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Related Experiment Video

Updated: Jun 27, 2025

Assessment of Social Interaction Behaviors

Assessment of Social Interaction Behaviors

Published on: February 25, 2011

The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions.

Jennifer J Sun¹, Tomomi Karigo¹, Dipam Chakraborty²

¹Caltech.

Advances in Neural Information Processing Systems

|May 6, 2024

Summary

This summary is machine-generated.

This study introduces the Caltech Mouse Social Interactions (CalMS21) Dataset, a novel resource for multi-agent behavior modeling. It enables advanced analysis of mouse social interactions and automated behavior classification.

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Last Updated: Jun 27, 2025

Assessment of Social Interaction Behaviors

Assessment of Social Interaction Behaviors

Published on: February 25, 2011

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

Experimental Assessment of Mouse Sociability Using an Automated Image Processing Approach

Experimental Assessment of Mouse Sociability Using an Automated Image Processing Approach

Published on: May 15, 2016

Area of Science:

Behavioral Neuroscience
Computational Biology
Machine Learning

Background:

Understanding complex interactions between agents is crucial in behavioral neuroscience.
Automated analysis of animal behavior is essential for accelerating research.
Existing datasets may lack the scale or diversity needed for robust model training.

Purpose of the Study:

Introduce the Caltech Mouse Social Interactions (CalMS21) Dataset for multi-agent behavior modeling.
Provide benchmarks for evaluating automated behavior classification methods.
Facilitate research on inter-annotator differences and learning new behaviors.

Main Methods:

Collected trajectory data from freely behaving mice in a resident-intruder assay.
Recorded 6 million frames of unlabeled tracked poses and over 1 million frames with annotations.
Developed benchmarks for training, style transfer, and few-shot learning of behaviors.

Main Results:

The CalMS21 dataset offers a large-scale resource for multi-agent behavior analysis.
Benchmarks address challenges in classifying behaviors from labeled and unlabeled data.
The dataset facilitates generalization to new behavioral settings and annotator styles.

Conclusions:

The CalMS21 dataset is a valuable tool for advancing automated behavior classification in neuroscience.
It supports the development of more robust and generalizable multi-agent behavior models.
This resource will accelerate discovery in social behavior research.