  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.
