Hierarchical models of object recognition in cortex

Visual processing in cortex is classically modeled as a hierarchy of increasingly sophisticated representations, naturally extending the model of simple to complex cells of Hubel and Wiesel. Surprisingly, little quantitative modeling has been done to explore the biological feasibility of this class of models to explain aspects of higher-level visual processing such as object recognition. We describe a new hierarchical model consistent with physiological data from inferotemporal cortex that accounts for this complex visual task and makes testable predictions. The model is based on a MAX-like operation applied to inputs to certain cortical neurons that may have a general role in cortical function.

[1]  D. H. Hubel,et al.  RECEPTIVE FIELDS, BINOCULAR AND FUNCTIONAL ARCHITECTURE IN THE CAT’S VISUAL CORTEX , 1962 .

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

[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]  R. Desimone,et al.  Visual properties of neurons in a polysensory area in superior temporal sulcus of the macaque. , 1981, Journal of neurophysiology.

[5]  Werner Reichardt,et al.  Figure-ground discrimination by relative movement , 1983 .

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

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

[8]  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.

[9]  Stephen Grossberg,et al.  Nonlinear neural networks: Principles, mechanisms, and architectures , 1988, Neural Networks.

[10]  T. Poggio,et al.  A network that learns to recognize three-dimensional objects , 1990, Nature.

[11]  Michael I. Jordan,et al.  Advances in Neural Information Processing Systems 30 , 1995 .

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

[13]  D. Heeger Normalization of cell responses in cat striate cortex , 1992, Visual Neuroscience.

[14]  H H Bülthoff,et al.  Psychophysical support for a two-dimensional view interpolation theory of object recognition. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

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

[16]  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.

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

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

[19]  N. Logothetis,et al.  View-dependent object recognition by monkeys , 1994, Current Biology.

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

[21]  T. Sejnowski,et al.  A selection model for motion processing in area MT of primates , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.

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

[23]  M. Tarr Rotating objects to recognize them: A case study on the role of viewpoint dependency in the recognition of three-dimensional objects , 1995, Psychonomic bulletin & review.

[24]  C. Koch,et al.  Recurrent excitation in neocortical circuits , 1995, Science.

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

[26]  Peter Dayan,et al.  Neural Models for Part-Whole Hierarchies , 1996, NIPS.

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

[28]  L. Abbott,et al.  Synaptic Depression and Cortical Gain Control , 1997, Science.

[29]  L. Abbott,et al.  Invariant visual responses from attentional gain fields. , 1997, Journal of neurophysiology.

[30]  E. Rolls,et al.  INVARIANT FACE AND OBJECT RECOGNITION IN THE VISUAL SYSTEM , 1997, Progress in Neurobiology.

[31]  D. V. van Essen,et al.  Spatial Attention Effects in Macaque Area V4 , 1997, The Journal of Neuroscience.

[32]  Tomaso A. Poggio,et al.  Just One View: Invariances in Inferotemporal Cell Tuning , 1997, NIPS.

[33]  G. Orban,et al.  Responses of macaque inferior temporal neurons to overlapping shapes. , 1997, Cerebral cortex.

[34]  Tomaso A. Poggio,et al.  Example-Based Learning for View-Based Human Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  Keiji Tanaka,et al.  Effects of shape-discrimination training on the selectivity of inferotemporal cells in adult monkeys. , 1998, Journal of neurophysiology.

[36]  Takeo Kanade,et al.  Neural Network-Based Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[37]  E. Rolls,et al.  View-invariant representations of familiar objects by neurons in the inferior temporal visual cortex. , 1998, Cerebral cortex.

[38]  N. Logothetis Object vision and visual awareness. , 1998, Current opinion in neurobiology.

[39]  Keiji Tanaka,et al.  Functional architecture in monkey inferotemporal cortex revealed by in vivo optical imaging , 1998, Neuroscience Research.

[40]  T. Poggio,et al.  Modeling Invariances in Inferotemporal Cell Tuning , 1998 .

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

[42]  T. Poggio,et al.  Are Cortical Models Really Bound by the “Binding Problem”? , 1999, Neuron.

[43]  R. Vogels Categorization of complex visual images by rhesus monkeys. Part 2: single‐cell study , 1999, The European journal of neuroscience.

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

[45]  C. Koch,et al.  Attention activates winner-take-all competition among visual filters , 1999, Nature Neuroscience.

[46]  Frances S. Chance,et al.  Complex cells as cortically amplified simple cells , 1999, Nature Neuroscience.

[47]  Edmund T. Rolls,et al.  A Model of Invariant Object Recognition in the Visual System: Learning Rules, Activation Functions, Lateral Inhibition, and Information-Based Performance Measures , 2000, Neural Computation.