Skip Connections in Spiking Neural Networks: An Analysis of Their Effect on Network Training
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Abstract (arXiv)
Spiking neural networks (SNNs) have gained attention as a promising alternative to traditional artificial neural networks (ANNs) due to their potential for energy efficiency and their ability to model spiking behavior in biological systems. However, the training of SNNs is still a challenging problem, and new techniques are needed to improve their performance. In this paper, we study the impact of skip connections on SNNs and propose a hyperparameter optimization technique that adapts models from ANN to SNN. We demonstrate that optimizing the position, type, and number of skip connections can significantly improve the accuracy and efficiency of SNNs by enabling faster convergence and increasing information flow through the network. Our results show an average +8% accuracy increase on CIFAR-10-DVS and DVS128 Gesture datasets adaptation of multiple state-of-the-art models.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 16 references that are in the map. First posted on arXiv 23 Mar 2023.
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8 of 16 references.
References · 8
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2016Densely Connected Convolutional Networkscited by 6,691
- 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 604
- 2017SuperSpike: Supervised learning in multi-layer spiking neural networkscited by 178
- 2020Revisiting Batch Normalization for Training Low-latency Deep Spiking Neural Networks from Scratchcited by 76
- 2020Event-Based Backpropagation can compute Exact Gradients for Spiking Neural Networkscited by 59
- 2018A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processescited by 52
- 2022Neural Architecture Search for Spiking Neural Networkscited by 52
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