Zebrafish behavior feature recognition using three-dimensional tracking and machine learning

Abstract In this work, we aim to construct a new behavior analysis method by using machine learning. We used two cameras to capture three-dimensional (3D) tracking data of zebrafish, which were analyzed using fuzzy adaptive resonance theory (FuzzyART), a type of machine learning algorithm, to identi...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Peng Yang, Hiro Takahashi, Masataka Murase, Motoyuki Itoh
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ea991d4753e54cbc961e4218a969ae1d
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:ea991d4753e54cbc961e4218a969ae1d
record_format	dspace
spelling	oai:doaj.org-article:ea991d4753e54cbc961e4218a969ae1d2021-12-02T18:18:59ZZebrafish behavior feature recognition using three-dimensional tracking and machine learning10.1038/s41598-021-92854-02045-2322https://doaj.org/article/ea991d4753e54cbc961e4218a969ae1d2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92854-0https://doaj.org/toc/2045-2322Abstract In this work, we aim to construct a new behavior analysis method by using machine learning. We used two cameras to capture three-dimensional (3D) tracking data of zebrafish, which were analyzed using fuzzy adaptive resonance theory (FuzzyART), a type of machine learning algorithm, to identify specific behavioral features. The method was tested based on an experiment in which electric shocks were delivered to zebrafish and zebrafish swimming was tracked in 3D simultaneously to find electric shock-associated behaviors. By processing the obtained data with FuzzyART, we discovered that distinguishing behaviors were statistically linked to the electric shock based on the machine learning algorithm. Moreover, our system could accept user-supplied data for detection and quantitative analysis of the behavior features, such as the behavior features defined by the 3D tracking analysis above. This system could be applied to discover new distinct behavior features in mutant zebrafish and used for drug administration screening and cognitive ability tests of zebrafish in the future.Peng YangHiro TakahashiMasataka MuraseMotoyuki ItohNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Peng Yang Hiro Takahashi Masataka Murase Motoyuki Itoh Zebrafish behavior feature recognition using three-dimensional tracking and machine learning
description	Abstract In this work, we aim to construct a new behavior analysis method by using machine learning. We used two cameras to capture three-dimensional (3D) tracking data of zebrafish, which were analyzed using fuzzy adaptive resonance theory (FuzzyART), a type of machine learning algorithm, to identify specific behavioral features. The method was tested based on an experiment in which electric shocks were delivered to zebrafish and zebrafish swimming was tracked in 3D simultaneously to find electric shock-associated behaviors. By processing the obtained data with FuzzyART, we discovered that distinguishing behaviors were statistically linked to the electric shock based on the machine learning algorithm. Moreover, our system could accept user-supplied data for detection and quantitative analysis of the behavior features, such as the behavior features defined by the 3D tracking analysis above. This system could be applied to discover new distinct behavior features in mutant zebrafish and used for drug administration screening and cognitive ability tests of zebrafish in the future.
format	article
author	Peng Yang Hiro Takahashi Masataka Murase Motoyuki Itoh
author_facet	Peng Yang Hiro Takahashi Masataka Murase Motoyuki Itoh
author_sort	Peng Yang
title	Zebrafish behavior feature recognition using three-dimensional tracking and machine learning
title_short	Zebrafish behavior feature recognition using three-dimensional tracking and machine learning
title_full	Zebrafish behavior feature recognition using three-dimensional tracking and machine learning
title_fullStr	Zebrafish behavior feature recognition using three-dimensional tracking and machine learning
title_full_unstemmed	Zebrafish behavior feature recognition using three-dimensional tracking and machine learning
title_sort	zebrafish behavior feature recognition using three-dimensional tracking and machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ea991d4753e54cbc961e4218a969ae1d
work_keys_str_mv	AT pengyang zebrafishbehaviorfeaturerecognitionusingthreedimensionaltrackingandmachinelearning AT hirotakahashi zebrafishbehaviorfeaturerecognitionusingthreedimensionaltrackingandmachinelearning AT masatakamurase zebrafishbehaviorfeaturerecognitionusingthreedimensionaltrackingandmachinelearning AT motoyukiitoh zebrafishbehaviorfeaturerecognitionusingthreedimensionaltrackingandmachinelearning
_version_	1718378189158350848

Zebrafish behavior feature recognition using three-dimensional tracking and machine learning

Ejemplares similares