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Elife. 2018 Oct 1;7. pii: e38105. doi: 10.7554/eLife.38105.

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

Lindsay GW1,2, Miller KD1,2,3,4.

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Center for Theoretical Neuroscience, College of Physicians and Surgeons, Columbia University, New York, United States.
Mortimer B. Zuckerman Mind Brain Behaviour Institute, Columbia University, New York, United States.
Swartz Program in Theoretical Neuroscience, Kavli Institute for Brain Science, New York, United States.
Department of Neuroscience, Columbia University, New York, United States.


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.


convolutional neural networks; gain modulation; neuroscience; none; visual attention

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