Self-supervised Representation Learning for Neuronal Morphologies

This repository contains code to the paper Self-Supervised Graph Representation Learning for Neuronal Morphologies by M.A. Weis, L. Hansel, T. Lüddecke and A.S. Ecker (2023).

System Requirements

Hardware requirements

The training of GraphDINO requires a GPU. All trainings for the publication were performed on a NVIDIA Quadro RTX 5000 single GPU. Training on the neuronal BBP dataset ran for approximately 10 hours for 100,000 training iterations.

Software requirements

OS Requirements

The code was developed and tested on Linux (Ubuntu 16.04).

Python Dependencies

The Python Dependencies are specified in setup.py.

Installation

python3 setup.py install

Data

Extract data using the Allen Software Development Kit. See demo notebook on how to use the Allen Cell Types Database.

See extract_allen_data.ipynb for data preprocessing.

See Data README for instructions on how to use GraphDINO with your custom dataset.

Training

Start training GraphDINO from scratch on ABA dataset:

python3 ssl_neuron/main.py --config=ssl_neuron/configs/config.json

The training code will write checkpoint files of the model weights to the checkpoint directory specified in the config file.

Demos

For examples on how to load the data, train the model and perform inference with a pretrained model, see Jupyter notebooks in the demos folder.

Citation

If you use this repository in your research, please cite:

@article{Weis2023,
      title={Self-Supervised Graph Representation Learning for Neuronal Morphologies},
      author={Marissa A. Weis and Laura Hansel and Timo L{\"u}ddecke and Alexander S. Ecker},
      journal={Transactions on Machine Learning Research},
      issn={2835-8856},
      year={2023}
}

License

This project is covered under the MIT License.