A mechanistic cortical microcircuit of attention for amplification, normalization and suppression

Abstract

Computational models of visual attention have replicated a large number of data from visual attention experiments. However, typically each computational model has been shown to account for only a few data sets. We developed a novel model of attention, particularly focused on explaining single cell recordings in multiple brain areas, to better understand the underlying computational circuits of attention involved in spatial- and feature-based biased competition, modulation of the contrast response function, modulation of the neuronal tuning curve, and modulation of surround suppression. In contrast to previous models, we use a two layer structure inspired by the layered cortical architecture which implements amplification, divisive normalization and suppression as well as spatial pooling.