A neural geometry approach comprehensively explains apparently conflicting models of visual perceptual learning

Yu-Ang Cheng, Mehdi Sanayei, Xing Chen, Ke Jia, Sheng Li, Takeo Watanabe, Alexander Thiele, & Ru-Yuan Zhang

About

This repo contains code for analyzing the changes of neural networks, human fMRI BOLD signals and macaque multi-unit acitivities from a geometric perspective.

Citation

This repository was released with the following pre-print. If you use this repository in your research, please cite as:

Cheng, Y. A., Sanayei, M., Chen, X., Jia, K., Li, S., Fang, F., ... & Zhang, R. Y. (2025). A neural geometry approach comprehensively explains apparently conflicting models of visual perceptual learning. Nature Human Behaviour, 1-18.

@article{cheng2025neural,
  title={A neural geometry approach comprehensively explains apparently conflicting models of visual perceptual learning},
  author={Cheng, Yu-Ang and Sanayei, Mehdi and Chen, Xing and Jia, Ke and Li, Sheng and Fang, Fang and Watanabe, Takeo and Thiele, Alexander and Zhang, Ru-Yuan},
  journal={Nature Human Behaviour},
  pages={1--18},
  year={2025},
  publisher={Nature Publishing Group UK London}
}

Authors

• Yu-Ang Cheng (https://github.com/Yu-AngCheng)

License

This project is licensed under the MIT License - see the LICENSE file for details