Predictive Coding

Predictive coding is a unifying framework for understanding redundancy reduction and efficient coding in the nervous system. By transmitting only the unpredicted portions of an incoming sensory signal, predictive coding allows the nervous system to reduce redundancy and make full use of the limited dynamic range of neurons. Starting with the hypothesis of efficient coding as a design principle in the sensory system, predictive coding provides a functional explanation for a range of neural responses and many aspects of brain organization. The lateral and temporal antagonism in receptive fields in the retina and lateral geniculate nucleus occur naturally as a consequence of predictive coding of natural images. In the higher visual system, predictive coding provides an explanation for oriented receptive fields and contextual effects as well as the hierarchical reciprocally connected organization of the cortex. Predictive coding has also been found to be consistent with a variety of neurophysiological and psychophysical data obtained from different areas of the brain. WIREs Cogni Sci 2011 2 580-593 DOI: 10.1002/wcs.142 For further resources related to this article, please visit the WIREs website.

[1]  F. Attneave.  Some informational aspects of visual perception. , 1954, Psychological review.

[2]  D. Mackay.  The Epistemological Problem for Automata , 1956 .

[3]  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.

[4]  D. Hubel,et al.  Receptive fields and functional architecture of monkey striate cortex , 1968, The Journal of physiology.

[5]  P. Schiller,et al.  Effect of cooling area 18 on striate cortex cells in the squirrel monkey. , 1982, Journal of neurophysiology.

[6]  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.

[7]  D C Van Essen,et al.  Functional properties of neurons in middle temporal visual area of the macaque monkey. I. Selectivity for stimulus direction, speed, and orientation. , 1983, Journal of neurophysiology.

[8]  G. Buchsbaum,et al.  Trichromacy, opponent colours coding and optimum colour information transmission in the retina , 1983, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[9]  T. Albright.  Direction and orientation selectivity of neurons in visual area MT of the macaque. , 1984, Journal of neurophysiology.

[10]  J. Allman,et al.  Stimulus specific responses from beyond the classical receptive field: neurophysiological mechanisms for local-global comparisons in visual neurons. , 1985, Annual review of neuroscience.

[11]  C. Gilbert,et al.  Generation of end-inhibition in the visual cortex via interlaminar connections , 1986, Nature.

[12]  R. Desimone,et al.  Visual properties of neurons in area V4 of the macaque: sensitivity to stimulus form. , 1987, Journal of neurophysiology.

[13]  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.

[14]  DH Hubel,et al.  Segregation of form, color, and stereopsis in primate area 18 , 1987, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[15]  D J Field,et al.  Relations between the statistics of natural images and the response properties of cortical cells. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[16]  S. Zucker,et al.  Endstopped neurons in the visual cortex as a substrate for calculating curvature , 1987, Nature.

[17]  P. C. Murphy,et al.  Corticofugal feedback influences the generation of length tuning in the visual pathway , 1987, Nature.

[18]  Peter Földiák,et al.  Adaptation and decorrelation in the cortex , 1989 .

[19]  Richard Durbin,et al.  The computing neuron , 1989 .

[20]  A. L. Humphrey,et al.  Spatial and temporal response properties of lagged and nonlagged cells in cat lateral geniculate nucleus. , 1990, Journal of neurophysiology.

[21]  M. Mignard,et al.  Paths of information flow through visual cortex. , 1991, Science.

[22]  S. W. Kuffler,et al.  From Neuron to Brain: A Cellular and Molecular Approach to the Function of the Nervous System , 1992 .

[23]  A. Pece.  Redundancy reduction of a Gabor representation: a possible computational role for feedback from primary visual cortex to lateral geniculate nucleus , 1992 .

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

[25]  Ehud Kaplan,et al.  Information filtering in the lateral geniculate nucleus , 1993 .

[26]  William Bialek,et al.  Statistics of Natural Images: Scaling in the Woods , 1993, NIPS.

[27]  Geoffrey E. Hinton,et al.  The Helmholtz Machine , 1995, Neural Computation.

[28]  Alan S. Willsky,et al.  Likelihood calculation for a class of multiscale stochastic models, with application to texture discrimination , 1995, IEEE Trans. Image Process..

[29]  J. Atick,et al.  Temporal decorrelation: a theory of lagged and nonlagged responses in the lateral geniculate nucleus , 1995 .

[30]  J. Atick,et al.  STATISTICS OF NATURAL TIME-VARYING IMAGES , 1995 .

[31]  R. Desimone,et al.  Neural mechanisms for visual memory and their role in attention. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

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

[33]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[34]  R C Reid,et al.  Efficient Coding of Natural Scenes in the Lateral Geniculate Nucleus: Experimental Test of a Computational Theory , 1996, The Journal of Neuroscience.

[35]  David J. Field,et al.  Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.

[36]  Terrence J. Sejnowski,et al.  The “independent components” of natural scenes are edge filters , 1997, Vision Research.

[37]  Rajesh P. N. Rao,et al.  Dynamic Model of Visual Recognition Predicts Neural Response Properties in the Visual Cortex , 1997, Neural Computation.

[38]  D. Ruderman,et al.  INDEPENDENT COMPONENT ANALYSIS OF NATURAL IMAGE SEQUENCES YIELDS SPATIOTEMPORAL FILTERS SIMILAR TO SIMPLE CELLS IN PRIMARY VISUAL CORTEX , 1998 .

[39]  Jorma Rissanen,et al.  Stochastic Complexity in Statistical Inquiry , 1989, World Scientific Series in Computer Science.

[40]  D. Ruderman,et al.  Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex , 1998, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[41]  Rajesh P. N. Rao,et al.  Development of localized oriented receptive fields by learning a translation-invariant code for natural images. , 1998, Network.

[42]  Michael J. Berry,et al.  The Neural Code of the Retina , 1999, Neuron.

[43]  Rajesh P. N. Rao,et al.  An optimal estimation approach to visual perception and learning , 1999, Vision Research.

[44]  C. Connor,et al.  Responses to contour features in macaque area V4. , 1999, Journal of neurophysiology.

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

[46]  J. Hegdé,et al.  Selectivity for Complex Shapes in Primate Visual Area V2 , 2000, The Journal of Neuroscience.

[47]  O. Andreassen,et al.  Mice Deficient in Cellular Glutathione Peroxidase Show Increased Vulnerability to Malonate, 3-Nitropropionic Acid, and 1-Methyl-4-Phenyl-1,2,5,6-Tetrahydropyridine , 2000, The Journal of Neuroscience.

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

[49]  M. Mehta.  Neuronal Dynamics of Predictive Coding , 2001, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[50]  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.

[51]  Klaus Obermayer,et al.  Modeling the adaptive visual system: a survey of principled approaches , 2003, Neural Networks.

[52]  A. Dale,et al.  Human posterior auditory cortex gates novel sounds to consciousness. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

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

[54]  Dana H. Ballard,et al.  A single spike model of predictive coding , 2004, Neurocomputing.

[55]  Gustavo Deco,et al.  Predictive Coding in the Visual Cortex by a Recurrent Network with Gabor Receptive Fields , 2001, Neural Processing Letters.

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

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

[58]  Rajesh P. N. Rao,et al.  Bilinear Sparse Coding for Invariant Vision , 2005, Neural Computation.

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

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

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

[62]  J. O'Doherty,et al.  Predictive Neural Coding of Reward Preference Involves Dissociable Responses in Human Ventral Midbrain and Ventral Striatum , 2006, Neuron.

[63]  Michael S. Lewicki,et al.  Efficient auditory coding , 2006, Nature.

[64]  Jennifer A. Mangels,et al.  Predictive Codes for Forthcoming Perception in the Frontal Cortex , 2006, Science.

[65]  Rajesh P. N. Rao,et al.  Learning the Lie Groups of Visual Invariance , 2007, Neural Computation.

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

[67]  Michael W. Spratling.  Predictive coding as a model of biased competition in visual attention , 2008, Vision Research.

[68]  Karl J. Friston,et al.  Predictive coding explains binocular rivalry: An epistemological review , 2008, Cognition.

[69]  Karl J. Friston,et al.  Predictive coding under the free-energy principle , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.

[70]  A. Roepstorff,et al.  Predictive coding of music – Brain responses to rhythmic incongruity , 2009, Cortex.

[71]  Karl J. Friston,et al.  Action and behavior: a free-energy formulation , 2010, Biological Cybernetics.

[72]  Michael W. Spratling.  Predictive Coding as a Model of Response Properties in Cortical Area V1 , 2010, The Journal of Neuroscience.

[73]  Karl J. Friston,et al.  Predictive Coding or Evidence Accumulation? False Inference and Neuronal Fluctuations , 2010, PloS one.

[74]  Joseph J Atick,et al.  Could information theory provide an ecological theory of sensory processing? , 2011, Network.