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Systems-Level Neuronal Modeling of Visual Attentional Mechanisms

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Abstract

We review different functions involved in visual perception that have been integrated by a model based on the biased competition hypothesis. Attentional top-down bias guides the dynamics to concentrate at a given spatial location or on given features. The model integrates, in a unifying form, the explanation of several existing types of experimental data obtained at different levels of investigation. At the microscopic level, single cell recordings are simulated. At the mesoscopic level of cortical areas, results of functional magnetic resonance imaging (fMRI) studies are reproduced. Finally, at the macroscopic level, psychophysical experiments like visual search tasks are also described by the model.

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Correspondence to S. Corchs.

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Corchs, S., Stetter, M. & Deco, G. Systems-Level Neuronal Modeling of Visual Attentional Mechanisms. Artificial Intelligence Review 20, 143–160 (2003). https://doi.org/10.1023/A:1026028429257

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