a Plane? Ultra-Rapid Visual Categorisation of Natural and Artifactual Objects , 2001, Perception.

[48]  A. Borst Seeing smells: imaging olfactory learning in bees , 1999, Nature Neuroscience.

[49]  John H. R. Maunsell,et al.  Attention to both space and feature modulates neuronal responses in macaque area V4. , 2000, Journal of neurophysiology.

[50]  S. Hochstein,et al.  View from the Top Hierarchies and Reverse Hierarchies in the Visual System , 2002, Neuron.

[51]  C. Schroeder,et al.  Intermodal selective attention in monkeys. II: physiological mechanisms of modulation. , 2000, Cerebral cortex.

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

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

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

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

[56]  A. Yuille,et al.  Object perception as Bayesian inference. , 2004, Annual review of psychology.