ArticleSensors (Basel, Switzerland)2022
Rethinking the Role of Normalization and Residual Blocks for Spiking Neural Networks.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 12 citations in OpenAlex.
- A Genetic Algorithm-Optimized Kernel Density Estimation and D-S Evidence Fusion Classification for Predicting the Stability of Double Perovskite Materials.Materials (Basel, Switzerland) · 2026Article
- Rethinking skip connections in Spiking Neural Networks with Time-To-First-Spike coding.Frontiers in neuroscience · 2024Article
- Direct learning-based deep spiking neural networks: a review.Frontiers in neuroscience · 2023Review
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
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Abstract
Biologically inspired spiking neural networks (SNNs) are widely used to realize ultralow-power energy consumption. However, deep SNNs are not easy to train due to the excessive firing of spiking neurons in the hidden layers. To tackle this problem, we propose a novel but simple normalization technique called postsynaptic potential normalization. This normalization removes the subtraction term from the standard normalization and uses the second raw moment instead of the variance as the division term. The spike firing can be controlled, enabling the training to proceed appropriately, by conducting this simple normalization to the postsynaptic potential. The experimental results show that SNNs with our normalization outperformed other models using other normalizations. Furthermore, through the pre-activation residual blocks, the proposed model can train with more than 100 layers without other special techniques dedicated to SNNs.
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