📝 Description

This project focuses on the automatic classification of animal behavior using multi-agent trajectory data. The dataset, the Caltech Mouse Social Interactions (CalMS21), tracks two mice interacting in the same cage, capturing their movements and social behaviors. The task involves classifying their behavior over time, transforming a multivariate time series of trajectories into a sequence of categorical labels (i.e., a seq-to-seq problem with continuous input and categorical output).

The student will work with the CalMS21 dataset, which includes trajectory data from videos of freely behaving mice in a resident-intruder assay. The goal is to develop a spatio-temporal model using frameworks like Torch Spatiotemporal to automatically classify behaviors, reducing the need for manual labeling by humans. This approach is both innovative and highly relevant, as manual labeling is currently a time-consuming process requiring significant human effort.

By the end of the project, the student will gain experience in handling multi-agent trajectory data, designing spatio-temporal models, and applying them to real-world behavior classification tasks. The project has broad implications for behavioral neuroscience and related fields, offering a more efficient and scalable solution for analyzing animal interactions.

Data: The Caltech Mouse Social Interactions (CalMS21) Dataset, consisting of trajectory data from videos of interacting mice.

📨 Contact:

Filippo Maria Bianchi filippo.m.bianchi@uit.no