Neural network features distinguish chemosensory stimuli in Caenorhabditis elegans.

Nervous systems extract and process information from the environment to alter animal behavior and physiology. Despite progress in understanding how different stimuli are represented by changes in neuronal activity, less is known about how they affect broader neural network properties. We developed a...

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Autores principales:	Javier J How, Saket Navlakha, Sreekanth H Chalasani
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/05a68e742d90469ca05846e210860f9b
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spelling	oai:doaj.org-article:05a68e742d90469ca05846e210860f9b2021-12-02T19:57:57ZNeural network features distinguish chemosensory stimuli in Caenorhabditis elegans.1553-734X1553-735810.1371/journal.pcbi.1009591https://doaj.org/article/05a68e742d90469ca05846e210860f9b2021-11-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009591https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Nervous systems extract and process information from the environment to alter animal behavior and physiology. Despite progress in understanding how different stimuli are represented by changes in neuronal activity, less is known about how they affect broader neural network properties. We developed a framework for using graph-theoretic features of neural network activity to predict ecologically relevant stimulus properties, in particular stimulus identity. We used the transparent nematode, Caenorhabditis elegans, with its small nervous system to define neural network features associated with various chemosensory stimuli. We first immobilized animals using a microfluidic device and exposed their noses to chemical stimuli while monitoring changes in neural activity of more than 50 neurons in the head region. We found that graph-theoretic features, which capture patterns of interactions between neurons, are modulated by stimulus identity. Further, we show that a simple machine learning classifier trained using graph-theoretic features alone, or in combination with neural activity features, can accurately predict salt stimulus. Moreover, by focusing on putative causal interactions between neurons, the graph-theoretic features were almost twice as predictive as the neural activity features. These results reveal that stimulus identity modulates the broad, network-level organization of the nervous system, and that graph theory can be used to characterize these changes.Javier J HowSaket NavlakhaSreekanth H ChalasaniPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 11, p e1009591 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Javier J How Saket Navlakha Sreekanth H Chalasani Neural network features distinguish chemosensory stimuli in Caenorhabditis elegans.
description	Nervous systems extract and process information from the environment to alter animal behavior and physiology. Despite progress in understanding how different stimuli are represented by changes in neuronal activity, less is known about how they affect broader neural network properties. We developed a framework for using graph-theoretic features of neural network activity to predict ecologically relevant stimulus properties, in particular stimulus identity. We used the transparent nematode, Caenorhabditis elegans, with its small nervous system to define neural network features associated with various chemosensory stimuli. We first immobilized animals using a microfluidic device and exposed their noses to chemical stimuli while monitoring changes in neural activity of more than 50 neurons in the head region. We found that graph-theoretic features, which capture patterns of interactions between neurons, are modulated by stimulus identity. Further, we show that a simple machine learning classifier trained using graph-theoretic features alone, or in combination with neural activity features, can accurately predict salt stimulus. Moreover, by focusing on putative causal interactions between neurons, the graph-theoretic features were almost twice as predictive as the neural activity features. These results reveal that stimulus identity modulates the broad, network-level organization of the nervous system, and that graph theory can be used to characterize these changes.
format	article
author	Javier J How Saket Navlakha Sreekanth H Chalasani
author_facet	Javier J How Saket Navlakha Sreekanth H Chalasani
author_sort	Javier J How
title	Neural network features distinguish chemosensory stimuli in Caenorhabditis elegans.
title_short	Neural network features distinguish chemosensory stimuli in Caenorhabditis elegans.
title_full	Neural network features distinguish chemosensory stimuli in Caenorhabditis elegans.
title_fullStr	Neural network features distinguish chemosensory stimuli in Caenorhabditis elegans.
title_full_unstemmed	Neural network features distinguish chemosensory stimuli in Caenorhabditis elegans.
title_sort	neural network features distinguish chemosensory stimuli in caenorhabditis elegans.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/05a68e742d90469ca05846e210860f9b
work_keys_str_mv	AT javierjhow neuralnetworkfeaturesdistinguishchemosensorystimuliincaenorhabditiselegans AT saketnavlakha neuralnetworkfeaturesdistinguishchemosensorystimuliincaenorhabditiselegans AT sreekanthhchalasani neuralnetworkfeaturesdistinguishchemosensorystimuliincaenorhabditiselegans
_version_	1718375761357832192

Neural network features distinguish chemosensory stimuli in Caenorhabditis elegans.

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