Abstract
How does attentional modulation of neural activity enhance performance? Here we use a deep convolutional neural network as a large-scale model of the visual system to address this question. We model the feature similarity gain model of attention, in which attentional modulation is applied according to neural stimulus tuning. Using a variety of visual tasks, we show that neural modulations of the kind and magnitude observed experimentally lead to performance changes of the kind and magnitude observed experimentally. We find that, at earlier layers, attention applied according to tuning does not successfully propagate through the network, and has a weaker impact on performance than attention applied according to values computed for optimally modulating higher areas. This raises the question of whether biological attention might be applied at least in part to optimize function rather than strictly according to tuning. We suggest a simple experiment to distinguish these alternatives.
Footnotes
The title has changed from "Understanding biological visual attention using convolutional neural networks" to "How biological attention mechanisms improve task performance in a large-scale visual system model" This version of the article is more succinct, with several findings removed or moved to supplementary materials in order to align with journal submission styles. A new analyses correlating activity changes with performance changes was also added.
