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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,...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2019
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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. |
format | Online Article Text |
id | pubmed-6930116 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
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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