Elements of a stochastic 3D prediction engine in larval zebrafish prey capture

  1. Andrew D Bolton  Is a corresponding author
  2. Martin Haesemeyer
  3. Josua Jordi
  4. Ulrich Schaechtle
  5. Feras A Saad
  6. Vikash K Mansinghka
  7. Joshua B Tenenbaum
  8. Florian Engert
  1. Harvard University, United States
  2. Massachusetts Institute of Technology, United States

Abstract

The computational principles underlying predictive capabilities in animals are poorly understood. Here, we wondered whether predictive models mediating prey capture could be reduced to a simple set of sensorimotor rules performed by a primitive organism. For this task, we chose the larval zebrafish, a tractable vertebrate that pursues and captures swimming microbes. Using a novel naturalistic 3D setup, we show that the zebrafish combines position and velocity perception to construct a future positional estimate of its prey, indicating an ability to project trajectories forward in time. Importantly, the stochasticity in the fish's sensorimotor transformations provides a considerable advantage over equivalent noise-free strategies. This surprising result coalesces with recent findings that illustrate the benefits of biological stochasticity to adaptive behavior. In sum, our study reveals that zebrafish are equipped with a recursive prey capture algorithm, built up from simple stochastic rules, that embodies an implicit predictive model of the world.

Data availability

All software related to behavioral analysis, modeling, and virtual prey capture simulation is freely available at www.github.com/larrylegend33/PreycapMaster. The software is licensed under a GNU General Public License 3.0. Source data for analysis and simulations is enclosed as "Source Data" in relevant figures. Source Data for Figure 2 contains all pursuit bouts analyzed in the dataset; it was used to construct Figures 2, 3, 5, and 6A, and is accompanied by instructions for running queries. Source Data for Figure 6 contains the generators for simulating from the DPMMs in Figure 6. Using the code at www.github.com/larrylegend33/PreycapMaster and the generators in Source Data - Figure 6 requires obtaining the BayesDB software package, which is freely available at http://probcomp.csail.mit.edu/software/bayesdb/.

Article and author information

Author details

  1. Andrew D Bolton

    Center for Brain Science, Harvard University, Cambridge, United States
    For correspondence
    andrewdbolton@fas.harvard.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3059-7226
  2. Martin Haesemeyer

    Center for Brain Science, Harvard University, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2704-3601
  3. Josua Jordi

    Center for Brain Science, Harvard University, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Ulrich Schaechtle

    Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Feras A Saad

    Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Vikash K Mansinghka

    Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Joshua B Tenenbaum

    Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Florian Engert

    Center for Brain Science, Harvard University, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.

Funding

National Institutes of Health (U19NS104653)

  • Florian Engert

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

Ethics

Animal experimentation: Experiments were conducted according to the guidelines of the National Institutes of Health and were approved by the Standing Committee on the Use of Animals in Research of Harvard University. Animals were handled according IACUC protocol #2729.

Reviewing Editor

  1. Gordon J Berman, Emory University, United States

Version history

  1. Received: September 18, 2019
  2. Accepted: November 25, 2019
  3. Accepted Manuscript published: November 26, 2019 (version 1)
  4. Version of Record published: December 24, 2019 (version 2)

Copyright

© 2019, Bolton 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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  1. Andrew D Bolton
  2. Martin Haesemeyer
  3. Josua Jordi
  4. Ulrich Schaechtle
  5. Feras A Saad
  6. Vikash K Mansinghka
  7. Joshua B Tenenbaum
  8. Florian Engert
(2019)
Elements of a stochastic 3D prediction engine in larval zebrafish prey capture
eLife 8:e51975.
https://doi.org/10.7554/eLife.51975

Share this article

https://doi.org/10.7554/eLife.51975

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