Literature DB >> 32176680

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

Liu Lei1,2, Ramón Escobedo2, Clément Sire3, Guy Theraulaz2.   

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.

Entities:  

Year:  2020        PMID: 32176680      PMCID: PMC7098660          DOI: 10.1371/journal.pcbi.1007194

Source DB:  PubMed          Journal:  PLoS Comput Biol        ISSN: 1553-734X            Impact factor:   4.475


  26 in total

1.  Inferring the rules of interaction of shoaling fish.

Authors:  James E Herbert-Read; Andrea Perna; Richard P Mann; Timothy M Schaerf; David J T Sumpter; Ashley J W Ward
Journal:  Proc Natl Acad Sci U S A       Date:  2011-11-07       Impact factor: 11.205

2.  Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations.

Authors:  C Muro; R Escobedo; L Spector; R P Coppinger
Journal:  Behav Processes       Date:  2011-09-28       Impact factor: 1.777

3.  Limited interactions in flocks: relating model simulations to empirical data.

Authors:  Nikolai W F Bode; Daniel W Franks; A Jamie Wood
Journal:  J R Soc Interface       Date:  2010-09-08       Impact factor: 4.118

4.  Interaction ruling animal collective behavior depends on topological rather than metric distance: evidence from a field study.

Authors:  M Ballerini; N Cabibbo; R Candelier; A Cavagna; E Cisbani; I Giardina; V Lecomte; A Orlandi; G Parisi; A Procaccini; M Viale; V Zdravkovic
Journal:  Proc Natl Acad Sci U S A       Date:  2008-01-28       Impact factor: 11.205

5.  Spatially balanced topological interaction grants optimal cohesion in flocking models.

Authors:  Marcelo Camperi; Andrea Cavagna; Irene Giardina; Giorgio Parisi; Edmondo Silvestri
Journal:  Interface Focus       Date:  2012-08-08       Impact factor: 3.906

Review 6.  From behavioural analyses to models of collective motion in fish schools.

Authors:  Ugo Lopez; Jacques Gautrais; Iain D Couzin; Guy Theraulaz
Journal:  Interface Focus       Date:  2012-10-03       Impact factor: 3.906

7.  Collective motion in animal groups from a neurobiological perspective: the adaptive benefits of dynamic sensory loads and selective attention.

Authors:  B H Lemasson; J J Anderson; R A Goodwin
Journal:  J Theor Biol       Date:  2009-08-20       Impact factor: 2.691

8.  Motion-guided attention promotes adaptive communications during social navigation.

Authors:  B H Lemasson; J J Anderson; R A Goodwin
Journal:  Proc Biol Sci       Date:  2013-01-16       Impact factor: 5.349

Review 9.  The Psychology of Superorganisms: Collective Decision Making by Insect Societies.

Authors:  Takao Sasaki; Stephen C Pratt
Journal:  Annu Rev Entomol       Date:  2017-10-04       Impact factor: 19.686

10.  Social conformity and propagation of information in collective U-turns of fish schools.

Authors:  Valentin Lecheval; Li Jiang; Pierre Tichit; Clément Sire; Charlotte K Hemelrijk; Guy Theraulaz
Journal:  Proc Biol Sci       Date:  2018-04-25       Impact factor: 5.349

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  6 in total

1.  A data-driven method for reconstructing and modelling social interactions in moving animal groups.

Authors:  R Escobedo; V Lecheval; V Papaspyros; F Bonnet; F Mondada; C Sire; G Theraulaz
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2020-07-27       Impact factor: 6.237

2.  Multi-scale analysis and modelling of collective migration in biological systems.

Authors:  Andreas Deutsch; Peter Friedl; Luigi Preziosi; Guy Theraulaz
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2020-07-27       Impact factor: 6.237

3.  Randomness in the choice of neighbours promotes cohesion in mobile animal groups.

Authors:  Vivek Jadhav; Vishwesha Guttal; Danny Raj Masila
Journal:  R Soc Open Sci       Date:  2022-03-23       Impact factor: 2.963

4.  Collective predator evasion: Putting the criticality hypothesis to the test.

Authors:  Pascal P Klamser; Pawel Romanczuk
Journal:  PLoS Comput Biol       Date:  2021-03-15       Impact factor: 4.475

5.  The impact of individual perceptual and cognitive factors on collective states in a data-driven fish school model.

Authors:  Weijia Wang; Ramón Escobedo; Stéphane Sanchez; Clément Sire; Zhangang Han; Guy Theraulaz
Journal:  PLoS Comput Biol       Date:  2022-03-02       Impact factor: 4.475

6.  Beyond Bio-Inspired Robotics: How Multi-Robot Systems Can Support Research on Collective Animal Behavior.

Authors:  Nikolaj Horsevad; Hian Lee Kwa; Roland Bouffanais
Journal:  Front Robot AI       Date:  2022-06-20
  6 in total

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