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Statistical modelling of navigational decisions based on intensity versus directionality in Drosophila larval phototaxis

Organisms use environmental cues for directed navigation. Understanding the basic logic behind navigational decisions critically depends on the complexity of the nervous system. Due to the comparably simple organization of the nervous system of the fruit fly larva, it stands as a powerful model to s...

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Autores principales: de Andres-Bragado, Lucia, Mazza, Christian, Senn, Walter, Sprecher, Simon G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6062584/
https://www.ncbi.nlm.nih.gov/pubmed/30050066
http://dx.doi.org/10.1038/s41598-018-29533-0
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author de Andres-Bragado, Lucia
Mazza, Christian
Senn, Walter
Sprecher, Simon G.
author_facet de Andres-Bragado, Lucia
Mazza, Christian
Senn, Walter
Sprecher, Simon G.
author_sort de Andres-Bragado, Lucia
collection PubMed
description Organisms use environmental cues for directed navigation. Understanding the basic logic behind navigational decisions critically depends on the complexity of the nervous system. Due to the comparably simple organization of the nervous system of the fruit fly larva, it stands as a powerful model to study decision-making processes that underlie directed navigation. We have quantitatively measured phototaxis in response to well-defined sensory inputs. Subsequently, we have formulated a statistical stochastic model based on biased Markov chains to characterize the behavioural basis of negative phototaxis. Our experiments show that larvae make navigational decisions depending on two independent physical variables: light intensity and its spatial gradient. Furthermore, our statistical model quantifies how larvae balance two potentially-contradictory factors: minimizing exposure to light intensity and at the same time maximizing their distance to the light source. We find that the response to the light field is manifestly non-linear, and saturates above an intensity threshold. The model has been validated against our experimental biological data yielding insight into the strategy that larvae use to achieve their goal with respect to the navigational cue of light, an important piece of information for future work to study the role of the different neuronal components in larval phototaxis.
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spelling pubmed-60625842018-07-31 Statistical modelling of navigational decisions based on intensity versus directionality in Drosophila larval phototaxis de Andres-Bragado, Lucia Mazza, Christian Senn, Walter Sprecher, Simon G. Sci Rep Article Organisms use environmental cues for directed navigation. Understanding the basic logic behind navigational decisions critically depends on the complexity of the nervous system. Due to the comparably simple organization of the nervous system of the fruit fly larva, it stands as a powerful model to study decision-making processes that underlie directed navigation. We have quantitatively measured phototaxis in response to well-defined sensory inputs. Subsequently, we have formulated a statistical stochastic model based on biased Markov chains to characterize the behavioural basis of negative phototaxis. Our experiments show that larvae make navigational decisions depending on two independent physical variables: light intensity and its spatial gradient. Furthermore, our statistical model quantifies how larvae balance two potentially-contradictory factors: minimizing exposure to light intensity and at the same time maximizing their distance to the light source. We find that the response to the light field is manifestly non-linear, and saturates above an intensity threshold. The model has been validated against our experimental biological data yielding insight into the strategy that larvae use to achieve their goal with respect to the navigational cue of light, an important piece of information for future work to study the role of the different neuronal components in larval phototaxis. Nature Publishing Group UK 2018-07-26 /pmc/articles/PMC6062584/ /pubmed/30050066 http://dx.doi.org/10.1038/s41598-018-29533-0 Text en © The Author(s) 2018 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
de Andres-Bragado, Lucia
Mazza, Christian
Senn, Walter
Sprecher, Simon G.
Statistical modelling of navigational decisions based on intensity versus directionality in Drosophila larval phototaxis
title Statistical modelling of navigational decisions based on intensity versus directionality in Drosophila larval phototaxis
title_full Statistical modelling of navigational decisions based on intensity versus directionality in Drosophila larval phototaxis
title_fullStr Statistical modelling of navigational decisions based on intensity versus directionality in Drosophila larval phototaxis
title_full_unstemmed Statistical modelling of navigational decisions based on intensity versus directionality in Drosophila larval phototaxis
title_short Statistical modelling of navigational decisions based on intensity versus directionality in Drosophila larval phototaxis
title_sort statistical modelling of navigational decisions based on intensity versus directionality in drosophila larval phototaxis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6062584/
https://www.ncbi.nlm.nih.gov/pubmed/30050066
http://dx.doi.org/10.1038/s41598-018-29533-0
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