Backpropagation with Biologically Plausible Spatio-Temporal Adjustment For Training Deep Spiking Neural Networks

This repository contains code from our paper [Backpropagation with Biologically Plausible Spatio-Temporal Adjustment For Training Deep Spiking Neural Networks] published in Cell Patterns. https://www.cell.com/patterns/fulltext/S2666-3899(22)00119-2. If you use our code or refer to this project, please cite this paper.

Requirments

• numpy
• scipy
• pytorch >= 1.7.0
• torchvision

Data preparation

First modify the DATA_DIR='path/to/datasets in code/datasets/__init__.py to the root directory of your datasets. Neuromorphic datasets NMNIST, DVS-Gesture and DVS-CIFAR10 need to be manually downloaded and placed under the /path/to/datasets/DVS/*

/path/to/datasets/
  DVS/
    DVS_Cifar10/
    DVS_Gesture/
    NMNIST/

Train

Run training scripts corresponding to different datasets.

For example, training and validating the proposed method on the MNIST dataset:

bash ./train_dvsg.sh