Invariance and selectivity in the ventral visual pathway

Pattern recognition systems that are invariant to shape, pose, lighting and texture are never sufficiently selective; they suffer a high rate of "false alarms". How are biological vision systems both invariant and selective? Specifically, how are proper arrangements of sub-patterns distinguished from the chance arrangements that defeat selectivity in artificial systems? The answer may lie in the nonlinear dynamics that characterize complex and other invariant cell types: these cells are temporarily more receptive to some inputs than to others (functional connectivity). One consequence is that pairs of such cells with overlapping receptive fields will possess a related property that might be termed functional common input. Functional common input would induce high correlation exactly when there is a match in the sub-patterns appearing in the overlapping receptive fields. These correlations, possibly expressed as a partial and highly local synchrony, would preserve the selectivity otherwise lost to invariance.

[1]  Yali Amit,et al.  Shape Quantization and Recognition with Randomized Trees , 1997, Neural Computation.

[2]  Christoph von der Malsburg,et al.  The Correlation Theory of Brain Function , 1994 .

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

[4]  L. Shastri,et al.  From simple associations to systematic reasoning: A connectionist representation of rules, variables and dynamic bindings using temporal synchrony , 1993, Behavioral and Brain Sciences.

[5]  D. Georgescauld Local Cortical Circuits, An Electrophysiological Study , 1983 .

[6]  H. Markram,et al.  The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[7]  L. Shastri,et al.  From Simple Associations to Systemic Reasoning: A Connectionist Representation of Rules, Variables and Dynamic Bindings , 1990 .

[8]  Stuart Geman,et al.  An Exact Jitter Method using Dynamic Programming , 2004 .

[9]  Stephen Grossberg,et al.  A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..

[10]  David Mumford,et al.  On the computational architecture of the neocortex , 2004, Biological Cybernetics.

[11]  R. Reid,et al.  Precisely correlated firing in cells of the lateral geniculate nucleus , 1996, Nature.

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

[13]  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.

[14]  A. M. Turing,et al.  Computing Machinery and Intelligence , 1950, The Philosophy of Artificial Intelligence.

[15]  M. Tarr News On Views: Pandemonium Revisited , 1999, Nature Neuroscience.

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

[17]  G. Edelman,et al.  Spike-timing dynamics of neuronal groups. , 2004, Cerebral cortex.

[18]  D. Mumford,et al.  On the computational architecture of the neocortex , 2004, Biological Cybernetics.

[19]  Paul H. E. Tiesinga,et al.  Rapid Temporal Modulation of Synchrony by Competition in Cortical Interneuron Networks , 2004, Neural Computation.

[20]  D C Van Essen,et al.  Information processing in the primate visual system: an integrated systems perspective. , 1992, Science.

[21]  Wolf Singer,et al.  Neuronal Synchrony: A Versatile Code for the Definition of Relations? , 1999, Neuron.

[22]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.

[23]  H. Markram,et al.  Differential signaling via the same axon of neocortical pyramidal neurons. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[24]  Donald Geman Coarse-to-Fine Classification and Scene Labeling , 2003 .

[25]  A. M. Turing,et al.  Computing Machinery and Intelligence , 1950, The Philosophy of Artificial Intelligence.

[26]  F. Crick Function of the thalamic reticular complex: the searchlight hypothesis. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[27]  Halbert White,et al.  Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappings , 1990, Neural Networks.

[28]  D. Mumford,et al.  Neural activity in early visual cortex reflects behavioral experience and higher-order perceptual saliency , 2002, Nature Neuroscience.

[29]  J. Fodor,et al.  Connectionism and cognitive architecture: A critical analysis , 1988, Cognition.

[30]  S Ullman,et al.  Sequence seeking and counter streams: a computational model for bidirectional information flow in the visual cortex. , 1995, Cerebral cortex.

[31]  D. V. van Essen,et al.  Corticocortical connections of visual, sensorimotor, and multimodal processing areas in the parietal lobe of the macaque monkey , 2000, The Journal of comparative neurology.

[32]  Bartlett W. Mel,et al.  Computational subunits in thin dendrites of pyramidal cells , 2004, Nature Neuroscience.

[33]  V. Vapnik Estimation of Dependences Based on Empirical Data , 2006 .

[34]  C. J. Stone,et al.  Consistent Nonparametric Regression , 1977 .

[35]  T. Poggio,et al.  Neural mechanisms of object recognition , 2002, Current Opinion in Neurobiology.

[36]  Daniel M. Keenan General Pattern Theory: A Mathematical Study of Regular Structures (U. Grenander) , 1995, SIAM Rev..

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

[38]  Seymour A. Papert,et al.  The Summer Vision Project , 1966 .

[39]  Zicheng Liu,et al.  ARTiFACIAL: automated reverse turing test using FACIAL features , 2003, MULTIMEDIA '03.

[40]  Terrence J. Sejnowski,et al.  Unsupervised Learning , 2018, Encyclopedia of GIS.

[41]  Refractor Vision , 2000, The Lancet.

[42]  Asohan Amarasingham,et al.  At what time scale does the nervous system operate? , 2003, Neurocomputing.

[43]  E. Bienenstock A model of neocortex , 1995 .

[44]  Peter Földiák,et al.  Stimulus optimisation in primary visual cortex , 2001, Neurocomputing.

[45]  J Szentagothai,et al.  [Neuronal circuits of the cerebral cortex]. , 1970, Bulletin de l'Academie royale de medecine de Belgique.

[46]  Elie Bienenstock,et al.  Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.

[47]  Yali Amit,et al.  A Computational Model for Visual Selection , 1999, Neural Computation.

[48]  D. Perrett,et al.  Rapid serial visual presentation for the determination of neural selectivity in area STSa. , 2004, Progress in brain research.

[49]  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.

[50]  E. Callaway,et al.  Excitatory cortical neurons form fine-scale functional networks , 2005, Nature.

[51]  Ernst Niebur,et al.  The Effects of Input Rate and Synchrony on a Coincidence Detector: Analytical Solution , 2003, Neural Computation.

[52]  J. Gallant,et al.  Natural Stimulation of the Nonclassical Receptive Field Increases Information Transmission Efficiency in V1 , 2002, The Journal of Neuroscience.

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

[54]  M. Tovée,et al.  The responses of single neurons in the temporal visual cortical areas of the macaque when more than one stimulus is present in the receptive field , 2004, Experimental Brain Research.

[55]  C. Gray,et al.  Adaptive Coincidence Detection and Dynamic Gain Control in Visual Cortical Neurons In Vivo , 2003, Neuron.

[56]  Shigeru Tanaka,et al.  Spatial pooling in the second-order spatial structure of cortical complex cells , 2000, Vision Research.

[57]  O. Firschein,et al.  Syntactic pattern recognition and applications , 1983, Proceedings of the IEEE.

[58]  David L. Sheinberg,et al.  Noticing Familiar Objects in Real World Scenes: The Role of Temporal Cortical Neurons in Natural Vision , 2001, The Journal of Neuroscience.

[59]  Elie Bienenstock,et al.  Using Statistics of Natural Images to Facilitate Automatic Receptive Field Analysis , 2004 .

[60]  Y. Dan,et al.  Coding of visual information by precisely correlated spikes in the lateral geniculate nucleus , 1998, Nature Neuroscience.

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