Code for training probabilistic binary and WTA SNNs using PyTorch. Part of this code has been used for the following works:
N. Skatchkovsky, H. Jang, and O. Simeone, Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge Intelligence, accepted to IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020. https://arxiv.org/abs/1910.09594
H. Jang, N. Skatchkovsky, and O. Simeone, VOWEL: A Local Online Learning Rule for Recurrent Networks of Probabilistic Spiking Winner-Take-All Circuits, to be presented at ICPR 2020 https://arxiv.org/abs/2004.09416
N. Skatchkovsky, H. Jang, and O. Simeone, End-to-End Learning of Neuromorphic Wireless Systems for Low-Power Edge Artificial Intelligence, to be presented at Asilomar 2020 https://arxiv.org/abs/2009.01527
This code can now be installed as a package and is meant to be shared in pip. To clone and install locally the package, run
git clone https://github.com/kclip/snn
cd snn/
python -m pip install -e .
An experiment can be run on the MNIST-DVS dataset by launching
python snn/launch_experiment.py
You must first download and preprocess the MNIST-DVS dataset.
The data_preprocessing module will be deprecated in following versions and is only kept for compability reasons.
Please download our neurodata data preprocessing and loading package instead.
New in the latest version: an implementation of layered SNNs, to make better use of training on GPU and train (much) larger networks in native Pytorch. An example code is in snn.test_layered.py
Author: Nicolas Skatchkovsky