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Neuron. 2015 Apr 22;86(2):428-41. doi: 10.1016/j.neuron.2015.03.026. Epub 2015 Apr 9.

Neural Mechanisms for Evaluating Environmental Variability in Caenorhabditis elegans.

Author information

1
Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA; Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA; Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA.
2
Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA.
3
Howard Hughes Medical Institute, Lulu and Anthony Wang Laboratory of Neural Circuits and Behavior, The Rockefeller University, New York, NY 10065, USA.
4
Waitt Advanced Biophotonics Center, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA.
5
Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA; Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA.
6
Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA; Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, USA. Electronic address: schalasani@salk.edu.

Abstract

The ability to evaluate variability in the environment is vital for making optimal behavioral decisions. Here we show that Caenorhabditis elegans evaluates variability in its food environment and modifies its future behavior accordingly. We derive a behavioral model that reveals a critical period over which information about the food environment is acquired and predicts future search behavior. We also identify a pair of high-threshold sensory neurons that encode variability in food concentration and the downstream dopamine-dependent circuit that generates appropriate search behavior upon removal from food. Further, we show that CREB is required in a subset of interneurons and determines the timescale over which the variability is integrated. Interestingly, the variability circuit is a subset of a larger circuit driving search behavior, showing that learning directly modifies the very same neurons driving behavior. Our study reveals how a neural circuit decodes environmental variability to generate contextually appropriate decisions.

PMID:
25864633
PMCID:
PMC4409562
DOI:
10.1016/j.neuron.2015.03.026
[Indexed for MEDLINE]
Free PMC Article

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