Spatio-temporal BP for spiking neural networks.

Matlab version of convolutional SNN on MNSIT[1].

Please find another branch for Pytorch version on CIFAR10[2].

For neurmorphic dataset(N-MNIST and DVS-Gesture), please refer to examples of our another projects[3]:

https://github.com/hewh16/SNNs-RNNs

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).
3. He W, Wu Y J, Deng L, et al. Comparing SNNs and RNNs on neuromorphic vision datasets: Similarities and differences[J]. Neural Networks, 2020, 132: 108-120.