Modeling neuronal response to disparity gradient

There is a rich literature of physiological studies that a subset of neurons in visual cortices is discriminative of 3-D surface orientation using only the disparity gradient information. One of the physiological models to account for this sensibility to surface slant is the dif-frequency disparity model. Although this model is physiologically plausible, no computational analysis is available to explain how first-order-disparity sensitive neurons detect slanted surface. In this paper a computational model based on the dif-frequency disparity model is presented. In particular, analytical expressions that fit well with neuronal responses to broadband stimuli are obtained when simple cell receptive field is described by log-Gabor filters. It is shown with mathematical analysis and numerical simulations that our proposed model can not only account for physiological data of neuronal response to surface slant but also detect disparity gradient from random dot and sinusoidal grating stereograms.

[1]  David J. Fleet,et al.  Neural encoding of binocular disparity: Energy models, position shifts and phase shifts , 1996, Vision Research.

[2]  Jerry D. Nguyenkim,et al.  Disparity-Based Coding of Three-Dimensional Surface Orientation by Macaque Middle Temporal Neurons , 2003, The Journal of Neuroscience.

[3]  H. Sakata,et al.  Parietal neurons represent surface orientation from the gradient of binocular disparity. , 2000, Journal of neurophysiology.

[4]  J P Frisby,et al.  PMF: A Stereo Correspondence Algorithm Using a Disparity Gradient Limit , 1985, Perception.

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

[6]  R A Young,et al.  The Gaussian derivative model for spatial vision: I. Retinal mechanisms. , 1988, Spatial vision.

[7]  B. Julesz,et al.  Modifications of the Classical Notion of Panum's Fusional Area , 1980, Perception.

[8]  Ning Qian,et al.  Relationship Between Phase and Energy Methods for Disparity Computation , 2000, Neural Computation.

[9]  J. Pokorny.  Foundations of Cyclopean Perception , 1972 .

[10]  C. Blakemore,et al.  A new kind of stereoscopic vision. , 1970, Vision research.

[11]  A. Parker,et al.  Spatial properties of neurons in the monkey striate cortex , 1987, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[12]  G. Orban,et al.  Three-Dimensional Shape Coding in Inferior Temporal Cortex , 2000, Neuron.

[13]  Takahisa M. Sanada,et al.  Encoding of three-dimensional surface slant in cat visual areas 17 and 18. , 2006, Journal of neurophysiology.

[14]  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.

[15]  I. Ohzawa,et al.  Stereoscopic depth discrimination in the visual cortex: neurons ideally suited as disparity detectors. , 1990, Science.

[16]  Jenny C. A. Read.  A Bayesian model of stereopsis depth and motion direction discrimination , 2002, Biological Cybernetics.

[17]  Ning Qian,et al.  A Coarse-to-Fine Disparity Energy Model with Both Phase-Shift and Position-Shift Receptive Field Mechanisms , 2004, Neural Computation.

[18]  C. Tyler,et al.  Depth from spatial frequency difference: An old kind of stereopsis? , 1979, Vision Research.

[19]  D. Burr,et al.  Feature detection in human vision: a phase-dependent energy model , 1988, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[20]  B. Cumming,et al.  Testing quantitative models of binocular disparity selectivity in primary visual cortex. , 2003, Journal of neurophysiology.

[21]  Jonathan D. Victor,et al.  Reading a population code: a multi-scale neural model for representing binocular disparity , 2003, Vision Research.

[22]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[23]  D. G. Albrecht,et al.  Spatial frequency selectivity of cells in macaque visual cortex , 1982, Vision Research.

[24]  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.

[25]  Ning Qian,et al.  Computing Stereo Disparity and Motion with Known Binocular Cell Properties , 1994, Neural Computation.

[26]  Jay Hegdé,et al.  Role of primate visual area V4 in the processing of 3-D shape characteristics defined by disparity. , 2005, Journal of neurophysiology.

[27]  Hideo Sakata,et al.  Neural mechanisms of three-dimensional vision , 2005, Neuroscience Research.

[28]  Peter Janssen,et al.  Anterior Regions of Monkey Parietal Cortex Process Visual 3D Shape , 2007, Neuron.

[29]  D. Pollen,et al.  Phase relationships between adjacent simple cells in the visual cortex. , 1981, Science.

[30]  Hong Zhou,et al.  Representation of stereoscopic edges in monkey visual cortex , 2000, Vision Research.

[31]  G. Orban,et al.  Macaque Inferior Temporal Neurons Are Selective for Three-Dimensional Boundaries and Surfaces , 2001, The Journal of Neuroscience.