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Emergent behaviour and neural dynamics in artificial agents tracking odour plumes
Tracking an odour plume to locate its source under variable wind and plume statistics is a complex task. Flying insects routinely accomplish such tracking, often over long distances, in pursuit of food or mates. Several aspects of this remarkable behaviour and its underlying neural circuitry have be...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10601839/ https://www.ncbi.nlm.nih.gov/pubmed/37886259 http://dx.doi.org/10.1038/s42256-022-00599-w |
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author | Singh, Satpreet H. van Breugel, Floris Rao, Rajesh P. N. Brunton, Bingni W. |
author_facet | Singh, Satpreet H. van Breugel, Floris Rao, Rajesh P. N. Brunton, Bingni W. |
author_sort | Singh, Satpreet H. |
collection | PubMed |
description | Tracking an odour plume to locate its source under variable wind and plume statistics is a complex task. Flying insects routinely accomplish such tracking, often over long distances, in pursuit of food or mates. Several aspects of this remarkable behaviour and its underlying neural circuitry have been studied experimentally. Here we take a complementary in silico approach to develop an integrated understanding of their behaviour and neural computations. Specifically, we train artificial recurrent neural network agents using deep reinforcement learning to locate the source of simulated odour plumes that mimic features of plumes in a turbulent flow. Interestingly, the agents’ emergent behaviours resemble those of flying insects, and the recurrent neural networks learn to compute task-relevant variables with distinct dynamic structures in population activity. Our analyses put forward a testable behavioural hypothesis for tracking plumes in changing wind direction, and we provide key intuitions for memory requirements and neural dynamics in odour plume tracking. |
format | Online Article Text |
id | pubmed-10601839 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
record_format | MEDLINE/PubMed |
spelling | pubmed-106018392023-10-26 Emergent behaviour and neural dynamics in artificial agents tracking odour plumes Singh, Satpreet H. van Breugel, Floris Rao, Rajesh P. N. Brunton, Bingni W. Nat Mach Intell Article Tracking an odour plume to locate its source under variable wind and plume statistics is a complex task. Flying insects routinely accomplish such tracking, often over long distances, in pursuit of food or mates. Several aspects of this remarkable behaviour and its underlying neural circuitry have been studied experimentally. Here we take a complementary in silico approach to develop an integrated understanding of their behaviour and neural computations. Specifically, we train artificial recurrent neural network agents using deep reinforcement learning to locate the source of simulated odour plumes that mimic features of plumes in a turbulent flow. Interestingly, the agents’ emergent behaviours resemble those of flying insects, and the recurrent neural networks learn to compute task-relevant variables with distinct dynamic structures in population activity. Our analyses put forward a testable behavioural hypothesis for tracking plumes in changing wind direction, and we provide key intuitions for memory requirements and neural dynamics in odour plume tracking. 2023-01 2023-01-25 /pmc/articles/PMC10601839/ /pubmed/37886259 http://dx.doi.org/10.1038/s42256-022-00599-w Text en https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . Reprints and permissions information is available at www.nature.com/reprints (http://www.nature.com/reprints) . |
spellingShingle | Article Singh, Satpreet H. van Breugel, Floris Rao, Rajesh P. N. Brunton, Bingni W. Emergent behaviour and neural dynamics in artificial agents tracking odour plumes |
title | Emergent behaviour and neural dynamics in artificial agents tracking odour plumes |
title_full | Emergent behaviour and neural dynamics in artificial agents tracking odour plumes |
title_fullStr | Emergent behaviour and neural dynamics in artificial agents tracking odour plumes |
title_full_unstemmed | Emergent behaviour and neural dynamics in artificial agents tracking odour plumes |
title_short | Emergent behaviour and neural dynamics in artificial agents tracking odour plumes |
title_sort | emergent behaviour and neural dynamics in artificial agents tracking odour plumes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10601839/ https://www.ncbi.nlm.nih.gov/pubmed/37886259 http://dx.doi.org/10.1038/s42256-022-00599-w |
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