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Pytorch implementation of TSSL-BP rule for Deep Spiking Neural Networks.

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[ML Reproducibility Challenge:] Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks (TSSL-BP)

This repository is for the reproduction of Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks.

Requirements

Dependencies and Libraries

  • python 3.7
  • pytorch
  • torchvision

Installation

To install requirements:

pip install -r requirements.txt

Training

Before running

Modify the data path and network settings in the config files.

Select the index of GPU in the main.py (0 by default)

Run the code

$ python main.py -config Networks/config_file.yaml
$ python main.py -config Networks/config_file.yaml -checkpoint checkpoint/ckpt.pth // load the checkpoint

Results

Performance comparison between original paper and this reproduction:

MNIST

Paper Network Size Time Steps Epochs Mean Stddev Best
Original paper 15C5-P2-40C5-P2-300 5 100 99.50% 0.02% 99.53%
Reproduction 15C5-P2-40C5-P2-300 5 100 99.40% 0.04% 99.47%

CIFAR 10- CNN1

Paper Network Size Time Steps Epochs Mean Stddev Best
Original paper 96C3-256C3-P2-384C3-P2-384C3-256C3-1024-1024 5 150 88.98% 0.27% 89.22%
Reproduction 96C3-256C3-P2-384C3-P2-384C3-256C3-1024-1024 5 150 88.96% 0.10% 89.07%

CIFAR 10- CNN2

Paper Network Size Time Steps Epochs Mean Stddev Best
Original paper 128C3-256C3-P2-512C3-P2-1024C3-512C3-1024-512 5 150 N/A N/A 91.41%
Reproduction 128C3-256C3-P2-512C3-P2-1024C3-512C3-1024-512 5 150 N/A N/A 89.61%

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Pytorch implementation of TSSL-BP rule for Deep Spiking Neural Networks.

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