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Using neuronal models to capture burst-and-glide motion and leadership in fish
While mathematical models, in particular self-propelled particle models, capture many properties of large fish schools, they do not always capture the interactions of smaller shoals. Nor do these models tend to account for the use of intermittent locomotion, often referred to as burst-and-glide, by...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10354474/ https://www.ncbi.nlm.nih.gov/pubmed/37464800 http://dx.doi.org/10.1098/rsif.2023.0212 |
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author | Gyllingberg, Linnéa Szorkovszky, Alex Sumpter, David J. T. |
author_facet | Gyllingberg, Linnéa Szorkovszky, Alex Sumpter, David J. T. |
author_sort | Gyllingberg, Linnéa |
collection | PubMed |
description | While mathematical models, in particular self-propelled particle models, capture many properties of large fish schools, they do not always capture the interactions of smaller shoals. Nor do these models tend to account for the use of intermittent locomotion, often referred to as burst-and-glide, by many species. In this paper, we propose a model of social burst-and-glide motion by combining a well-studied model of neuronal dynamics, the FitzHugh–Nagumo model, with a model of fish motion. We first show that our model can capture the motion of a single fish swimming down a channel. Extending to a two-fish model, where visual stimulus of a neighbour affects the internal burst or glide state of the fish, we observe a rich set of dynamics found in many species. These include: leader–follower behaviour; periodic changes in leadership; apparently random (i.e. chaotic) leadership change; and tit-for-tat turn taking. Moreover, unlike previous studies where a randomness is required for leadership switching to occur, we show that this can instead be the result of deterministic interactions. We give several empirically testable predictions for how bursting fish interact and discuss our results in light of recently established correlations between fish locomotion and brain activity. |
format | Online Article Text |
id | pubmed-10354474 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-103544742023-07-20 Using neuronal models to capture burst-and-glide motion and leadership in fish Gyllingberg, Linnéa Szorkovszky, Alex Sumpter, David J. T. J R Soc Interface Life Sciences–Mathematics interface While mathematical models, in particular self-propelled particle models, capture many properties of large fish schools, they do not always capture the interactions of smaller shoals. Nor do these models tend to account for the use of intermittent locomotion, often referred to as burst-and-glide, by many species. In this paper, we propose a model of social burst-and-glide motion by combining a well-studied model of neuronal dynamics, the FitzHugh–Nagumo model, with a model of fish motion. We first show that our model can capture the motion of a single fish swimming down a channel. Extending to a two-fish model, where visual stimulus of a neighbour affects the internal burst or glide state of the fish, we observe a rich set of dynamics found in many species. These include: leader–follower behaviour; periodic changes in leadership; apparently random (i.e. chaotic) leadership change; and tit-for-tat turn taking. Moreover, unlike previous studies where a randomness is required for leadership switching to occur, we show that this can instead be the result of deterministic interactions. We give several empirically testable predictions for how bursting fish interact and discuss our results in light of recently established correlations between fish locomotion and brain activity. The Royal Society 2023-07-19 /pmc/articles/PMC10354474/ /pubmed/37464800 http://dx.doi.org/10.1098/rsif.2023.0212 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 | Life Sciences–Mathematics interface Gyllingberg, Linnéa Szorkovszky, Alex Sumpter, David J. T. Using neuronal models to capture burst-and-glide motion and leadership in fish |
title | Using neuronal models to capture burst-and-glide motion and leadership in fish |
title_full | Using neuronal models to capture burst-and-glide motion and leadership in fish |
title_fullStr | Using neuronal models to capture burst-and-glide motion and leadership in fish |
title_full_unstemmed | Using neuronal models to capture burst-and-glide motion and leadership in fish |
title_short | Using neuronal models to capture burst-and-glide motion and leadership in fish |
title_sort | using neuronal models to capture burst-and-glide motion and leadership in fish |
topic | Life Sciences–Mathematics interface |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10354474/ https://www.ncbi.nlm.nih.gov/pubmed/37464800 http://dx.doi.org/10.1098/rsif.2023.0212 |
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