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

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,...

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Detalles Bibliográficos
Autores principales: Bolton, Andrew D, Haesemeyer, Martin, Jordi, Josua, Schaechtle, Ulrich, Saad, Feras A, Mansinghka, Vikash K, Tenenbaum, Joshua B, Engert, Florian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6930116/
https://www.ncbi.nlm.nih.gov/pubmed/31769753
http://dx.doi.org/10.7554/eLife.51975
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author Bolton, Andrew D
Haesemeyer, Martin
Jordi, Josua
Schaechtle, Ulrich
Saad, Feras A
Mansinghka, Vikash K
Tenenbaum, Joshua B
Engert, Florian
author_facet Bolton, Andrew D
Haesemeyer, Martin
Jordi, Josua
Schaechtle, Ulrich
Saad, Feras A
Mansinghka, Vikash K
Tenenbaum, Joshua B
Engert, Florian
author_sort Bolton, Andrew D
collection PubMed
description 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.
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spelling pubmed-69301162019-12-26 Elements of a stochastic 3D prediction engine in larval zebrafish prey capture Bolton, Andrew D Haesemeyer, Martin Jordi, Josua Schaechtle, Ulrich Saad, Feras A Mansinghka, Vikash K Tenenbaum, Joshua B Engert, Florian eLife Neuroscience 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. eLife Sciences Publications, Ltd 2019-11-26 /pmc/articles/PMC6930116/ /pubmed/31769753 http://dx.doi.org/10.7554/eLife.51975 Text en © 2019, Bolton et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Neuroscience
Bolton, Andrew D
Haesemeyer, Martin
Jordi, Josua
Schaechtle, Ulrich
Saad, Feras A
Mansinghka, Vikash K
Tenenbaum, Joshua B
Engert, Florian
Elements of a stochastic 3D prediction engine in larval zebrafish prey capture
title Elements of a stochastic 3D prediction engine in larval zebrafish prey capture
title_full Elements of a stochastic 3D prediction engine in larval zebrafish prey capture
title_fullStr Elements of a stochastic 3D prediction engine in larval zebrafish prey capture
title_full_unstemmed Elements of a stochastic 3D prediction engine in larval zebrafish prey capture
title_short Elements of a stochastic 3D prediction engine in larval zebrafish prey capture
title_sort elements of a stochastic 3d prediction engine in larval zebrafish prey capture
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6930116/
https://www.ncbi.nlm.nih.gov/pubmed/31769753
http://dx.doi.org/10.7554/eLife.51975
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