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RAINSTORM: Automated Analysis of Mouse Exploratory Behavior Using Artificial Neural Networks
Santiago D'hers1,2, Agustina Denise Robles1,2, Santiago Ojea Ramos1,2
1Departamento de Fisiología, Biología Molecular y Celular, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina.
Current Protocols
|July 23, 2025
Summary
RAINSTORM is a new AI tool that analyzes rodent exploratory behavior for cognitive function research. It streamlines data analysis, reducing bias and improving the efficiency of memory performance studies.
Area of Science:
- Neuroscience
- Cognitive Science
- Animal Behavior
Background:
- Rodent exploratory behavior is crucial for assessing cognitive function.
- Current analysis methods can be time-consuming and subjective.
Purpose of the Study:
- To introduce RAINSTORM, a versatile tool for streamlining rodent behavioral analysis.
- To enhance the reproducibility, scalability, and efficiency of cognitive research.
Main Methods:
- RAINSTORM integrates manual, geometric, and AI-powered behavioral labeling.
- It processes raw positional data from pose estimation software (e.g., DeepLabCut).
- The tool learns from experimenter labeling to reduce subjective bias.
Main Results:
- Automates identification of exploratory behaviors and provides insights into memory performance.
- Enables rapid analysis from raw data to exploration patterns.
- Accurately quantifies exploration times for novel and familiar objects.
Conclusions:
- RAINSTORM significantly enhances the reliability and efficiency of behavioral research.
- It is a robust methodology for assessing recognition memory in rodents.
- The software is applicable to various exploratory behaviors and experimental designs.
Keywords:
artificial neural networksautomated behavioral analysisexploratory behaviorlearning and memorymice
