Image Representation Using 2D Gabor Wavelets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Neural Field Dynamics in the Context of Attentional Control
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Towards Novel Neuroscience-Inspired Computing
Emergent Neural Computational Architectures Based on Neuroscience - Towards Neuroscience-Inspired Computing
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Visual attention poses a mechanism for the selection of behaviourally relevant information from natural scenes which usually contain multiple objects. The aim of the present work is to formulate a neurodynamical model of selective visual attention based on the "biased competition hypothesis" and structured in several network modules which can be related with the different areas of the dorsal and ventral path of the visual cortex. Spatial and object attention are accomplished by a multiplicative gain control that emerges dynamically through intercortical mutual biased coupling. We also include in our computational model the "resolution hypothesis" in order to explain the role of the neurodynamics control of spatial resolution evidenced in psychophysical experiments. We propose that V1 neurons have different latencies depending on the spatial frequency to which they respond more sensitively. In concrete, we pose that V1 neurons sensitive to low spatial frequency are faster than V1 neurons sensitive to high spatial frequency. In this sense, a scene is first predominantly analysed at a coarse resolution level and the dynamics enhances subsequently the resolution at the location of an object until the object is identified.