This is the official implementation of Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings by Ilyass Hammouamri, Ismail Khalfaoui Hassani and Timothée Masquelier.
Please use Python 3.9 and above python>=3.9
Install SpikingJelly using:
git clone https://github.com/fangwei123456/spikingjelly.git
cd spikingjelly
python setup.py install --user
Installing SpikingJelly using pip is not yet compatible with this repo.
Install the other dependencies from the requirements.txt file using:
pip install -r requirements.txt
The first thing to do after installing all the dependencies is to specify the datasets_path in config.py. Simply create an empty data directory, preferably with two subdirectories, one for SHD and the other SSC. The datasets_path should correspond to these subdirectories.
The datasets will then be downloaded and preprocessed automatically. For example:
cd SNN-delays
mkdir -p Datasets/SHD
mkdir -p Datasets/SSC
To train a new model as defined by the config.py simply use:
python main.py
The loss and accuracy for the training and validation at every epoch will be printed to stdout and the best model will be saved to the current directory.
If the use_wandb parameter is set to True, a more detailed log will be available at the wandb project specified in the configuration.
If you use DCLS-Delays in your work, please consider to cite it as follows:
@inproceedings{
hammouamri2024learning,
title={Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings},
author={Ilyass Hammouamri and Ismail Khalfaoui-Hassani and Timoth{\'e}e Masquelier},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=4r2ybzJnmN}
}