Data from: A stochastic vision based model inspired by the collective behaviour of zebrafish in heterogeneous environments

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 impels one to revise classical assumptions made in decisional algorithms. In this context, we present a model describing the three-dimensional visual sensory system of fish that adjust their trajectory according to their perception field. Furthermore, we introduce a stochastic process based on a probability distribution function to move in targeted directions rather than on a summation of influential vectors as is classically assumed by most models. 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.群体运动是社会性生物体最为普遍的行为表现之一，由此催生了诸多相关研究模型。近年来，学界对动物感觉系统与信息处理机制的认知不断深入，这促使研究者重新审视决策算法中的经典假设。在此背景下，本研究提出一款模型，用于描述鱼类依据自身感知域调整运动轨迹的三维视觉感知系统。此外，本研究引入一种基于概率分布函数的随机过程，使个体能够沿目标方向运动，而非多数经典模型所预设的通过叠加影响力向量完成运动决策的逻辑。与此同时，本研究还给出了斑马鱼（单独饲养或10只成群）在均质与非均质环境中游动的实验结果。本研究利用上述实验数据确定模型的参数取值，并证实该基于感知的建模方法能够模拟在非均质环境中展现出聚集行为的物种的群体运动。最后，本研究探讨了该多层模型的研究进展，以及其在生物学、物理学与机器人科学领域的潜在应用价值。