ArticlePLoS computational biology2020
Computational and robotic modeling reveal parsimonious combinations of interactions between individuals in schooling fish.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed.
- Closed-loop real-virtual interactions validate 3D model of social coordination in fish.PLoS computational biology · 2026Article
- Swimming with robots: investigating fish locomotion, sensing, and schooling behavior with robotic swimmers.Nature communications · 2026Review
- An open-source closed-loop Virtual Reality system to investigate social interactions and collective behavior in fish.PloS one · 2026Article
- Collective phases and long-term dynamics in a fish school model with burst-and-coast swimming.Royal Society open science · 2025Article
- Survival Strategies and Color Preferences of Mandarin Fish (Animals : an open access journal from MDPI · 2025Article
- Collective responses of flocking sheep (Ovis aries) to a herding dog (border collie).Communications biology · 2024Article
- Body orientation change of neighbors leads to scale-free correlation in collective motion.Nature communications · 2024Article
- Speckle statistics as a tool to distinguish collective behaviors of Zebrafish shoals.Scientific reports · 2024Article
- Perception of motion salience shapes the emergence of collective motions.Nature communications · 2024Article
- Selective social interactions and speed-induced leadership in schooling fish.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Predicting the long-term collective behaviour of fish pairs with deep learning.Journal of the Royal Society, Interface · 2024Article
- Tuning social interactions' strength drives collective response to light intensity in schooling fish.PLoS computational biology · 2023Article
- Nearest Neighbour Node Deployment Algorithm for Mobile Sensor Networks.Sensors (Basel, Switzerland) · 2023Article
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- Randomness in the choice of neighbours promotes cohesion in mobile animal groups.Royal Society open science · 2022Article
- The impact of individual perceptual and cognitive factors on collective states in a data-driven fish school model.PLoS computational biology · 2022Article
- Beyond Bio-Inspired Robotics: How Multi-Robot Systems Can Support Research on Collective Animal Behavior.Frontiers in robotics and AI · 2022Article
- Collective predator evasion: Putting the criticality hypothesis to the test.PLoS computational biology · 2021Article
- A data-driven method for reconstructing and modelling social interactions in moving animal groups.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2020Article
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4 authors.
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Abstract
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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