Elements of a stochastic 3D prediction engine in larval zebrafish prey capture. Bolton, A. D., Haesemeyer, M., Jordi, J., Schaechtle, U., Saad, F. A., Mansinghka, V. K., Tenenbaum, J. B., & Engert, F. eLife, 8:e51975, eLife Sciences Publications, Ltd, November, 2019.
Elements of a stochastic 3D prediction engine in larval zebrafish prey capture [link]Link  abstract   bibtex   7 downloads  
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.
@article {bolton2019zebra,
title                 = {Elements of a stochastic {3D} prediction engine in larval zebrafish prey capture},
author                = {Bolton, Andrew D. and Haesemeyer, Martin and Jordi, Joshua and Schaechtle, Ulrich and Saad, Feras A. and Mansinghka, Vikash K. and Tenenbaum, Joshua B. and Engert, Florian},
editor                = {Berman, Gordon J},
volume                = 8,
year                  = 2019,
month                 = nov,
pages                 = {e51975},
journal               = {eLife},
fdoi                  = {10.7554/eLife.51975},
url_link              = {https://doi.org/10.7554/eLife.51975},
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.},
publisher             = {eLife Sciences Publications, Ltd},
}

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