Cross-species behavior analysis with attention-based domain-adversarial deep neural networks

Comparing changes in behaviour across various species is not always trivial, especial across significantly divergent species. Here, the authors develop a deep learning framework that allows them to map changes in locomotion demonstrated on dopamine-deficient humans, mice and worms.

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Autores principales:	Takuya Maekawa, Daiki Higashide, Takahiro Hara, Kentarou Matsumura, Kaoru Ide, Takahisa Miyatake, Koutarou D. Kimura, Susumu Takahashi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/911657e57a84400da94bf27d5bd462da
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spelling	oai:doaj.org-article:911657e57a84400da94bf27d5bd462da2021-12-02T17:23:40ZCross-species behavior analysis with attention-based domain-adversarial deep neural networks10.1038/s41467-021-25636-x2041-1723https://doaj.org/article/911657e57a84400da94bf27d5bd462da2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-25636-xhttps://doaj.org/toc/2041-1723Comparing changes in behaviour across various species is not always trivial, especial across significantly divergent species. Here, the authors develop a deep learning framework that allows them to map changes in locomotion demonstrated on dopamine-deficient humans, mice and worms.Takuya MaekawaDaiki HigashideTakahiro HaraKentarou MatsumuraKaoru IdeTakahisa MiyatakeKoutarou D. KimuraSusumu TakahashiNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Takuya Maekawa Daiki Higashide Takahiro Hara Kentarou Matsumura Kaoru Ide Takahisa Miyatake Koutarou D. Kimura Susumu Takahashi Cross-species behavior analysis with attention-based domain-adversarial deep neural networks
description	Comparing changes in behaviour across various species is not always trivial, especial across significantly divergent species. Here, the authors develop a deep learning framework that allows them to map changes in locomotion demonstrated on dopamine-deficient humans, mice and worms.
format	article
author	Takuya Maekawa Daiki Higashide Takahiro Hara Kentarou Matsumura Kaoru Ide Takahisa Miyatake Koutarou D. Kimura Susumu Takahashi
author_facet	Takuya Maekawa Daiki Higashide Takahiro Hara Kentarou Matsumura Kaoru Ide Takahisa Miyatake Koutarou D. Kimura Susumu Takahashi
author_sort	Takuya Maekawa
title	Cross-species behavior analysis with attention-based domain-adversarial deep neural networks
title_short	Cross-species behavior analysis with attention-based domain-adversarial deep neural networks
title_full	Cross-species behavior analysis with attention-based domain-adversarial deep neural networks
title_fullStr	Cross-species behavior analysis with attention-based domain-adversarial deep neural networks
title_full_unstemmed	Cross-species behavior analysis with attention-based domain-adversarial deep neural networks
title_sort	cross-species behavior analysis with attention-based domain-adversarial deep neural networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/911657e57a84400da94bf27d5bd462da
work_keys_str_mv	AT takuyamaekawa crossspeciesbehavioranalysiswithattentionbaseddomainadversarialdeepneuralnetworks AT daikihigashide crossspeciesbehavioranalysiswithattentionbaseddomainadversarialdeepneuralnetworks AT takahirohara crossspeciesbehavioranalysiswithattentionbaseddomainadversarialdeepneuralnetworks AT kentaroumatsumura crossspeciesbehavioranalysiswithattentionbaseddomainadversarialdeepneuralnetworks AT kaoruide crossspeciesbehavioranalysiswithattentionbaseddomainadversarialdeepneuralnetworks AT takahisamiyatake crossspeciesbehavioranalysiswithattentionbaseddomainadversarialdeepneuralnetworks AT koutaroudkimura crossspeciesbehavioranalysiswithattentionbaseddomainadversarialdeepneuralnetworks AT susumutakahashi crossspeciesbehavioranalysiswithattentionbaseddomainadversarialdeepneuralnetworks
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Cross-species behavior analysis with attention-based domain-adversarial deep neural networks

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