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.

翻译：理解实验动物的行为是探寻同样影响人类的疾病与神经发育障碍答案的关键。其中，停止行为因其与个体的探索、进食及睡眠习惯相关而备受关注。为增进对动物行为的理解，本研究聚焦于通过每个个体的连续停止位置序列来识别揭示小鼠年龄/性别的特征。我们使用LiveMouseTracker（LMT）系统对4只小鼠进行了为期3天的追踪，随后构建了停止位置的二维直方图堆栈。该直方图堆栈通过一个浅层CNN架构对小鼠进行年龄与性别分类。研究发现，雌性小鼠表现出更具辨识度的行为模式，分类准确率超过90%；而雄性小鼠因缺乏足够可区分的模式，准确率为62.5%。为从模型中获得可解释性，我们分析了卷积层的激活函数，发现笼舍的某些区域更受雌性偏好探索。雄性（尤其是幼年个体）则表现出在幼年雌性与成年雄性之间摇摆的行为模式。