Analysis of Neural Circuit for Visual Attention Using Lognormally Distributed Input
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- Nagano Y., Watanabe N., Aoyama A. (2014) Analysis of Neural Circuit for Visual Attention Using Lognormally Distributed Input. In: Wermter S. et al. (eds) Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham
Visual attention has recently been reported to modulate neural activity of narrow spiking and broad spiking neurons in V4, with increased firing rate and less inter-trial variations. We simulated these physiological phenomena using a neural network model based on spontaneous activity, assuming that the visual attention modulation could be achieved by a change in variance of input firing rate distributed with a lognormal distribution. Consistent with the physiological studies, an increase in firing rate and a decrease in inter-trial variance was simultaneously obtained in the simulation by increasing variance of input firing rate distribution. These results indicate that visual attention forms strong sparse and weak dense input or a ‘winner-take-all’ state, to improve the signal-to-noise ratio of the target information.
KeywordsVisual Attention Neural Network Model Spontaneous Activity Lognormal Distribution
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