Reverse engineering the control law for schooling in zebrafish using virtual reality

Abstract

Revealing the evolved mechanisms that give rise to collective behavior is a central objective in the study of cellular and organismal systems. In addition, understanding the algorithmic basis of social interactions in a causal and quantitative way offers an important foundation for subsequently quantifying social deficits. Here, with virtual reality technology, we used virtual robot fish to reverse engineer the sensory-motor control of social response during schooling in a vertebrate model: juvenile zebrafish (Danio rerio). In addition to providing a highly controlled means to understand how zebrafish translate visual input into movement decisions, networking our systems allowed real fish to swim and interact together in the same virtual world. Thus, we were able to directly test models of social interactions in situ. A key feature of social response is shown to be single- and multitarget-oriented pursuit. This is based on an egocentric representation of the positional information of conspecifics and is highly robust to incomplete sensory input. We demonstrated, including with a Turing test and a scalability test for pursuit behavior, that all key features of this behavior are accounted for by individuals following a simple experimentally derived proportional derivative control law, which we termed “BioPD.” Because target pursuit is key to effective control of autonomous vehicles, we evaluated—as a proof of principle—the potential use of this simple evolved control law for human-engineered systems. In doing so, we found close-to-optimal pursuit performance in autonomous vehicle (terrestrial, airborne, and watercraft) pursuit while requiring limited system-specific tuning or optimization.