SSTDP: Supervised Spike Timing Dependent Plasticity for Efficient Spiking Neural Network Training

pytorch implementation of SSTDP for Efficient Spiking Neural Network Training

Overview

Description

• sstdp_module (/code/sstdp_module.py)

  • SpikeOnceNeuron: The neuron that will only spike once for each sample. Such neuron reduces the spike density while maintain spike information
  • stdp_update: The STDP update rule that direct the stdp update with gradient.
  • stdp_linear_container: The linear spiking neuron layer container.
  • StdpLinear: The callable linear model.
  • stdp_conv2d_container: The 2d convolution spiking neuron layer container.
  • StdpConv2d: The callable 2d convolution model.

• sstdp_train (/code/sstdp_train.py) run single, with parameters

  python sstdp_train.py --threshold 100 --result_dir test_train/ --weight_decay 1e-5 --learning_rate 10

Citation

We now have a paper, titled "SSTDP: Supervised Spike Timing Dependent Plasticity for Efficient Spiking Neural Network Training", which is published in Frontiers in Neuroscience, Section Neuromorphic Engineering.

@article{liu2021sstdp,
  title={SSTDP: Supervised Spike Timing Dependent Plasticity for Efficient Spiking Neural Network Training},
  author={Liu, Fangxin and Zhao, Wenbo and Chen, Yongbiao and Wang, Zongwu and Yang, Tao and Li, JIANG},
  journal={Frontiers in neuroscience},
  year={2021},
  pages={1413},
  publisher={Frontiers}
}

To-do

• Coming soon: Updated Code.