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An Assistive Computer Vision Tool to Automatically Detect Changes in Fish Behavior In Response to Ambient Odor

View ORCID ProfileSreya Banerjee, Lauren Alvey, Paula Brown, Sophie Yue, Lei Li, Walter J. Scheirer
doi: https://doi.org/10.1101/2020.09.01.277657
Sreya Banerjee
1Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, Indiana, USA
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  • For correspondence: sbanerj2@nd.edu
Lauren Alvey
2Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, USA
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Paula Brown
2Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, USA
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Sophie Yue
2Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, USA
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Lei Li
2Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, USA
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Walter J. Scheirer
1Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, Indiana, USA
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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 warrants 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.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • The entire manuscript has been updated after a minor revision

  • https://github.com/sbanerj2/zebrafish_behavior

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted December 15, 2020.
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An Assistive Computer Vision Tool to Automatically Detect Changes in Fish Behavior In Response to Ambient Odor
Sreya Banerjee, Lauren Alvey, Paula Brown, Sophie Yue, Lei Li, Walter J. Scheirer
bioRxiv 2020.09.01.277657; doi: https://doi.org/10.1101/2020.09.01.277657
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An Assistive Computer Vision Tool to Automatically Detect Changes in Fish Behavior In Response to Ambient Odor
Sreya Banerjee, Lauren Alvey, Paula Brown, Sophie Yue, Lei Li, Walter J. Scheirer
bioRxiv 2020.09.01.277657; doi: https://doi.org/10.1101/2020.09.01.277657

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