Spiking Transformers offer an energy-efficient alternative to conventional deep learning by transmitting information solely through binary (0/1) spikes. However, there remains a substantial performance gap compared to artificial neural networks. A common belief is that their binary and sparse activation transmission leads to information loss, thus degrading feature representation and accuracy. In this work, however, we reveal for the first time that spiking neurons preferentially propagate low-frequency information. We hypothesize that the rapid dissipation of high-frequency components is the primary cause of performance degradation. For example, on Cifar-100, adopting Avg-Pooling (low-pass) for token mixing lowers performance to 76. 73%; interestingly, replacing it with Max-Pooling (high-pass) pushes the top-1 accuracy to 79. 12%, surpassing the well-tuned Spikformer baseline by 0. 97%. Accordingly, we introduce Max-Former that restores high-frequency signals through two frequency-enhancing operators: extra Max-Pooling in patch embedding and Depth-Wise Convolution in place of self-attention. Notably, our Max-Former (63. 99 M) hits the top-1 accuracy of 82. 39% on ImageNet, showing a +7. 58% improvement over Spikformer with comparable model size (74. 81%, 66. 34 M). We hope this simple yet effective solution inspires future research to explore the distinctive nature of spiking neural networks, beyond the established practice in standard deep learning. https: //github. com/bic-L/Spiking-Transformers-Need-High-Frequency-InformationCode is available.
Fang et al. (2025) studied this question.