A simple circuit model of visual cortex explains neural and behavioral aspects of attention
Lindsay, G. W.; Rubin, D. B.; Miller, K. D.
Show abstract
Selective visual attention modulates neural activity in the visual system in complex ways and leads to enhanced performance on difficult visual tasks. Here, we show that a simple circuit model, the stabilized supralinear network, gives a unified account of a wide variety of effects of attention on neural responses. We replicate results from studies of both feature and spatial attention, addressing findings in a variety of experimental paradigms on changes both in firing rates and in correlated neural variability. Finally, we expand this circuit model into an architecture that can perform visual tasks--a convolutional neural network--in order to show that these neural effects can enhance detection performance. This work provides the first unified mechanistic account of the effects of attention on neural and behavioral responses.
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