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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-11-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1009591 |
| _version_ | 1798034156087672832 |
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| author | Javier J How Saket Navlakha Sreekanth H Chalasani |
| author_facet | Javier J How Saket Navlakha Sreekanth H Chalasani |
| author_sort | Javier J How |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-11T20:39:26Z |
| format | Article |
| id | doaj.art-05a68e742d90469ca05846e210860f9b |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-11T20:39:26Z |
| publishDate | 2021-11-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-05a68e742d90469ca05846e210860f9b2022-12-22T04:04:14ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582021-11-011711e100959110.1371/journal.pcbi.1009591Neural network features distinguish chemosensory stimuli in Caenorhabditis elegans.Javier J HowSaket NavlakhaSreekanth H ChalasaniNervous 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.https://doi.org/10.1371/journal.pcbi.1009591 |
| spellingShingle | Javier J How Saket Navlakha Sreekanth H Chalasani Neural network features distinguish chemosensory stimuli in Caenorhabditis elegans. PLoS Computational Biology |
| title | 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_short | Neural network features distinguish chemosensory stimuli in Caenorhabditis elegans. |
| title_sort | neural network features distinguish chemosensory stimuli in caenorhabditis elegans |
| url | https://doi.org/10.1371/journal.pcbi.1009591 |
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