# Robust and accelerated single-spike spiking neural network training with applicability to challenging temporal tasks

arXiv: https://arxiv.org/abs/2205.15286 · HTML: https://mlsanity.com/paper/2205.15286/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2022.

## Built on by (most cited first, 4 of 4)

- [Backpropagation-free Spiking Neural Networks with the Forward-Forward Algorithm](https://mlsanity.com/paper/2502.20411/) (2025)
- [FFGAF-SNN: The Forward-Forward Based Gradient Approximation Free Training Framework for Spiking Neural Networks](https://mlsanity.com/paper/2507.23643/) (2025)
- [Equivalence of approximation by networks of single- and multi-spike neurons](https://mlsanity.com/paper/2603.13478/) (2026)
- [Multi-Task Neural Architecture Search Using Architecture Embedding and Transfer Rank](https://mlsanity.com/paper/2504.00772/) (2025)

## Abstract (arXiv)

Spiking neural networks (SNNs), particularly the single-spike variant in which neurons spike at most once, are considerably more energy efficient than standard artificial neural networks (ANNs). However, single-spike SSNs are difficult to train due to their dynamic and non-differentiable nature, where current solutions are either slow or suffer from training instabilities. These networks have also been critiqued for their limited computational applicability such as being unsuitable for time-series datasets. We propose a new model for training single-spike SNNs which mitigates the aforementioned training issues and obtains competitive results across various image and neuromorphic datasets, with up to a 13.98× training speedup and up to an 81% reduction in spikes compared to the multi-spike SNN. Notably, our model performs on par with multi-spike SNNs in challenging tasks involving neuromorphic time-series datasets, demonstrating a broader computational role for single-spike SNNs than previously believed.