How biological attention mechanisms improve task performance in a large-scale visual system model

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...

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Bibliographic Details
Published in:eLife 2018-10, Vol.7
Main Authors: Lindsay, Grace W, Miller, Kenneth D
Format: Article
Language:English
Subjects:
Architecture
Artificial neural networks
Attention
Attention task
Brain research
Computer vision
convolutional neural networks
gain modulation
Human performance
Image processing equipment
Information processing
Models, Biological
Neural networks
Neurons
Neuroscience
Neurosciences
Orientation
Task Performance and Analysis
visual attention
Visual Pathways - physiology
Visual system
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Summary: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.
ISSN:2050-084X
2050-084X
DOI:10.7554/eLife.38105