A stochastic neuronal model predicts random search behaviors at multiple spatial scales in C. elegans
- University of Oregon, United States
- University of Pennsylvania, United States
- Reed College, United States
- California Institute of Technology, United States
- University of Toronto, Canada
- Indiana University, United States
- Children's Hospital Los Angeles, United States
- University of Minnesota, United States
- New York Institiute of Technology, United States
- Howard Hughes Medical Institute, Rockefeller University, United States
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 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.
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© 2016, Roberts et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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A stochastic neuronal model predicts random search behaviors at multiple spatial scales in C. elegans
eLife 5:e12572.
https://doi.org/10.7554/eLife.12572
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