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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.

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