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

  1. Grace W Lindsay  Is a corresponding author
  2. Kenneth D Miller
  1. 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)

The following data sets were generated

Article and author information

Author details

  1. Grace W Lindsay

    Center for Theoretical Neuroscience, Columbia University, New York, United States
    For correspondence
    gracewlindsay@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9904-7471
  2. Kenneth D Miller

    Center for Theoretical Neuroscience, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1433-0647

Funding

National Science Foundation (DBI-1707398)

  • Kenneth D Miller

National Institutes of Health (T32 NS064929)

  • Kenneth D Miller

Gatsby Charitable Foundation

  • Kenneth D Miller

Google

  • 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

  1. Marcel van Gerven, Radboud Universiteit, Netherlands

Version history

  1. Received: May 5, 2018
  2. Accepted: September 28, 2018
  3. Accepted Manuscript published: October 1, 2018 (version 1)
  4. 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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  1. Grace W Lindsay
  2. Kenneth D Miller
(2018)
How biological attention mechanisms improve task performance in a large-scale visual system model
eLife 7:e38105.
https://doi.org/10.7554/eLife.38105

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https://doi.org/10.7554/eLife.38105

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