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Shallow neural networks trained to detect collisions recover features of visual loom-selective neurons

  1. Baohua Zhou
  2. Zifan Li
  3. Sunnie Kim
  4. John Lafferty  Is a corresponding author
  5. Damon A Clark  Is a corresponding author
  1. Yale University, United States
Research Article
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Cite this article as: eLife 2022;11:e72067 doi: 10.7554/eLife.72067

Abstract

Animals have evolved sophisticated visual circuits to solve a vital inference problem: detecting whether or not a visual signal corresponds to an object on a collision course. Such events are detected by specific circuits sensitive to visual looming, or objects increasing in size. Various computational models have been developed for these circuits, but how the collision-detection inference problem itself shapes the computational structures of these circuits remains unknown. Here, inspired by the distinctive structures of LPLC2 neurons in the visual system of Drosophila, we build anatomically-constrained shallow neural network models and train them to identify visual signals that correspond to impending collisions. Surprisingly, the optimization arrives at two distinct, opposing solutions, only one of which matches the actual dendritic weighting of LPLC2 neurons. Both solutions can solve the inference problem with high accuracy when the population size is large enough. The LPLC2-like solutions reproduces experimentally observed LPLC2 neuron responses for many stimuli, and reproduces canonical tuning of loom sensitive neurons, even though the models are never trained on neural data. Thus, LPLC2 neuron properties and tuning are predicted by optimizing an anatomically-constrained neural network to detect impending collisions. More generally, these results illustrate how optimizing inference tasks that are important for an animal's perceptual goals can reveal and explain computational properties of specific sensory neurons.

Data availability

Code to perform all simulations in this paper and to reproduce all figures is available at http://www.github.com/ClarkLabCode/LoomDetectionANN.

Article and author information

Author details

  1. Baohua Zhou

    Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Zifan Li

    Department of Statistics and Data Science, Yale University, New Haven, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Sunnie Kim

    Department of Statistics and Data Science, Yale University, New Haven, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8901-7233
  4. John Lafferty

    Department of Statistics and Data Science, Yale University, New Haven, United States
    For correspondence
    john.lafferty@yale.edu
    Competing interests
    The authors declare that no competing interests exist.
  5. Damon A Clark

    Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, United States
    For correspondence
    damon.clark@yale.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8487-700X

Funding

National Institutes of Health (R01EY026555)

  • Baohua Zhou
  • Damon A Clark

National Science Foundation (CCF-1839308)

  • Baohua Zhou
  • John Lafferty
  • Damon A Clark

National Science Foundation (DMS-1513594)

  • John Lafferty

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Fred Rieke, University of Washington, United States

Publication history

  1. Received: July 8, 2021
  2. Accepted: January 11, 2022
  3. Accepted Manuscript published: January 13, 2022 (version 1)

Copyright

© 2022, Zhou et al.

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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