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

Revealing the evolved mechanisms that give rise to collective behavior is
a central objective in the study of cellular and organismal systems.
Additionally, 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 (VR)
technology, we employ 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
to movement decisions, networking our systems allows real fish to swim and
interact together in the same virtual world. Together, this allows us to
directly test models of social interactions in situ. A key feature of
social response is shown to be single- and multi-target-oriented pursuit.
This is based on an egocentric representation of the positional
information of conspecifics, and is highly robust to incomplete sensory
input. We demonstrate, 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 term ‘BioPD’. Since target pursuit is key
to effective control of autonomous vehicles, we evaluate—as a proof of
principle—the potential utility of this simple evolved control law for
human-engineered systems. In doing so, we find close-to-optimal pursuit
performance in autonomous vehicle (terrestrial, airborne, and watercraft)
pursuit, while requiring limited system-specific tuning or optimization.