NeuPRINT

Official implementation of Neuronal Time-Invariant Representations (NeuPRINT).

Learning Time-Invariant Representations for Individual Neurons from Population Dynamics. NeurIPS 2023

Lu Mi*, Trung Le*, Tianxing He, Eli Shlizerman, Uygar Sümbül

Datasets

Our framework is evaluated on the datasets from Bugeon et al. 2022, Nature (A transcriptomic axis predicts state modulation of cortical interneurons), download dataset from this link.

Environment Setup

Assuming you have Python 3.8+ and Miniconda installed, run the following to set up the environment with necessary dependencies:

conda env create -f environment.yml

Run Experiments

python main.py --exp-tag neuprint_train

Citations

If you find our code helpful, please cite our paper:

@article{mi2024learning,
  title={Learning Time-Invariant Representations for Individual Neurons from Population Dynamics},
  author={Mi, Lu and Le, Trung and He, Tianxing and Shlizerman, Eli and S{\"u}mb{\"u}l, Uygar},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}

If you use the dataset, please cite this paper:

@article{bugeon2022transcriptomic,
  title={A transcriptomic axis predicts state modulation of cortical interneurons},
  author={Bugeon, Stephane and Duffield, Joshua and Dipoppa, Mario and Ritoux, Anne and Prankerd, Isabelle and Nicoloutsopoulos, Dimitris and Orme, David and Shinn, Maxwell and Peng, Han and Forrest, Hamish and others},
  journal={Nature},
  volume={607},
  number={7918},
  pages={330--338},
  year={2022},
  publisher={Nature Publishing Group UK London}
}