# Quantifying How Training Gradient Sparsity Affect Spiking Neural Network Accuracy And Robustness

arXiv: https://arxiv.org/abs/2509.23762 · HTML: https://mlsanity.com/paper/2509.23762/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 29 references that are in the map.
- First posted on arXiv 28 Sep 2025.

## Builds on (most cited first, 18 of 18)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples](https://mlsanity.com/paper/1802.00420/) (2018)
- [Robustness May Be at Odds with Accuracy](https://mlsanity.com/paper/1805.12152/) (2018)
- [Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks](https://mlsanity.com/paper/2007.05785/) (2020)
- [Deep Residual Learning in Spiking Neural Networks](https://mlsanity.com/paper/2102.04159/) (2021)
- [Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations](https://mlsanity.com/paper/1703.03717/) (2017)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence](https://mlsanity.com/paper/2310.16620/) (2023)
- [Failures of Gradient-Based Deep Learning](https://mlsanity.com/paper/1703.07950/) (2017)
- [HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training with Crafted Input Noise](https://mlsanity.com/paper/2110.11417/) (2021)
- [Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activations](https://mlsanity.com/paper/2003.10399/) (2020)
- [Enhancing Adversarial Robustness in SNNs with Sparse Gradients](https://mlsanity.com/paper/2405.20355/) (2024)
- [Optical Quantum Mixed-State Reconstruction With Multiple Deep Learning Approaches](https://mlsanity.com/paper/2407.01734/) (2024)
- [Improvement of Spiking Neural Network with Bit Planes and Color Models](https://mlsanity.com/paper/2410.08229/) (2024)

## Abstract (arXiv)

Spiking Neural Networks (SNNs) have recently received increasing attention in both computational neuroscience and artificial intelligence owing to their potential for energy-efficient computation and reduced memory requirements. Despite these advantages, improving adversarial robustness in SNNs (particularly for vision-based applications) remains an emerging and relatively underexplored research problem. Recent work has suggested that encouraging sparse gradients can act as a regularization mechanism to improve resistance against adversarial perturbations. In this study, we report an unexpected observation: under certain architectural configurations, SNNs inherently exhibit sparse gradients and can attain state-of-the-art adversarial defense performance without requiring any explicit regularization strategy. Further investigation reveals an inherent trade-off between robustness and generalization. Specifically, increased gradient sparsity enhances resistance to adversarial attacks but may reduce the model's generalization capability, whereas denser gradients tend to improve generalization while simultaneously increasing susceptibility to adversarial perturbations. These findings provide new perspectives on the role of gradient sparsity in the training dynamics of SNNs.