snn

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

Installing

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 . 

Run example

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.

Data preprocessing

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.

Layered SNNs

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