A multiscale dynamic routing circuit for forming size- and position-invariant object representations

We describe a neural model for forming size- and position-invariant representations of visual objects. The model is based on a previously proposed dynamic routing circuit that remaps selected portions of an input array into an object-centered reference frame. Here, we show how a multiscale representation may be incorporated at the input stage of the model, and we describe the control architecture and dynamics for a hierarchical, multistage routing circuit. Specific neurobiological substrates and mechanisms for the model are proposed, and a number of testable predictions are described.

[1]  Geoffrey E. Hinton.  A Parallel Computation that Assigns Canonical Object-Based Frames of Reference , 1981, IJCAI.

[2]  C. Eriksen,et al.  Visual attention within and around the field of focal attention: A zoom lens model , 1986, Perception & psychophysics.

[3]  B. Olshausen.  Neural routing circuits for forming invariant representations of visual objects , 1994 .

[4]  B L McNaughton,et al.  Dynamics of the hippocampal ensemble code for space. , 1993, Science.

[5]  A. Parker,et al.  Two-dimensional spatial structure of receptive fields in monkey striate cortex. , 1988, Journal of the Optical Society of America. A, Optics and image science.

[6]  D C Van Essen,et al.  Shifter circuits: a computational strategy for dynamic aspects of visual processing. , 1987, Proceedings of the National Academy of Sciences of the United States of America.

[7]  P. J. Burt,et al.  Change Detection and Tracking Using Pyramid Transform Techniques , 1985, Other Conferences.

[8]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[9]  David G. Lowe,et al.  Perceptual Organization and Visual Recognition , 2012 .

[10]  David C. Van Essen,et al.  Information processing strategies and pathways in the primate retina and visual cortex , 1990 .

[11]  John H. R. Maunsell,et al.  The projections from striate cortex (V1) to areas V2 and V3 in the macaque monkey: Asymmetries, areal boundaries, and patchy connections , 1986, The Journal of comparative neurology.

[12]  R. Baron.  The cerebral computer , 1987 .

[13]  K. Rockland,et al.  Configuration, in serial reconstruction, of individual axons projecting from area V2 to V4 in the macaque monkey. , 1992, Cerebral cortex.

[14]  Bruno A. Olshausen,et al.  Pattern recognition, attention, and information bottlenecks in the primate visual system , 1991, Defense, Security, and Sensing.

[15]  E. Switkes,et al.  Functional anatomy of macaque striate cortex. V. Spatial frequency , 1988, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[16]  David LaBerge,et al.  A Network Simulation of Thalamic Circuit Operations in Selective Attention , 1992, Neural Computation.

[17]  E. L. Schwartz,et al.  Spatial mapping in the primate sensory projection: Analytic structure and relevance to perception , 1977, Biological Cybernetics.

[18]  Joachim M. Buhmann,et al.  Size and distortion invariant object recognition by hierarchical graph matching , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[19]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[20]  Bruno A. Olshausen,et al.  A Nonlinear Hebbian Network that Learns to Detect Disparity in Random-Dot Stereograms , 1996, Neural Computation.

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

[22]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[23]  Bruno A. Olshausen,et al.  A Model of the Spatial-Frequency Organization in Primate Striate Cortex , 1995 .

[24]  Christof Koch,et al.  A model for the neuronal implementation of selective visual attention based on temporal correlation among neurons , 1994, Journal of Computational Neuroscience.

[25]  Eric O. Postma,et al.  The Gating Lattice: A Neural Substrate for Dynamic Gating , 1993 .

[26]  A. Trehub,et al.  Neuronal models for cognitive processes: networks for learning, perception and imagination. , 1977, Journal of theoretical biology.

[27]  Leif H. Finkel,et al.  Dual Mechanisms for Neural Binding and Segmentation , 1993, NIPS.

[28]  Tomaso Poggio,et al.  Cooperative computation of stereo disparity , 1988 .

[29]  John K. Tsotsos.  Toward a computational model of visual attention , 1995 .

[30]  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.

[31]  Terrence J. Sejnowski,et al.  Filter Selection Model for Generating Visual Motion Signals , 1992, NIPS.

[32]  W. Pitts,et al.  How we know universals; the perception of auditory and visual forms. , 1947, The Bulletin of mathematical biophysics.

[33]  John H. R. Maunsell,et al.  The visual field representation in striate cortex of the macaque monkey: Asymmetries, anisotropies, and individual variability , 1984, Vision Research.

[34]  Subutai Ahmad,et al.  VISIT: A Neural Model of Covert Visual Attention , 1991, NIPS.

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

[36]  C. Cherniak.  The Bounded Brain: Toward Quantitative Neuroanatomy , 1990, Journal of Cognitive Neuroscience.

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

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

[39]  W. Freeman.  Steerable filters and local analysis of image structure , 1992 .

[40]  David C. Van Essen,et al.  Multiple processing streams in occipitotemporal visual cortex , 1994, Nature.

[41]  J. Koenderink,et al.  Visual detection of spatial contrast; Influence of location in the visual field, target extent and illuminance level , 1978, Biological Cybernetics.

[42]  N. Drasdo.  The neural representation of visual space , 1977, Nature.

[43]  E. DeYoe,et al.  Concurrent processing in the primate visual cortex. , 1995 .

[44]  Geoffrey E. Hinton,et al.  Shape Recognition and Illusory Conjunctions , 1985, IJCAI.

[45]  D. Pelli,et al.  The information capacity of visual attention , 1992, Vision Research.

[46]  M. Pettet,et al.  Dynamic changes in receptive-field size in cat primary visual cortex. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[47]  E. O. Postma,et al.  SCAN: a neural model of covert attention , 1994 .

[49]  David J. Field,et al.  What Is the Goal of Sensory Coding? , 1994, Neural Computation.

[50]  Masao Yukie,et al.  Laminar origin of direct projection from cortex area V1 to V4 in the rhesus monkey , 1985, Brain Research.

[51]  Robert S. Thau.  Visual Segmentation and Feature Binding without Synchronization , 1995 .

[52]  C. Malsburg,et al.  Statistical Coding and Short-Term Synaptic Plasticity: A Scheme for Knowledge Representation in the Brain , 1986 .

[53]  David J. Field,et al.  What The Statistics Of Natural Images Tell Us About Visual Coding , 1989, Photonics West - Lasers and Applications in Science and Engineering.