Image segmentation based on oscillatory correlation
Neural Computation
Dynamics of neural networks with a central element
Neural Networks
Object selection based on oscillatory correlation
Neural Networks
Stimulus Competition by Inhibitory Interference
Neural Computation
Distributed cortical network for visual attention
Journal of Cognitive Neuroscience
Locally excitatory globally inhibitory oscillator networks
IEEE Transactions on Neural Networks
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A new biologically plausible model of visual selective attention is developed based on synaptically coupled Hodgkin-Huxley neurons. The model is designed according to a two-layer architecture of excitatory and inhibitory connections which comprises two central neurons and a population of peripheral neurons. Two types of inhibition from the central neurons are present: fixed inhibition which is responsible for the formation of the attention focus, and short-term plastic inhibition which is responsible for the shift of attention. The regimes of synchronous dynamics associated with the development of the attentional focus are studied. In particular, the regime of partial synchronization between spiking activity of the central and peripheral neurons is interpreted as object selection to the focus of attention. It is shown that peripheral neurons with higher firing rates are selected preferentially by the attention system. The model correctly reproduces some observations concerning the mechanisms of attentional control, such as the coherence of spikes in the population of neurons included in the focus of attention, and the inhibition of neurons outside the focus of attention. Sequential selection of stimuli simultaneously present in the visual scene is demonstrated by the model in the frequency domain in both a formal example and a real image.