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This paper proposes a saliency-based attention model based on pulsed cosine transform that simulates the lateral surround inhibition of neurons with similar visual features. The model can be extended to Hebbian-based neural networks. The visual saliency can be represented in binary codes, which agrees with the firing pulse of neurons in human brain. In addition, motion saliency can be directly generated...
This paper presents a computational model of neural network for both spatial and temporal weights, and a unified adaptation scheme based on two biologically plausible learning rules-Hebbian rule and lateral inhibition is proposed. This model is applied to color video environment to develop a set of complete spatiotemporal weights simulating receptive field of simple cell in primary visual cortex,...
There have been many computational models mimicking the visual cortex that are based on spatial adaptations of unsupervised neural networks. In this paper, we present a new model called neuronal cluster which includes spatial as well as temporal weights in its unified adaptation scheme. The ldquoin-placerdquo nature of the model is based on two biologically plausible learning rules, Hebbian rule and...
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