Spatio-temporal BP for spiking neural networks.

Matlab version of convolutional SNN on MNSIT.

Please find another branch for Pytorch version on CIFAR10 and N-MNIST (update later).

Requirement

• Python 3.6
• MNIST dataset
• CIFAR10 dataset
• N_MSNIT dataset

Results

After 100 epochs, it can obtain ~ 99.4% acc on MNIST.

Reference

1. Wu, Yujie, Lei Deng, Guoqi Li, Jun Zhu, and Luping Shi. "Direct Training for Spiking Neural Networks: Faster, Larger, Better." arXiv preprint arXiv:1809.05793 (2018).
2. Wu, Yujie, Lei Deng, Guoqi Li, Jun Zhu, and Luping Shi. "Spatio-temporal backpropagation for training high-performance spiking neural networks." Frontiers in neuroscience 12 (2018).