1994 Special Issue: Modeling visual recognition from neurobiological constraints
Neural Networks - Special issue: models of neurodynamics and behavior
Hebbian learning of context in recurrent neural networks
Neural Computation
Representation of visual features of objects in the inferotemporal cortex
Neural Networks - 1996 Special issue: four major hypotheses in neuroscience
The objects of action and perception
Object recognition in man, monkey, and machine
Journal of Cognitive Neuroscience
Spatial transformations in the parietal cortex using basis functions
Journal of Cognitive Neuroscience
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Occlusion, attention and object representations
Integrated Computer-Aided Engineering - Artificial Neural Networks
2008 Special Issue: The state of MIIND
Neural Networks
Anticipative Control of Voluntary Action: Towards a Computational Model
Anticipatory Behavior in Adaptive Learning Systems
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
A neural model of binding and capacity in visual working memory
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Advances in Artificial Intelligence - Special issue on artificial intelligence in neuroscience and systems biology: lessons learnt, open problems, and the road ahead
Coding of objects in low-level visual cortical areas
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Occlusion, attention and object representations
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
A population-based inference framework for feature-based attention in natural scenes
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
Learning location invariance for object recognition and localization
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
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We propose a neural model of visual object-based attention in which the identity of an object is used to select its location in an array of objects. The model is based on neural activity observed in visual search tasks performed by monkeys. In the model, the identity of the object (target) is selected in the higher areas of the ventral stream by means of a cue. Feedback activation from these higher areas carries information about the identity of the target to the (lower) retinotopic areas of the ventral stream. In these areas, the feedback activation interacts with feedforward activation produced by the object array. The interaction occurs in local microcircuits, and results in a selective activation on locations in the retinotopic areas of the visual stream that correspond to the location of the target in the object array. The selective activation consists of a form of gain control, produced by disinhibition. Transmitted to the dorsal stream, this activation directs spatial attention to the location of the target. In this way, an action directed at the target can be generated.