A model of V4 shape selectivity and invariance.

Object recognition in primates is mediated by the ventral visual pathway and is classically described as a feedforward hierarchy of increasingly sophisticated representations. Neurons in macaque monkey area V4, an intermediate stage along the ventral pathway, have been shown to exhibit selectivity to complex boundary conformation and invariance to spatial translation. How could such a representation be derived from the signals in lower visual areas such as V1? We show that a quantitative model of hierarchical processing, which is part of a larger model of object recognition in the ventral pathway, provides a plausible mechanism for the translation-invariant shape representation observed in area V4. Simulated model neurons successfully reproduce V4 selectivity and invariance through a nonlinear, translation-invariant combination of locally selective subunits, suggesting that a similar transformation may occur or culminate in area V4. Specifically, this mechanism models the selectivity of individual V4 neurons to boundary conformation stimuli, exhibits the same degree of translation invariance observed in V4, and produces observed V4 population responses to bars and non-Cartesian gratings. This work provides a quantitative model of the widely described shape selectivity and invariance properties of area V4 and points toward a possible canonical mechanism operating throughout the ventral pathway.

[1]  J. Hegdé,et al.  A comparative study of shape representation in macaque visual areas v2 and v4. , 2007, Cerebral cortex.

[2]  Thomas Serre,et al.  A feedforward architecture accounts for rapid categorization , 2007, Proceedings of the National Academy of Sciences.

[3]  Thomas Serre,et al.  Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  J. Movshon,et al.  Motion Integration by Neurons in Macaque MT Is Local, Not Global , 2007, The Journal of Neuroscience.

[5]  J. Gallant,et al.  Spectral receptive field properties explain shape selectivity in area V4. , 2006, Journal of neurophysiology.

[6]  Eero P. Simoncelli,et al.  How MT cells analyze the motion of visual patterns , 2006, Nature Neuroscience.

[7]  J. Gallant,et al.  Complete functional characterization of sensory neurons by system identification. , 2006, Annual review of neuroscience.

[8]  Scott L. Brincat,et al.  Dynamic Shape Synthesis in Posterior Inferotemporal Cortex , 2006, Neuron.

[9]  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 .

[10]  Tomaso Poggio,et al.  Fast Readout of Object Identity from Macaque Inferior Temporal Cortex , 2005, Science.

[11]  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.

[12]  Tomaso Poggio,et al.  Generalization in vision and motor control , 2004, Nature.

[13]  Charles E Connor,et al.  Underlying principles of visual shape selectivity in posterior inferotemporal cortex , 2004, Nature Neuroscience.

[14]  D. Ringach.  Mapping receptive fields in primary visual cortex , 2004, The Journal of physiology.

[15]  Doris Y. Tsao,et al.  Complex and dynamic receptive field structure in macaque cortical area V4d , 2004 .

[16]  Heather J. Chisum,et al.  The contribution of vertical and horizontal connections to the receptive field center and surround in V1 , 2004, Neural Networks.

[17]  Minami Ito,et al.  Representation of Angles Embedded within Contour Stimuli in Area V2 of Macaque Monkeys , 2004, The Journal of Neuroscience.

[18]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[19]  T. Poggio,et al.  Considerations on models of movement detection , 1973, Kybernetik.

[20]  J. Gallant,et al.  Goal-Related Activity in V4 during Free Viewing Visual Search Evidence for a Ventral Stream Visual Salience Map , 2003, Neuron.

[21]  Maximilian Riesenhuber,et al.  Investigating shape representation in area V4 with HMAX: Orientation and Grating selectivities , 2003 .

[22]  J. Hegdé,et al.  Strategies of shape representation in macaque visual area V2 , 2003, Visual Neuroscience.

[23]  C. Connor,et al.  Population coding of shape in area V4 , 2002, Nature Neuroscience.

[24]  Martin A. Giese,et al.  Biophysiologically Plausible Implementations of the Maximum Operation , 2002, Neural Computation.

[25]  Jean Bullier,et al.  Shape discrimination deficits during reversible deactivation of area V4 in the macaque monkey. , 2002, Cerebral cortex.

[26]  T. Gawne,et al.  Responses of primate visual cortical neurons to stimuli presented by flash, saccade, blink, and external darkening. , 2002, Journal of neurophysiology.

[27]  T. Gawne,et al.  Responses of primate visual cortical V4 neurons to simultaneously presented stimuli. , 2002, Journal of neurophysiology.

[28]  C. Connor,et al.  Three-dimensional orientation tuning in macaque area V4 , 2002, Nature Neuroscience.

[29]  D. Pollen,et al.  Spatial receptive field organization of macaque V4 neurons. , 2002, Cerebral cortex.

[30]  Terrence J. Sejnowski,et al.  Slow Feature Analysis: Unsupervised Learning of Invariances , 2002, Neural Computation.

[31]  C. Connor,et al.  Shape representation in area V4: position-specific tuning for boundary conformation. , 2001, Journal of neurophysiology.

[32]  R. L. de Valois,et al.  Cartesian and non-Cartesian responses in LGN, V1, and V2 cells , 2001, Visual Neuroscience.

[33]  J. Gallant,et al.  A Human Extrastriate Area Functionally Homologous to Macaque V4 , 2000, Neuron.

[34]  Bartlett W. Mel,et al.  Minimizing Binding Errors Using Learned Conjunctive Features , 2000, Neural Computation.

[35]  D J Felleman,et al.  Segregation and convergence of functionally defined V2 thin stripe and interstripe compartment projections to area V4 of macaques. , 1999, Cerebral cortex.

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

[37]  T. Poggio,et al.  Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.

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

[39]  H. Wilson,et al.  Detection of global structure in Glass patterns: implications for form vision , 1998, Vision Research.

[40]  Bartlett W. Mel,et al.  Translation-Invariant Orientation Tuning in Visual “Complex” Cells Could Derive from Intradendritic Computations , 1998, The Journal of Neuroscience.

[41]  Zhaoping Li,et al.  A Neural Model of Contour Integration in the Primary Visual Cortex , 1998, Neural Computation.

[42]  H. A. Pham,et al.  V4 lesions in macaques affect both single- and multiple-viewpoint shape discriminations , 1998, Visual Neuroscience.

[43]  J. Movshon,et al.  Linearity and Normalization in Simple Cells of the Macaque Primary Visual Cortex , 1997, The Journal of Neuroscience.

[44]  Bartlett W. Mel.  SEEMORE: Combining Color, Shape, and Texture Histogramming in a Neurally Inspired Approach to Visual Object Recognition , 1997, Neural Computation.

[45]  Guy M. Wallis,et al.  Using Spatio-temporal Correlations to Learn Invariant Object Recognition , 1996, Neural Networks.

[46]  D. C. Essen,et al.  Neural responses to polar, hyperbolic, and Cartesian gratings in area V4 of the macaque monkey. , 1996, Journal of neurophysiology.

[47]  Denis Fize,et al.  Speed of processing in the human visual system , 1996, Nature.

[48]  Leslie G. Ungerleider,et al.  Cue-dependent deficits in grating orientation discrimination after V4 lesions in macaques , 1996, Visual Neuroscience.

[49]  S. Zeki,et al.  Segregation and convergence of specialised pathways in macaque monkey visual cortex. , 1995, Journal of anatomy.

[50]  P. H. Schiller.  Effect of lesions in visual cortical area V4 on the recognition of transformed objects , 1995, Nature.

[51]  N. Logothetis,et al.  Shape representation in the inferior temporal cortex of monkeys , 1995, Current Biology.

[52]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[53]  Leslie G. Ungerleider,et al.  ‘What’ and ‘where’ in the human brain , 1994, Current Opinion in Neurobiology.

[54]  Keiji Tanaka,et al.  Neuronal selectivities to complex object features in the ventral visual pathway of the macaque cerebral cortex. , 1994, Journal of neurophysiology.

[55]  Kechen Zhang,et al.  Emergence of Position-Independent Detectors of Sense of Rotation and Dilation with Hebbian Learning: An Analysis , 1999, Neural Computation.

[56]  D. Heeger.  Modeling simple-cell direction selectivity with normalized, half-squared, linear operators. , 1993, Journal of neurophysiology.

[57]  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.

[58]  David I. Perrett,et al.  Neurophysiology of shape processing , 1993, Image Vis. Comput..

[59]  Keiji Tanaka,et al.  Coding visual images of objects in the inferotemporal cortex of the macaque monkey. , 1991, Journal of neurophysiology.

[60]  Peter Földiák,et al.  Learning Invariance from Transformation Sequences , 1991, Neural Comput..

[61]  P. H. Schiller,et al.  The role of the primate extrastriate area V4 in vision. , 1991, Science.

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

[63]  R. Desimone,et al.  Spectral properties of V4 neurons in the macaque , 1990, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[64]  J. P. Jones,et al.  The two-dimensional spatial structure of simple receptive fields in cat striate cortex. , 1987, Journal of neurophysiology.

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

[66]  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.

[67]  Takayuki Ito,et al.  Neocognitron: A neural network model for a mechanism of visual pattern recognition , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[68]  R. L. Valois,et al.  The orientation and direction selectivity of cells in macaque visual cortex , 1982, Vision Research.

[69]  J. Daugman.  Two-dimensional spectral analysis of cortical receptive field profiles , 1980, Vision Research.

[70]  T. Poggio,et al.  The Volterra Representation and the Wiener Expansion: Validity and Pitfalls , 1977 .

[71]  J. Baizer,et al.  Visual responses of area 18 neurons in awake, behaving monkey. , 1977, Journal of neurophysiology.

[72]  P. Schiller,et al.  Quantitative studies of single-cell properties in monkey striate cortex. III. Spatial frequency. , 1976, Journal of neurophysiology.

[73]  D. B. Bender,et al.  Visual properties of neurons in inferotemporal cortex of the Macaque. , 1972, Journal of neurophysiology.

[74]  E. Bedrosian,et al.  The output properties of Volterra systems (nonlinear systems with memory) driven by harmonic and Gaussian inputs , 1971 .

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

[76]  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.

[77]  Parag A. Pathak,et al.  Massachusetts Institute of Technology , 1964, Nature.

[78]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.