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Reverse-Correlation Analysis of the Mechanosensation Circuit and Behavior in C. elegans Reveals Temporal and Spatial Encoding

Animals must integrate the activity of multiple mechanoreceptors to navigate complex environments. In Caenorhabditis elegans, the general roles of the mechanosensory neurons have been defined, but most studies involve end-point or single-time-point measurements, and thus lack dynamic information. He...

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Autores principales: Porto, Daniel A., Giblin, John, Zhao, Yiran, Lu, Hang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6435754/
https://www.ncbi.nlm.nih.gov/pubmed/30914655
http://dx.doi.org/10.1038/s41598-019-41349-0
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author Porto, Daniel A.
Giblin, John
Zhao, Yiran
Lu, Hang
author_facet Porto, Daniel A.
Giblin, John
Zhao, Yiran
Lu, Hang
author_sort Porto, Daniel A.
collection PubMed
description Animals must integrate the activity of multiple mechanoreceptors to navigate complex environments. In Caenorhabditis elegans, the general roles of the mechanosensory neurons have been defined, but most studies involve end-point or single-time-point measurements, and thus lack dynamic information. Here, we formulate a set of unbiased quantitative characterizations of the mechanosensory system by using reverse correlation analysis on behavior. We use a custom tracking, selective illumination, and optogenetics platform to compare two mechanosensory systems: the gentle-touch (TRNs) and harsh-touch (PVD) circuits. This method yields characteristic linear filters that allow for the prediction of behavioral responses. The resulting filters are consistent with previous findings and further provide new insights on the dynamics and spatial encoding of the systems. Our results suggest that the tiled network of the gentle-touch neurons has better resolution for spatial encoding than the harsh-touch neurons. Additionally, linear-nonlinear models can predict behavioral responses based only on sensory neuron activity. Our results capture the overall dynamics of behavior induced by the activation of sensory neurons, providing simple transformations that quantitatively characterize these systems. Furthermore, this platform can be extended to capture the behavioral dynamics induced by any neuron or other excitable cells in the animal.
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spelling pubmed-64357542019-04-03 Reverse-Correlation Analysis of the Mechanosensation Circuit and Behavior in C. elegans Reveals Temporal and Spatial Encoding Porto, Daniel A. Giblin, John Zhao, Yiran Lu, Hang Sci Rep Article Animals must integrate the activity of multiple mechanoreceptors to navigate complex environments. In Caenorhabditis elegans, the general roles of the mechanosensory neurons have been defined, but most studies involve end-point or single-time-point measurements, and thus lack dynamic information. Here, we formulate a set of unbiased quantitative characterizations of the mechanosensory system by using reverse correlation analysis on behavior. We use a custom tracking, selective illumination, and optogenetics platform to compare two mechanosensory systems: the gentle-touch (TRNs) and harsh-touch (PVD) circuits. This method yields characteristic linear filters that allow for the prediction of behavioral responses. The resulting filters are consistent with previous findings and further provide new insights on the dynamics and spatial encoding of the systems. Our results suggest that the tiled network of the gentle-touch neurons has better resolution for spatial encoding than the harsh-touch neurons. Additionally, linear-nonlinear models can predict behavioral responses based only on sensory neuron activity. Our results capture the overall dynamics of behavior induced by the activation of sensory neurons, providing simple transformations that quantitatively characterize these systems. Furthermore, this platform can be extended to capture the behavioral dynamics induced by any neuron or other excitable cells in the animal. Nature Publishing Group UK 2019-03-26 /pmc/articles/PMC6435754/ /pubmed/30914655 http://dx.doi.org/10.1038/s41598-019-41349-0 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Porto, Daniel A.
Giblin, John
Zhao, Yiran
Lu, Hang
Reverse-Correlation Analysis of the Mechanosensation Circuit and Behavior in C. elegans Reveals Temporal and Spatial Encoding
title Reverse-Correlation Analysis of the Mechanosensation Circuit and Behavior in C. elegans Reveals Temporal and Spatial Encoding
title_full Reverse-Correlation Analysis of the Mechanosensation Circuit and Behavior in C. elegans Reveals Temporal and Spatial Encoding
title_fullStr Reverse-Correlation Analysis of the Mechanosensation Circuit and Behavior in C. elegans Reveals Temporal and Spatial Encoding
title_full_unstemmed Reverse-Correlation Analysis of the Mechanosensation Circuit and Behavior in C. elegans Reveals Temporal and Spatial Encoding
title_short Reverse-Correlation Analysis of the Mechanosensation Circuit and Behavior in C. elegans Reveals Temporal and Spatial Encoding
title_sort reverse-correlation analysis of the mechanosensation circuit and behavior in c. elegans reveals temporal and spatial encoding
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6435754/
https://www.ncbi.nlm.nih.gov/pubmed/30914655
http://dx.doi.org/10.1038/s41598-019-41349-0
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