This is the public version of NORAD. NORAD is an innovative, open-source platform designed for the analysis of rodent behavior, particularly in the context of novel object recognition memory (NOR). Developed with deep learning technology, NORAD aims to address the limitations of traditional NOR tests by offering a high-throughput, unbiased, and customizable solution for behavioral analysis.
- Customizable Analysis: Tailor NOR parameters to fit specific research needs.
- High Throughput: Automates data analysis for efficient memory performance quantification.
- Unbiased Quantification: Machine learning minimizes observer bias, ensuring reliable results.
- Novel Parameters Extraction: Identifies unique behavioral patterns and performance metrics.
- Clone the repository to your local machine.
- Install the required dependencies listed in requirements.txt.
- Run the Main.py file; a GUI will open. Follow the on-screen instructions.
We welcome contributions from the community. Please raise an issue in the GitHub repository to let us know you want to contribute.
For support, raise an issue in the GitHub repository.
Developed by Raman Abbaspour and Steven A. Connor at York University, NORAD was supported by the Canada Research Chairs Program and the NSERC.
NORAD is released under the MIT License. See LICENSE for more details.
If you use NORAD in your research, please cite our paper: Abbaspour, R., & Connor, S.A. (2023). NORAD: An AI-powered solution for behavioral analysis of rodent recognition memory. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4415237