Directed visual attention and the dynamic control of information flow

Visual attention serves as an information bottleneck, allowing for ecient analysis of a region of interest that shifts in location and spatial scale from moment to moment. We discuss a modeling framework for visual attention in which information flow through the visual hierarchy is regulated by dynamic control of connection strengths. A key aspect of this model involves the establishment of object-centered reference frames for visual working memory as well as object recognition. Psychophysical evidence suggests that the region of interest is about 30 resolution elements across. This model is neurobiologically plausible, supported by several lines of anatomical and physiological data, and suitable for embedding into a larger computational framework for modeling of neural representations and transformations.

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

[2]  David C. Van Essen,et al.  Information Processing Strategies and Pathways in the Primate Visual System . , 1995 .

[3]  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.

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

[5]  Bruno A. Olshausen,et al.  A multiscale dynamic routing circuit for forming size- and position-invariant object representations , 1995, Journal of Computational Neuroscience.

[6]  David W. Arathorn,et al.  Map-Seeking Circuits in Visual Cognition: A Computational Mechanism for Biological and Machine Vision , 2002 .

[7]  J. White.  Neural engineering , 2007, Annals of Biomedical Engineering.

[8]  Bruno A. Olshausen,et al.  Dynamic routing strategies in sensory, motor, and cognitive processing , 1994 .

[9]  O. G. Selfridge,et al.  Pandemonium: a paradigm for learning , 1988 .

[10]  Bartlett W. Mel,et al.  Information Processing in Dendritic Trees , 1994, Neural Computation.

[11]  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.

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

[13]  Thomas K. Landauer,et al.  How Much do People Remember? Some Estimates of the Quantity of Learned Information in Long-Term Memory , 1986, Cogn. Sci..

[14]  S. Yantis,et al.  Abrupt visual onsets and selective attention: evidence from visual search. , 1984, Journal of experimental psychology. Human perception and performance.

[15]  Y. Amit,et al.  An integrated network for invariant visual detection and recognition , 2003, Vision Research.

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

[17]  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.

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

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