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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
PubMed
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.

Keywords:
artificial neural networksautomated behavioral analysisexploratory behaviorlearning and memorymice

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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.