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Updated: Jan 16, 2026

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Low-Cost 3D-Printed Mazes with Open-Source ML Tracking for Mouse Behavior.

James D O'Leary1, Dhwani C Gondalia1, Molly O'Brien1

  • 1Laboratory for Brain Injury and Dementia, Department of Neuroscience, Georgetown University Medical Centre, Washington, DC 20057.

Eneuro
|September 26, 2025
PubMed
Summary
This summary is machine-generated.

This study combines 3D printing and machine learning to create affordable behavioral neuroscience research tools. This innovative approach reduces costs for custom mouse mazes and automated behavior analysis, enhancing accessibility for scientists.

Keywords:
3D printingbehaviorlearning and memorymachine learningopen source

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Area of Science:

  • Behavioral Neuroscience
  • Computational Neuroscience
  • Biomedical Engineering

Background:

  • High costs of specialized equipment and software pose significant barriers in behavioral neuroscience research.
  • Limited funding restricts access to essential tools for early-career scientists and established laboratories.
  • Need for cost-effective and customizable solutions to advance research in cognition and anxiety-like behaviors.

Purpose of the Study:

  • To investigate the integration of 3D printing and machine learning for cost reduction in behavioral neuroscience.
  • To develop and validate affordable, custom-designed mouse behavioral assessment tools.
  • To implement machine learning for accurate and automated behavioral analysis.

Main Methods:

  • 3D printing was utilized to fabricate custom mouse T-mazes and elevated plus mazes.
  • Machine learning algorithms were integrated for automated tracking and behavioral analysis of male mice.
  • The efficacy and accuracy of custom-built mazes and analysis software were compared to commercial alternatives.

Main Results:

  • 3D-printed mazes demonstrated comparable efficacy to commercial options, offering enhanced affordability and customization.
  • Machine learning-based behavioral analysis achieved accuracy on par with commercial solutions and human scoring.
  • The combined approach significantly reduced both startup and operational costs for behavioral research.

Conclusions:

  • The integration of 3D printing and machine learning offers a cost-effective solution for behavioral neuroscience research.
  • This approach lowers financial barriers for new investigators and optimizes resource allocation for established labs.
  • The developed methodology expands research capabilities and promotes greater accessibility in the field.