A neural cocktail-party processor
Biological Cybernetics
Synchronization of pulse-coupled biological oscillators
SIAM Journal on Applied Mathematics
Object selection based on oscillatory correlation
Neural Networks
Journal of Cognitive Neuroscience
Locally excitatory globally inhibitory oscillator networks
IEEE Transactions on Neural Networks
A control model of the movement of attention
Neural Networks
An oscillatory neural model of multiple object tracking
Neural Computation
Selective Attention Model of Moving Objects
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Selective attention model with spiking elements
Neural Networks
An oscillatory correlation model of object-based attention
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Partial synchronization of neural activity and information processing
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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We present a neurodynamical model to study and simulate visual search tasks experiments. The model consists of different pools of interconnected phase oscillators. Each oscillator is described by an integrate-and-fire type equation. Visual attention appears as an emergent property of the dynamic of the system, resulting from the temporal synchronization of the pools which bind the features of the searched target. The time courses observed in the psychophysical visual search experiments can be explained within a purely parallel dynamic and without assuming priority maps and serial spotlight mechanisms, as is usually done in the standard theories. The model fits also the measured activity reported for the neural responses in inferotemporal visual cortex of monkeys performing visual search tasks.