Conv-SNN

Convolutional spiking neural networks (SNN) for spatio-temporal feature extraction

This paper highlights potentials of Convolutional spiking neural networks and introduces a new architecture to tackle training deep convolutional SNN problems.

Prerequisites

The Following Setup is tested and it is working:

• Python>=3.5
• Pytorch>=0.4.1
• Cuda>=9.0
• opencv>=3.4.2

Docker

• Set up the environment where all the programs can run
  • Run ./run.sh

Data preparation

• Download CIFAR10-DVS dataset
  • Extract the dataset under DVS-CIFAR10/dvs-cifar10 folder
  • Use test_dvs.m in matlab to convert events into matrix of t, x, y, p (make sure to adjust the test_dvs.m folder addresses inside the code)
  • Run python3 dvscifar_dataloader.py to prepare the dataset (make sure to have files like dvs-cifar10/airplane/0.mat inside main.py directory)

Training & Testing

• CIFAR10-DVS model

  • Run python3 main.py

• Spatio-temporal feature extraction tests

  • For each architecture simply run main file with python3

• Note: There are problems with training SNNs, such as extreme importance of initialization; Therefore, you may not reach the highest accuracy as mentioned in the paper. The solution is to try other torch versions and parameters or contact me / make an issue if you truly need the highest accuracy.

Citing

Please adequately refer to the papers any time this Work is being used. If you do publish a paper where this Work helped your research, Please cite the following papers in your publications.

@misc{samadzadeh2020convolutional,
        title={Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction},
        author={Ali Samadzadeh and Fatemeh Sadat Tabatabaei Far and Ali Javadi and Ahmad Nickabadi and Morteza Haghir Chehreghani},
        year={2020},
        eprint={2003.12346},
        archivePrefix={arXiv},
        primaryClass={cs.CV}
    }