Simultaneous Parallel Processing of Object and Position by Temporal Correlation
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Systems-Level Neuronal Modeling of Visual Attentional Mechanisms
Artificial Intelligence Review
Perceptual grouping and the interactions between visual cortical areas
Neural Networks - 2004 Special issue Vision and brain
A model of active visual search with object-based attention guiding scan paths
Neural Networks - 2004 Special issue Vision and brain
Stimulus Competition by Inhibitory Interference
Neural Computation
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
A Feedback Model of Visual Attention
Journal of Cognitive Neuroscience
A Cortical Mechanism for Binding in Visual Working Memory
Journal of Cognitive Neuroscience
Journal of Cognitive Neuroscience
Selective Attention Model of Moving Objects
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
2008 Special Issue: The state of MIIND
Neural Networks
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Selective Attention Improves Learning
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
VIEW'06 Proceedings of the 1st first visual information expert conference on Pixelization paradigm
EURASIP Journal on Advances in Signal Processing - Special issue on biologically inspired signal processing: analyses, algorithms and applications
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
Modeling attention: from computational neuroscience to computer vision
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
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We propose a neural model for object-oriented attention in which various visual stimuli (shapes, colors, letters, etc.) are represented by competing, mutually inhibitory, cell assemblies. The model's response to a sequence of cue and target stimuli mimics the neural responses in infero temporal (IT) visual cortex of monkeys performing a visual search task: enhanced response during the display of the stimulus, which decays but remains above a spontaneous rate after the cue disappears. When, subsequently, a display consisting of the target and several distractors is presented, the activity of all stimulus-driven cells is initially enhanced. After a short period of time, however, the activity of the cell assembly representing the cue stimulus is enhanced while the activity of the distractors decays because of mutual competition and a small top-down “expectational” input. The model fits the measured delayed activity in IT-cortex, recently reported by Chelazzi, Miller, Duncan, and Desimone (1993a), and we suggest that such a process, which is largely independent of the number of distractors, may be used by the visual system for selecting an expected target (appearing at an uncertain location) among distractors.