This is not the latest version of this item. The latest version can be found here.
Elements of a stochastic 3D prediction engine in larval zebrafish prey capture
Name
elife-51975-v2.pdf
Description
Published version
Size
1.79 MB
Format
Adobe PDF
Checksum (MD5)
63aa00ab1a7dd03b92d678557b047fde
Author(s)
Bolton, Andrew D
Haesemeyer, Martin
Jordi, Josua
Schaechtle, Ulrich
Saad, Feras A
Mansinghka, Vikash K
Tenenbaum, Joshua B
Engert, Florian
Date Issued
2019
Journal
eLife
Publisher
eLife Sciences Publications, Ltd
Citation
Bolton, Andrew D, Haesemeyer, Martin, Jordi, Josua, Schaechtle, Ulrich, Saad, Feras A et al. 2019. "Elements of a stochastic 3D prediction engine in larval zebrafish prey capture." eLife, 8.
Version
Final published version
Abstract
© 2019, eLife Sciences Publications Ltd. All rights reserved. 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.
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
10.7554/ELIFE.51975
