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

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Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks (TSSL-BP)

This repository is the official implementation 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

Datasets

NMNIST: dataset, preprocessing

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

Our proposed method achieves the following performance on :

MNIST

Network Size Time Steps Epochs Mean Stddev Best
15C5-P2-40C5-P2-300 5 100 99.50% 0.02% 99.53%

N-MNIST

Network Size Time Steps Epochs Mean Stddev Best
12C5-P2-64C5-P2 100 100 99.35% 0.03% 99.40%
12C5-P2-64C5-P2 30 100 99.23% 0.05% 99.28%

Fashion MNIST

Network Size Time Steps Epochs Mean Stddev Best
400 − 400 5 100 89.75% 0.03% 89.92%
32C5-P2-64C5-P2-1024 5 100 92.69% 0.09% 92.83%

CIFAR 10

Network Size Time Steps Epochs Mean Stddev Best
96C3-256C3-P2-384C3-P2-384C3-256C3-1024-1024 5 150 88.98% 0.27% 89.37%
128C3-256C3-P2-512C3-P2-1024C3-512C3-1024-512 5 150 N/A N/A 91.41%

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

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