Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

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

View ORCID ProfileGrace W. Lindsay, Kenneth D. Miller
doi: https://doi.org/10.1101/233338
Grace W. Lindsay
aCenter for Theoretical Neuroscience, College of Physicians and Surgeons, Columbia University, New York, New York, USA
bMortimer B. Zuckerman Mind Brain Behavior Institute, College of Physicians and Surgeons, Columbia University, New York, New York, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Grace W. Lindsay
Kenneth D. Miller
aCenter for Theoretical Neuroscience, College of Physicians and Surgeons, Columbia University, New York, New York, USA
bMortimer B. Zuckerman Mind Brain Behavior Institute, College of Physicians and Surgeons, Columbia University, New York, New York, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

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.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted August 17, 2018.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
How biological attention mechanisms improve task performance in a large-scale visual system model
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
How biological attention mechanisms improve task performance in a large-scale visual system model
Grace W. Lindsay, Kenneth D. Miller
bioRxiv 233338; doi: https://doi.org/10.1101/233338
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
How biological attention mechanisms improve task performance in a large-scale visual system model
Grace W. Lindsay, Kenneth D. Miller
bioRxiv 233338; doi: https://doi.org/10.1101/233338

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (3479)
  • Biochemistry (7318)
  • Bioengineering (5296)
  • Bioinformatics (20197)
  • Biophysics (9976)
  • Cancer Biology (7703)
  • Cell Biology (11250)
  • Clinical Trials (138)
  • Developmental Biology (6418)
  • Ecology (9916)
  • Epidemiology (2065)
  • Evolutionary Biology (13280)
  • Genetics (9352)
  • Genomics (12554)
  • Immunology (7674)
  • Microbiology (18939)
  • Molecular Biology (7417)
  • Neuroscience (40893)
  • Paleontology (298)
  • Pathology (1226)
  • Pharmacology and Toxicology (2126)
  • Physiology (3140)
  • Plant Biology (6838)
  • Scientific Communication and Education (1270)
  • Synthetic Biology (1891)
  • Systems Biology (5296)
  • Zoology (1085)