<b>A stochastic neuronal model predicts random search behaviors at multiple spatial scales in</b> <i>C. elegans</i>.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 26824391.
- Also identified by DOI 10.7554/eLife.12572 and PMC identifier 4798983.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
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 <i>Caenorhabditis elegans</i>, an organism with a small, well-described nervous system. Here we formulate a mathematical model of random search abstracted from the <i>C. elegans</i> connectome and fit to a large-scale kinematic analysis of <i>C. elegans</i> 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 <i>C. elegans</i> 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 <i>C. elegans</i> locomotion and a testable neuronal model of random search that can be applied to other organisms.