Spiking Neural Networks Need High-Frequency Information

Yuetong Fang, Deming Zhou, Ziqing Wang, Hongwei Ren, ZeCui Zeng, Lusong Li, shibo zhou, Renjing Xu

Keywords: Spiking Neural Networks, Vision Transformers

TL;DR: The paper reveals that the performance gap between SNNs and ANNs stems not from information loss caused by binary spike activations, but from the intrinsic low-pass filtering of spiking neurons.

Abstract: Spiking Neural Networks promise brain-inspired and energy-efficient computation by transmitting information through binary (0/1) spikes. Yet, their performance still lags behind that of artificial neural networks, often assumed to result from information loss caused by sparse and binary activations. In this work, we challenge this long-standing assumption and reveal a previously overlooked frequency bias: **spiking neurons inherently suppress high-frequency components and preferentially propagate low-frequency information.** This frequency-domain imbalance, we argue, is the root cause of degraded feature representation in SNNs. Empirically, on Spiking Transformers, adopting Avg-Pooling (low-pass) for token mixing lowers performance to 76.73% on Cifar-100, whereas replacing it with Max-Pool (high-pass) pushes the top-1 accuracy to 79.12%. Accordingly, we introduce **Max-Former** that restores high-frequency signals through two frequency-enhancing operators: (1) extra Max-Pool in patch embedding, and (2) Depth-Wise Convolution in place of self-attention. Notably, **Max-Former** attains 82.39% top-1 accuracy on ImageNet using only 63.99M parameters, surpassing Spikformer (74.81%, 66.34M) by +7.58%. Extending our insight beyond transformers, our **Max-ResNet-18** achieves state-of-the-art performance on convolution-based benchmarks: 97.17% on CIFAR-10 and 83.06% on CIFAR-100. We hope this simple yet effective solution inspires future research to explore the distinctive nature of spiking neural networks. Code is available: https://github.com/bic-L/MaxFormer.

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Submission Number: 5320