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Reverse-correlation analysis of navigation dynamics in Drosophila larva using optogenetics

Neural circuits for behavior transform sensory inputs into motor outputs in patterns with strategic value. Determining how neurons along a sensorimotor circuit contribute to this transformation is central to understanding behavior. To do this, a quantitative framework to describe behavioral dynamics...

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Autores principales: Hernandez-Nunez, Luis, Belina, Jonas, Klein, Mason, Si, Guangwei, Claus, Lindsey, Carlson, John R, Samuel, Aravinthan DT
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4466337/
https://www.ncbi.nlm.nih.gov/pubmed/25942453
http://dx.doi.org/10.7554/eLife.06225
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author Hernandez-Nunez, Luis
Belina, Jonas
Klein, Mason
Si, Guangwei
Claus, Lindsey
Carlson, John R
Samuel, Aravinthan DT
author_facet Hernandez-Nunez, Luis
Belina, Jonas
Klein, Mason
Si, Guangwei
Claus, Lindsey
Carlson, John R
Samuel, Aravinthan DT
author_sort Hernandez-Nunez, Luis
collection PubMed
description Neural circuits for behavior transform sensory inputs into motor outputs in patterns with strategic value. Determining how neurons along a sensorimotor circuit contribute to this transformation is central to understanding behavior. To do this, a quantitative framework to describe behavioral dynamics is needed. In this study, we built a high-throughput optogenetic system for Drosophila larva to quantify the sensorimotor transformations underlying navigational behavior. We express CsChrimson, a red-shifted variant of channelrhodopsin, in specific chemosensory neurons and expose large numbers of freely moving animals to random optogenetic activation patterns. We quantify their behavioral responses and use reverse-correlation analysis to uncover the linear and static nonlinear components of navigation dynamics as functions of optogenetic activation patterns of specific sensory neurons. We find that linear–nonlinear models accurately predict navigational decision-making for different optogenetic activation waveforms. We use our method to establish the valence and dynamics of navigation driven by optogenetic activation of different combinations of bitter-sensing gustatory neurons. Our method captures the dynamics of optogenetically induced behavior in compact, quantitative transformations that can be used to characterize circuits for sensorimotor processing and their contribution to navigational decision making. DOI: http://dx.doi.org/10.7554/eLife.06225.001
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spelling pubmed-44663372015-06-17 Reverse-correlation analysis of navigation dynamics in Drosophila larva using optogenetics Hernandez-Nunez, Luis Belina, Jonas Klein, Mason Si, Guangwei Claus, Lindsey Carlson, John R Samuel, Aravinthan DT eLife Neuroscience Neural circuits for behavior transform sensory inputs into motor outputs in patterns with strategic value. Determining how neurons along a sensorimotor circuit contribute to this transformation is central to understanding behavior. To do this, a quantitative framework to describe behavioral dynamics is needed. In this study, we built a high-throughput optogenetic system for Drosophila larva to quantify the sensorimotor transformations underlying navigational behavior. We express CsChrimson, a red-shifted variant of channelrhodopsin, in specific chemosensory neurons and expose large numbers of freely moving animals to random optogenetic activation patterns. We quantify their behavioral responses and use reverse-correlation analysis to uncover the linear and static nonlinear components of navigation dynamics as functions of optogenetic activation patterns of specific sensory neurons. We find that linear–nonlinear models accurately predict navigational decision-making for different optogenetic activation waveforms. We use our method to establish the valence and dynamics of navigation driven by optogenetic activation of different combinations of bitter-sensing gustatory neurons. Our method captures the dynamics of optogenetically induced behavior in compact, quantitative transformations that can be used to characterize circuits for sensorimotor processing and their contribution to navigational decision making. DOI: http://dx.doi.org/10.7554/eLife.06225.001 eLife Sciences Publications, Ltd 2015-05-05 /pmc/articles/PMC4466337/ /pubmed/25942453 http://dx.doi.org/10.7554/eLife.06225 Text en © 2015, Hernandez-Nunez et al http://creativecommons.org/licenses/by/4.0/ This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Neuroscience
Hernandez-Nunez, Luis
Belina, Jonas
Klein, Mason
Si, Guangwei
Claus, Lindsey
Carlson, John R
Samuel, Aravinthan DT
Reverse-correlation analysis of navigation dynamics in Drosophila larva using optogenetics
title Reverse-correlation analysis of navigation dynamics in Drosophila larva using optogenetics
title_full Reverse-correlation analysis of navigation dynamics in Drosophila larva using optogenetics
title_fullStr Reverse-correlation analysis of navigation dynamics in Drosophila larva using optogenetics
title_full_unstemmed Reverse-correlation analysis of navigation dynamics in Drosophila larva using optogenetics
title_short Reverse-correlation analysis of navigation dynamics in Drosophila larva using optogenetics
title_sort reverse-correlation analysis of navigation dynamics in drosophila larva using optogenetics
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4466337/
https://www.ncbi.nlm.nih.gov/pubmed/25942453
http://dx.doi.org/10.7554/eLife.06225
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