Jupyter notebook for “Chemogenetic Activation of Hippocampal Neurons Enhances Nest Selection in Neonatal Rats

This project shows how to build an automated system for tracking behavior and analyzing space using deep‑learning computer vision. It uses the open‑source DeepLabCut framework to follow user‑chosen body points in videos of newborn rodents, so no physical markers are needed. The positions of each point are saved as trajectories and then processed in a Jupyter notebook to compute movement metrics such as speed, distance traveled, and body orientation. To measure preferences, the program draws the trajectories on the video frames and marks several regions of interest (ROIs). It then adds up the time the animal spends in each ROI, giving an automatic readout of nest‑selection behavior. The framework includes

step‑by‑step instructions for setting up the deep‑learning model, handling the coordinate CSV files, and running the spatial analysis. Automating the scoring cuts down on manual work and reduces human bias in experiments. Electrical and electronics engineering students can use this as a hands‑on guide for combining deep learning, computer vision, and bio‑signal analysis in automated labs.

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