PUBLICATION
Zebrafish behavior feature recognition using three-dimensional tracking and machine learning
- Authors
- Yang, P., Takahashi, H., Murase, M., Itoh, M.
- ID
- ZDB-PUB-210701-4
- Date
- 2021
- Source
- Scientific Reports 11: 13492 (Journal)
- Registered Authors
- Itoh, Motoyuki
- Keywords
- none
- MeSH Terms
-
- Animals
- Behavior, Animal/physiology*
- Machine Learning*
- Video Recording*
- Zebrafish/physiology*
- PubMed
- 34188116 Full text @ Sci. Rep.
Citation
Yang, P., Takahashi, H., Murase, M., Itoh, M. (2021) Zebrafish behavior feature recognition using three-dimensional tracking and machine learning. Scientific Reports. 11:13492.
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.
Genes / Markers
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping