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.

中文翻译：

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辅助计算机视觉工具，可根据环境气味自动检测鱼类行为的变化

鱼类对气味刺激行为的分析是脊椎动物跨模态感官整合研究的重要组成部分。在斑马鱼中，离心途径在嗅球和神经视网膜之间延伸，起源于嗅球的末端神经元。鱼类环境中任何环境气味的变化都可以保证视觉灵敏度的变化，并且由于末端神经元中GnRH信号的增加，可以触发雄性交配行为。研究这种现象的行为实验通常是在受控环境中进行的，在该环境中，在将化学药品应用于水之前和之后的一段时间内都会记录鱼的视频。考虑到行为改变的微妙之处，作为研究的一部分，目前需要训练有素的生物学家注释此类视频。手动分析视频的过程非常耗时，需要多个专家来避免人为错误/偏见，并且不能轻易地在Internet上众包。另一方面，事实证明，来自计算机视觉的机器学习算法对于视频注释任务是有效的，因为它们快速，准确，并且，如果设计得当，与人类相比，偏差较小。在这项工作中，我们建议通过使用计算机视觉工具，并依靠最少的专家监督，自动分析斑马鱼行为变化视频的整个过程。这项工作的总体目标是创建一个通用工具，以使用最新的深度学习模型从视频中预测动物的行为，