Data from: A stochastic neuronal model predicts random search behaviors at multiple spatial scales in C. elegans

Random search is a behavioral strategy used by organisms from bacteria to
humans to locate food that is randomly distributed and undetectable at a
distance. We investigated this behavior in the nematode Caenorhabditis
elegans, an organism with a small, well-described nervous system. Here we
formulate a mathematical model of random search abstracted from the C.
elegans connectome and fit to a large-scale kinematic analysis of C.
elegans behavior at submicron resolution. The model predicts behavioral
effects of neuronal ablations and genetic perturbations, as well as
unexpected aspects of wild type behavior. The predictive success of the
model indicates that random search in C. elegans can be understood in
terms of a neuronal flip-flop circuit involving reciprocal inhibition
between two populations of stochastic neurons. Our findings establish a
unified theoretical framework for understanding C. elegans locomotion and
a testable neuronal model of random search that can be applied to other
organisms.