eLife (Nov 2019)

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

  • Andrew D Bolton,
  • Martin Haesemeyer,
  • Josua Jordi,
  • Ulrich Schaechtle,
  • Feras A Saad,
  • Vikash K Mansinghka,
  • Joshua B Tenenbaum,
  • Florian Engert

DOI
https://doi.org/10.7554/eLife.51975
Journal volume & issue
Vol. 8

Abstract

Read online

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.

Keywords