The single-spike block model

A new model for robust and accelerated training of single-spike spiking neural networks (SNNs) with competitive performance across various image and neuromorphic datasets. This is the accompanying repository to the paper Robust and accelerated single-spike spiking neural network training with applicability to challenging temporal tasks.

Installing dependencies

Install all required dependencies and activate the block environment using conda.

conda env create -f environment.yml
conda activate block

Getting started tutorial

See the notebooks/Tutorial.ipynb notebook for getting started with the block model.

Reproducing paper results

All the paper results can be reproduced using the scripts available in the scripts folder. Alternatively, all speedup benchmarks and pretrained models can be found under the releases.

Running benchmark experiments

The python run_benchmarks.py script will benchmark the time of the forward and backward passes of the block and standard single-spike SNN model for different numbers of neurons and simulation steps.

Training models

Ensure that the computer has a CUDA capable GPU with CUDA 11.0 installed.

1. Downloading and processing datasets

The Fashion-MNIST dataset will be downloaded by PyTorch by default. The content of the Neuromorphic-MNIST dataset can be downloaded and unzipped into the data/N-MNIST directory. Thereafter, the python convert_nmnist2h5.py script (taken from Perez-Nieves et al., 2021) needs to be run which processes the raw dataset. Finally, the Spiking Heidelberg Digits (SHD) dataset can be downloaded and unzipped into the data/SHD directory.

2. Train model

You can train the block and standard single-spike SNN on the different datasets using the train.py script. As an example, see the scripts/run_robustness.py script.

Building result figures

All speedup and training results can be built by running the notebooks/results/benchmark_results.ipynb and notebooks/results/dataset_results.ipynb notebooks. The code for the other paper figures can be found under notebooks/figures directory.

Speedup results

Training speedup of our model over the standard model. a. Total training speedup as a function of the number of hidden neurons $n$ and simulation steps $t$ (left), alongside the corresponding forward and backward pass speedups (right). b. Training durations of both models for fixed hidden neurons $n=100$ and variable batch size $b$. c. Training speedup over different number of layers for fixed time steps $t=2^7$ and batch size $b=128$. d. Training speedup over large number of hidden neurons $n$ for fixed time steps $t=2^7$ and variable batch size $b$. e. Forward pass speedup for fixed time steps $t=2^7$ and variable batch size $b$. f. Forward vs the backward pass speedup of our model for fixed time steps $t=2^7$ and variable batch size $b$. b-f use a $10$ sample average with the mean and s.d. plotted.

Dataset results

Analysis of our models performance on real datasets. a. Difference in accuracy between the standard multi-spike and our model. b. Training speedup of our model vs the standard single-spike model. c. Reduction in spikes of our single-spike model vs the standard multi-spike model (a-c use a $3$ sample average with the mean and s.d. plotted). d. Training robustness of our model to solve different datasets when starting with zero network activity, which is fatal to other single-spike training methods. Top panel: Normalised training loss over time. Bottom panel: Normalised network activity over time, where the red cross denotes the absence of any spikes.