How biological attention mechanisms improve task performance in a large-scale visual system model
- Columbia University, United States
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.
Data availability
The weights for the model used are linked to in the study. The data resulting from simulations have been packaged and are available on Dryad (doi:10.5061/dryad.jc14081). The analysis code are available on GitHub (https://github.com/gwl2108/CNN_attention)
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Data from: How biological attention mechanisms improve task performance in a large-scale visual system modelAvailable at Dryad Digital Repository under a CC0 Public Domain Dedication.
Article and author information
Author details
Funding
National Science Foundation (DBI-1707398)
- Kenneth D Miller
National Institutes of Health (T32 NS064929)
- Kenneth D Miller
Gatsby Charitable Foundation
- Kenneth D Miller
- Grace W Lindsay
National Science Foundation (IIS-1704938)
- Kenneth D Miller
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Marcel van Gerven, Radboud Universiteit, Netherlands
Version history
- Received: May 5, 2018
- Accepted: September 28, 2018
- Accepted Manuscript published: October 1, 2018 (version 1)
- Version of Record published: October 30, 2018 (version 2)
Copyright
© 2018, Lindsay & Miller
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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How biological attention mechanisms improve task performance in a large-scale visual system model
eLife 7:e38105.
https://doi.org/10.7554/eLife.38105
