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A study of transfer of information in animal collectives using deep learning tools

We studied how the interactions among animals in a collective allow for the transfer of information. We performed laboratory experiments to study how zebrafish in a collective follow a subset of trained animals that move towards a light when it turns on because they expect food at that location. We...

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Detalles Bibliográficos
Autores principales: Romero-Ferrero, Francisco, Heras, Francisco J. H., Rance, Dean, de Polavieja, Gonzalo G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9939271/
https://www.ncbi.nlm.nih.gov/pubmed/36802786
http://dx.doi.org/10.1098/rstb.2022.0073
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author Romero-Ferrero, Francisco
Heras, Francisco J. H.
Rance, Dean
de Polavieja, Gonzalo G.
author_facet Romero-Ferrero, Francisco
Heras, Francisco J. H.
Rance, Dean
de Polavieja, Gonzalo G.
author_sort Romero-Ferrero, Francisco
collection PubMed
description We studied how the interactions among animals in a collective allow for the transfer of information. We performed laboratory experiments to study how zebrafish in a collective follow a subset of trained animals that move towards a light when it turns on because they expect food at that location. We built some deep learning tools to distinguish from video which are the trained and the naïve animals and to detect when each animal reacts to the light turning on. These tools gave us the data to build a model of interactions that we designed to have a balance between transparency and accuracy. The model finds a low-dimensional function that describes how a naïve animal weights neighbours depending on focal and neighbour variables. According to this low-dimensional function, neighbour speed plays an important role in the interactions. Specifically, a naïve animal weights more a neighbour in front than to the sides or behind, and more so the faster the neighbour is moving; and if the neighbour moves fast enough, the differences coming from the neighbour’s relative position largely disappear. From the lens of decision-making, neighbour speed acts as confidence measure about where to go. This article is part of a discussion meeting issue ‘Collective behaviour through time’.
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spelling pubmed-99392712023-02-20 A study of transfer of information in animal collectives using deep learning tools Romero-Ferrero, Francisco Heras, Francisco J. H. Rance, Dean de Polavieja, Gonzalo G. Philos Trans R Soc Lond B Biol Sci Articles We studied how the interactions among animals in a collective allow for the transfer of information. We performed laboratory experiments to study how zebrafish in a collective follow a subset of trained animals that move towards a light when it turns on because they expect food at that location. We built some deep learning tools to distinguish from video which are the trained and the naïve animals and to detect when each animal reacts to the light turning on. These tools gave us the data to build a model of interactions that we designed to have a balance between transparency and accuracy. The model finds a low-dimensional function that describes how a naïve animal weights neighbours depending on focal and neighbour variables. According to this low-dimensional function, neighbour speed plays an important role in the interactions. Specifically, a naïve animal weights more a neighbour in front than to the sides or behind, and more so the faster the neighbour is moving; and if the neighbour moves fast enough, the differences coming from the neighbour’s relative position largely disappear. From the lens of decision-making, neighbour speed acts as confidence measure about where to go. This article is part of a discussion meeting issue ‘Collective behaviour through time’. The Royal Society 2023-04-10 2023-02-20 /pmc/articles/PMC9939271/ /pubmed/36802786 http://dx.doi.org/10.1098/rstb.2022.0073 Text en © 2023 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited.
spellingShingle Articles
Romero-Ferrero, Francisco
Heras, Francisco J. H.
Rance, Dean
de Polavieja, Gonzalo G.
A study of transfer of information in animal collectives using deep learning tools
title A study of transfer of information in animal collectives using deep learning tools
title_full A study of transfer of information in animal collectives using deep learning tools
title_fullStr A study of transfer of information in animal collectives using deep learning tools
title_full_unstemmed A study of transfer of information in animal collectives using deep learning tools
title_short A study of transfer of information in animal collectives using deep learning tools
title_sort study of transfer of information in animal collectives using deep learning tools
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9939271/
https://www.ncbi.nlm.nih.gov/pubmed/36802786
http://dx.doi.org/10.1098/rstb.2022.0073
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