Zebrafish collective behaviour in heterogeneous environment modeled by a stochastic model based on visual perception

Abstract

Collective motion is one of the most ubiquitous behaviours displayed by social organisms and has led to the development of numerous models. Recent advances in the understanding of sensory system and information processing by animals impel to revise classical assumptions made in decisional algorithms. In this context, we present a new model describing the three dimensional visual sensory system of fish that adjust their trajectory according to their perception field. Furthermore, we introduce a new stochastic process based on a probability distribution function to move in targeted directions rather than on a summation of influential vectors as it is classically assumed by most models. We show that this model can spontaneously transits from consensus to choice. In parallel, we present experimental results of zebrafish (alone or in group of 10) swimming in both homogeneous and heterogeneous environments. We use these experimental data to set the parameter values of our model and show that this perception-based approach can simulate the collective motion of species showing cohesive behaviour in heterogeneous environments. Finally, we discuss the advances of this multilayer model and its possible outcomes in biological, physical and robotic sciences.

15 pages, 16 figures

References in corpus (4)

• Novel type of phase transition in a system of self-driven particles
• Collective motion of self-propelled particles interacting without cohesion
• Collective Motion of Self-Propelled Particles with Memory
• A hierarchy of heuristic-based models of crowd dynamics

Cited by in corpus (13)

• Disentangling and modeling interactions in fish with burst-and-coast swimming
• Collective behavior from surprise minimization
• Strains differences in the collective behaviour of zebrafish (Danio rerio) in heterogeneous environment
• How to Blend a Robot within a Group of Zebrafish: Achieving Social Acceptance through Real-time Calibration of a Multi-level Behavioural Model
• Automated optimisation of multi-level models of collective behaviour in a mixed society of animals and robots
• Development of collective behavior in newborn artificial agents
• Selective decision-making and collective behavior of fish by the motion of visual attention
• Automatic Calibration of Artificial Neural Networks for Zebrafish Collective Behaviours using a Quality Diversity Algorithm
• Loose social organisation of AB strain zebrafish groups in a two-patch environment
• Evolutionary optimisation of neural network models for fish collective behaviours in mixed groups of robots and zebrafish
• Visual collective behaviors on spherical robots
• Modelling zebrafish collective behaviours with multilayer perceptrons optimised by evolutionary algorithms
• Boltzmann approach to collective motion via nonlocal visual interaction