PUBLICATION
An assistive computer vision tool to automatically detect changes in fish behavior in response to ambient odor
- Authors
- Banerjee, S., Alvey, L., Brown, P., Yue, S., Li, L., Scheirer, W.J.
- ID
- ZDB-PUB-210115-2
- Date
- 2021
- Source
- Scientific Reports 11: 1002 (Journal)
- Registered Authors
- Li, Lei
- Keywords
- none
- MeSH Terms
-
- Algorithms
- Animals
- Behavior, Animal/physiology*
- Computers
- Female
- Male
- Neurons/physiology
- Odorants
- Olfactory Bulb/physiology*
- Retina/physiology
- Zebrafish/physiology*
- PubMed
- 33441714 Full text @ Sci. Rep.
Citation
Banerjee, S., Alvey, L., Brown, P., Yue, S., Li, L., Scheirer, W.J. (2021) An assistive computer vision tool to automatically detect changes in fish behavior in response to ambient odor. Scientific Reports. 11:1002.
Abstract
The analysis of fish behavior in response to odor stimulation is a crucial component of the general study of cross-modal sensory integration in vertebrates. In zebrafish, the centrifugal pathway runs between the olfactory bulb and the neural retina, originating at the terminalis neuron in the olfactory bulb. Any changes in the ambient odor of a fish's environment warrant a change in visual sensitivity and can trigger mating-like behavior in males due to increased GnRH signaling in the terminalis neuron. Behavioral experiments to study this phenomenon are commonly conducted in a controlled environment where a video of the fish is recorded over time before and after the application of chemicals to the water. Given the subtleties of behavioral change, trained biologists are currently required to annotate such videos as part of a study. This process of manually analyzing the videos is time-consuming, requires multiple experts to avoid human error/bias and cannot be easily crowdsourced on the Internet. Machine learning algorithms from computer vision, on the other hand, have proven to be effective for video annotation tasks because they are fast, accurate, and, if designed properly, can be less biased than humans. In this work, we propose to automate the entire process of analyzing videos of behavior changes in zebrafish by using tools from computer vision, relying on minimal expert supervision. The overall objective of this work is to create a generalized tool to predict animal behaviors from videos using state-of-the-art deep learning models, with the dual goal of advancing understanding in biology and engineering a more robust and powerful artificial information processing system for biologists.
Genes / Markers
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping