First-Spike Coding of SNNs

Siying Liu (siying.liu20@imperial.ac.uk), Vincent C. H. Leung, and Pier Luigi Dragotti.

This repository is the official PyTorch implementation of the paper First-spike coding promotes accurate and efficient spiking neural networks for discrete events with rich temporal structures.

Implementation

There are two implementation versions of SNNs, based on SuperSpike and SpikingJelly under folders named superspike and spkjelly, respectively. Please install spikingjelly==0.0.0.0.14 before use.

In superspike, the backpropagation of neurons is implemented by PyTorch Autograd, training speed is relatively slow. Only Current-based LIF (CuLIF) neuron with fixed or trainable time constants are implemented.

In spkjelly, the FP and BP of neurons are accelerated when using backend='cupy' under step_mode='m'. We implemented the cupy backend for three types of spiking neurons, including CuLIF, Parametric CuLIF (PCuLIF) and Adaptive LIF (AdLIF) neurons. We recommend using this implementation, since the training is much faster and models with AdLIF for SHD and NTIDIGITS achieve the best performance.

Datasets

Four datasets are trained and tested, including audio datasets SHD, N-TIDIGITIS and visual datasets DVSGesture, DVSPlane. These data are transformed into .hdf5 format, which can be downloaded from here. Put them in the datasets folder.

Training

Run bash run.sh to train. Parameter settings can be found in .yaml under configs. Parameters can also be specified by args. Please cd superspike or cd spkjelly before run bash run.sh.

An example of training.

cd spikingjelly
path_to_yaml='../configs/adlif_shd.yaml'
python train.py \
--path_to_yaml $path_to_yaml \
--log-interval 5 \
--lr 1e-3 \
--batch-size 128 \
--test-batch-size 256 \
--epochs 80 \
--loss_mode 'first_time' \
--T 100 \
--T_empty 0 \
--dt 10 \
--hidden_size 256 256 \
--FS 0.2 16 200 \
--neuron1 5. 5. 0.5 \
--neuron2 60. 60. 10 \
--treg 0.01 0.02 \

Testing

Each model can be evaluated with FS/FR coding. Run bash run_test.sh, check the FR/FS accuracy, spike count, time delay, spike pattern, etc.

An example of testing.

python test.py \
--path_to_yaml $path_to_yaml \
--test-batch-size $test_batch_size \
--T $chunck_size \
--T_empty 0 \
--load_pt_file $load_pt_file \
--best_model \

Citation

If you use any of this code, please cite the publications as follows:

@ARTICLE{10.3389/fnins.2023.1266003,
  AUTHOR={Liu, Siying and Leung, Vincent C. H. and Dragotti, Pier Luigi},   
  TITLE={First-spike coding promotes accurate and efficient spiking neural networks for discrete events with rich temporal structures},      
  JOURNAL={Frontiers in Neuroscience},      
  VOLUME={17},           
  YEAR={2023},      
  URL={https://www.frontiersin.org/articles/10.3389/fnins.2023.1266003},       
  DOI={10.3389/fnins.2023.1266003},      
  ISSN={1662-453X},   
}