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Computational and robotic modeling reveal parsimonious combinations of interactions between individuals in schooling fish

Coordinated motion and collective decision-making in fish schools result from complex interactions by which individuals integrate information about the behavior of their neighbors. However, little is known about how individuals integrate this information to take decisions and control their motion. H...

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Autores principales: Lei, Liu, Escobedo, Ramón, Sire, Clément, Theraulaz, Guy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7098660/
https://www.ncbi.nlm.nih.gov/pubmed/32176680
http://dx.doi.org/10.1371/journal.pcbi.1007194
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author Lei, Liu
Escobedo, Ramón
Sire, Clément
Theraulaz, Guy
author_facet Lei, Liu
Escobedo, Ramón
Sire, Clément
Theraulaz, Guy
author_sort Lei, Liu
collection PubMed
description Coordinated motion and collective decision-making in fish schools result from complex interactions by which individuals integrate information about the behavior of their neighbors. However, little is known about how individuals integrate this information to take decisions and control their motion. Here, we combine experiments with computational and robotic approaches to investigate the impact of different strategies for a fish to interact with its neighbors on collective swimming in groups of rummy-nose tetra (Hemigrammus rhodostomus). By means of a data-based agent model describing the interactions between pairs of H. rhodostomus (Calovi et al., 2018), we show that the simple addition of the pairwise interactions with two neighbors quantitatively reproduces the collective behavior observed in groups of five fish. Increasing the number of interacting neighbors does not significantly improve the simulation results. Remarkably, and even without confinement, we find that groups remain cohesive and polarized when each agent interacts with only one of its neighbors: the one that has the strongest contribution to the heading variation of the focal agent, dubbed as the “most influential neighbor”. However, group cohesion is lost when each agent only interacts with its nearest neighbor. We then investigate by means of a robotic platform the collective motion in groups of five robots. Our platform combines the implementation of the fish behavioral model and a control system to deal with real-world physical constraints. A better agreement with experimental results for fish is obtained for groups of robots only interacting with their most influential neighbor, than for robots interacting with one or even two nearest neighbors. Finally, we discuss the biological and cognitive relevance of the notion of “most influential neighbors”. Overall, our results suggest that fish have to acquire only a minimal amount of information about their environment to coordinate their movements when swimming in groups.
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spelling pubmed-70986602020-04-03 Computational and robotic modeling reveal parsimonious combinations of interactions between individuals in schooling fish Lei, Liu Escobedo, Ramón Sire, Clément Theraulaz, Guy PLoS Comput Biol Research Article Coordinated motion and collective decision-making in fish schools result from complex interactions by which individuals integrate information about the behavior of their neighbors. However, little is known about how individuals integrate this information to take decisions and control their motion. Here, we combine experiments with computational and robotic approaches to investigate the impact of different strategies for a fish to interact with its neighbors on collective swimming in groups of rummy-nose tetra (Hemigrammus rhodostomus). By means of a data-based agent model describing the interactions between pairs of H. rhodostomus (Calovi et al., 2018), we show that the simple addition of the pairwise interactions with two neighbors quantitatively reproduces the collective behavior observed in groups of five fish. Increasing the number of interacting neighbors does not significantly improve the simulation results. Remarkably, and even without confinement, we find that groups remain cohesive and polarized when each agent interacts with only one of its neighbors: the one that has the strongest contribution to the heading variation of the focal agent, dubbed as the “most influential neighbor”. However, group cohesion is lost when each agent only interacts with its nearest neighbor. We then investigate by means of a robotic platform the collective motion in groups of five robots. Our platform combines the implementation of the fish behavioral model and a control system to deal with real-world physical constraints. A better agreement with experimental results for fish is obtained for groups of robots only interacting with their most influential neighbor, than for robots interacting with one or even two nearest neighbors. Finally, we discuss the biological and cognitive relevance of the notion of “most influential neighbors”. Overall, our results suggest that fish have to acquire only a minimal amount of information about their environment to coordinate their movements when swimming in groups. Public Library of Science 2020-03-16 /pmc/articles/PMC7098660/ /pubmed/32176680 http://dx.doi.org/10.1371/journal.pcbi.1007194 Text en © 2020 Lei et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Lei, Liu
Escobedo, Ramón
Sire, Clément
Theraulaz, Guy
Computational and robotic modeling reveal parsimonious combinations of interactions between individuals in schooling fish
title Computational and robotic modeling reveal parsimonious combinations of interactions between individuals in schooling fish
title_full Computational and robotic modeling reveal parsimonious combinations of interactions between individuals in schooling fish
title_fullStr Computational and robotic modeling reveal parsimonious combinations of interactions between individuals in schooling fish
title_full_unstemmed Computational and robotic modeling reveal parsimonious combinations of interactions between individuals in schooling fish
title_short Computational and robotic modeling reveal parsimonious combinations of interactions between individuals in schooling fish
title_sort computational and robotic modeling reveal parsimonious combinations of interactions between individuals in schooling fish
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7098660/
https://www.ncbi.nlm.nih.gov/pubmed/32176680
http://dx.doi.org/10.1371/journal.pcbi.1007194
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