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Abstract:
We propose a system of interconnected modules consisting in
populations of neurons for modeling the underlying mechanisms
involved in selective visual attention. We demonstrate that it is
plausible to build a neural system for visual search, which works
across the visual field in parallel but due to the different
intrinsic dynamics can show the two experimentally observed modes
of visual attention, namely: the serial focal and the parallel
spread over the space mode. In other words, neither explicit serial
focal search nor saliencies maps should be assumed. The focus of
attention is not included in the system but is a result of the
convergence of the dynamic behavior of the neural networks. The
dynamics of the system can be interpreted as an intrinsic dynamical
routing for binding features if top-down information is available.
The neural population dynamics are handled in the framework of the
mean-field approximation. Consequently, the whole process can be
expressed as a system of coupled differential equations.
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