A computational model of internal representations of chemical gradients in environments for chemotaxis of Caenorhabditis elegans

Scientific Reports Volume 8 Page 17190- published_at 2018-11-21
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Title ( eng )
A computational model of internal representations of chemical gradients in environments for chemotaxis of Caenorhabditis elegans
Creator
Sakamoto Kazuma
Suzuki Michiyo
Iino Yuichi
Source Title
Scientific Reports
Volume 8
Start Page 17190
Abstract
The small roundworm Caenorhabditis elegans employs two strategies, termed pirouette and weathervane, which are closely related to the internal representation of chemical gradients parallel and perpendicular to the travelling direction, respectively, to perform chemotaxis. These gradients must be calculated from the chemical information obtained at a single point, because the sensory neurons are located close to each other at the nose tip. To formulate the relationship between this sensory input and internal representations of the chemical gradient, this study proposes a simple computational model derived from the directional decomposition of the chemical concentration at the nose tip that can generate internal representations of the chemical gradient. The ability of the computational model was verified by using a chemotaxis simulator that can simulate the body motions of pirouette and weathervane, which confirmed that the computational model enables the conversion of the sensory input and head-bending angles into both types of gradients with high correlations of approximately r > 0.90 (p < 0.01) with the true gradients. In addition, the chemotaxis index of the model was 0.64, which is slightly higher than that in the actual animal (0.57). In addition, simulation using a connectome-based neural network model confirmed that the proposed computational model is implementable in the actual network structure.
Descriptions
This work was supported by JSPS KAKENHI Grant Number 15H03950 to T.T and MEXT KAKENHI Grant Numbers 20115010 to T.T. and 20115002 to Y.I.
Language
eng
Resource Type journal article
Publisher
Nature Research
Date of Issued 2018-11-21
Rights
© The Author(s) 2018. 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/.
Publish Type Version of Record
Access Rights open access
Source Identifier
[ISSN] 2045-2322
[DOI] 10.1038/s41598-018-35157-1
[DOI] https://doi.org/10.1038/s41598-018-35157-1