Matches in SemOpenAlex for { <https://semopenalex.org/work/W2893184150> ?p ?o ?g. }
- W2893184150 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." @default.
- W2893184150 created "2018-10-05" @default.
- W2893184150 creator A5006191787 @default.
- W2893184150 creator A5082132955 @default.
- W2893184150 date "2018-10-01" @default.
- W2893184150 modified "2023-10-18" @default.
- W2893184150 title "How biological attention mechanisms improve task performance in a large-scale visual system model" @default.
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- W2893184150 doi "https://doi.org/10.7554/elife.38105" @default.