Abstract
Selective visual attention modulates neural activity in the visual system and leads to enhanced performance on difficult visual tasks. Here, we use an existing circuit model of visual cortex, known as the stabilized supralinear network, to demonstrate that many neural correlates of attention can arise from simple circuit mechanisms. Using different variants of the model we replicate results from studies of both feature and spatial attention. In addition to firing rate changes, we also replicate findings regarding how attention impacts trial-to-trial variability. Finally, we expand this circuit model into an architecture that can perform visual tasks in order to show that these neural effects can enhance detection performance. This work advances our understanding of the physical underpinnings of attention.