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Abstract:
Attentional mechanisms are required in order to process
information from a given scene because of the limited processing
capacity of the visual system. According to the biased competition
hypothesis, the multiple stimuli in the visual field activate
populations of neurons that engage in competitive mechanisms.
Single-cell recording studies in monkeys from extrastriate areas
seem to support this theory (Reynolds et al. 1999). Another
evidence comes from functional magnetic resonance imaging in humans
(Kastner et al. 1999). They found that when multiple stimuli are
present simultaneously in the visual field, their cortical
representations interact in a competitive, suppressive fashion but
not when the stimuli are presented sequentially. Moreover,
directing attention to one of the stimuli counteracts the
suppressive influence of nearby stimuli. The aim of the present
work is to provide a mathematical formulation that unifies
microscopic, mesoscopic and macroscopic mechanisms i! nvolved in
the brain functions allowing the description of the existing
experimental data at all neuroscience levels (psychophysics,
functional brain imaging and single cells measurements). The model
is structured in several network modules which can be related with
the different areas of the dorsal and ventral path of the visual
cortex and the biased competition hypothesis is implemented. The
dynamics of the neural population evolves according to the
mean-field approximation. The above cited experimental data are
reproduced within the present theoretical framework.
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