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Elife. 2016 Jan 29;5. pii: e12572. doi: 10.7554/eLife.12572.

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

Author information

1
Institute of Neuroscience, University of Oregon, Eugene, United States.
2
School of Nursing, University of Pennsylvania, Philadelphia, United States.
3
Biology Department, Reed College, Portland, United States.
4
Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States.
5
Department of Biological Sciences, University of Toronto, Toronto, Canada.
6
Cognitive Science Program, Indiana University, Bloomington, United States.
7
Department of Ophthalmology, The Vision Center, Children's Hospital Los Angeles, Los Angeles, United States.
8
Department of Neurology, University of Minnesota, Minneapolis, United States.
9
Department of Life Sciences, New York Institute of Technology, Old Westbury, United States.
10
Howard Hughes Medical Institute, Rockefeller University, New York, United States.
#
Contributed equally

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.

KEYWORDS:

C. elegans; computational biology; hidden Markov model; locomotion; neuroscience; spatial orientation; stochastic neural network; systems biology

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