PT  - JOURNAL ARTICLE
AU  - Grace W. Lindsay
AU  - Daniel B. Rubin
AU  - Kenneth D. Miller
TI  - A unified circuit model of attention: Neural and behavioral effects
AID  - 10.1101/2019.12.13.875534
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2019.12.13.875534
4099  - http://biorxiv.org/content/early/2020/07/24/2019.12.13.875534.short
4100  - http://biorxiv.org/content/early/2020/07/24/2019.12.13.875534.full
AB  - 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.Competing Interest StatementThe authors have declared no competing interest.