1. Neuroscience
Download icon

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
Research Article
  • Cited 2
  • Views 2,026
  • Annotations
Cite this article as: eLife 2018;7:e38105 doi: 10.7554/eLife.38105

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.

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

Publication 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.

Metrics

  • 2,026
    Page views
  • 297
    Downloads
  • 2
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)