用卷积网络分析小鼠停顿行为，区分年龄性别，女性更易识别。
Gaining Explainability from a CNN for Stereotype Detection Based on Mice Stopping Behavior
- 用2D直方图堆叠输入浅层CNN，从停顿位置识别小鼠年龄性别。
- 雌性小鼠识别准确率超90%，雄性仅62.5%。
- 通过激活图揭示雌性偏好特定区域，适合动物行为研究者。
理解实验动物行为是揭示人类疾病与神经发育障碍的关键。本文关注小鼠停顿行为，因其与探索、摄食和睡眠习惯相关。我们使用LiveMouseTracker（LMT）系统追踪4只小鼠3天，构建每只小鼠停顿位置的2D直方图堆叠，并输入浅层卷积神经网络（CNN）以分类其年龄与性别。结果显示，雌性小鼠行为模式更显著，分类准确率超过90%；雄性则表现较弱，准确率为62.5%。进一步分析卷积层激活函数发现，雌性偏好特定笼区活动；幼年雄性行为介于幼年雌性和成年雄性之间。该方法可为动物行为建模提供可解释性支持。
原文摘要 · Abstract (English)
Understanding the behavior of laboratory animals is a key to find answers about diseases and neurodevelopmental disorders that also affects humans. One behavior of interest is the stopping, as it correlates with exploration, feeding and sleeping habits of individuals. To improve comprehension of animal's behavior, we focus on identifying trait revealing age/sex of mice through the series of stopping spots of each individual. We track 4 mice using LiveMouseTracker (LMT) system during 3 days. Then, we build a stack of 2D histograms of the stop positions. This stack of histograms passes through a shallow CNN architecture to classify mice in terms of age and sex. We observe that female mice show more recognizable behavioral patterns, reaching a classification accuracy of more than 90%, while males, which do not present as many distinguishable patterns, reach an accuracy of 62.5%. To gain explainability from the model, we look at the activation function of the convolutional layers and found that some regions of the cage are preferentially explored by females. Males, especially juveniles, present behavior patterns that oscillate between juvenile female and adult male.
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