Understanding object recognition patterns in mice is crucial for advancing behavioral neuroscience and has significant implications for human health, particularly in the realm of Alzheimer's research. This study is centered on the development, application, and evaluation of a state-of-the-art computational pipeline designed to analyze such behaviors, specifically focusing on Novel Object Recognition (NOR) and Spontaneous Location Recognition (SLR) tasks. The pipeline integrates three advanced computational models: Any-Maze for initial data collection, DeepLabCut for detailed pose estimation, and Convolutional Neural Networks (CNNs) for nuanced behavioral classification. Employed across four distinct mouse groups, this pipeline demonstrated high levels of accuracy and robustness. Despite certain challenges like video quality limitations and the need for manual calculations, the results affirm the pipeline's efficacy and potential for scalability. The study serves as a proof of concept for a multidimensional computational approach to behavioral neuroscience, emphasizing the pipeline's versatility and readiness for future, more complex analyses.

翻译：理解小鼠的物体识别模式对推进行为神经科学至关重要，并对人类健康（尤其是阿尔茨海默病研究领域）具有重要意义。本研究聚焦于开发、应用和评估一套用于分析此类行为的先进计算流程，特别关注新物体识别（NOR）和自发位置识别（SLR）任务。该流程整合了三种先进计算模型：用于初始数据采集的Any-Maze、用于详细姿态估计的DeepLabCut以及用于细微行为分类的卷积神经网络（CNN）。在四种不同小鼠群体中的应用表明，该流程具有较高的准确性和鲁棒性。尽管存在视频质量限制和需要手动计算等挑战，结果证实了该流程的有效性及其可扩展性潜力。本研究为行为神经科学的多维计算方法提供了概念验证，强调了该流程的通用性及其在未来更复杂分析中的就绪状态。