Papers › Robust and accelerated single-spike spiking neural network training with applicability...
Robust and accelerated single-spike spiking neural network training with applicability to challenging temporal tasks
Luke Taylor, Andrew King, Nicol Harper
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Audio Classification | SHD | FastSNN | Percentage correct | 70.32 | #11 of 11 | Archive leaderboard | report |
| Image Classification | Fashion-MNIST | FastSNN (CNN) | Accuracy | 90.57 | #28 of 34 | Archive leaderboard | report |
| Image Classification | Fashion-MNIST | FastSNN (MLP) | Accuracy | 89.05 | #29 of 34 | Archive leaderboard | report |
| Image Classification | MNIST | FastSNN (CNN) | Accuracy | 99.3 | #69 of 81 | Archive leaderboard | report |
| Image Classification | MNIST | FastSNN (MLP) | Accuracy | 97.91 | #77 of 81 | Archive leaderboard | report |
| Image Classification | N-MNIST | FastSNN | Accuracy | 95.91 | #3 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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