A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Viewpoint Invariant Perceptual Representations from Cluttered Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Modeling attention: from computational neuroscience to computer vision
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
Structural enhanced information to detect features in competitive learning
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Pseudo-network growing for gradual interpretation of input patterns
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
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Visual attention is generally considered to facilitate the processing of the attended stimulus. Its mechanisms, however, are still under debate. We have developed a systems-level model of visual attention which predicts that attentive effects emerge by the interactions between different brain areas. Recent physiological studies have provided evidence that attention also alters the receptive field structure. For example, V4 receptive fields typically shrink and shift towards the saccade target around saccade onset. We show that receptive field dynamics are inherently predicted by the mechanism of feedback in our model. According to the model an oculomotor feedback signal from an area involved in the competition for the saccade target location, e.g. the frontal eye field, enhances the gain of V4 cells. V4 receptive field dynamics can be observed after pooling the gain modulated responses to obtain a certain degree of spatial invariance. The time course of the receptive field dynamics in the model resemble those obtained from macaque V4.