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 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.