Unveiling the Spatial-temporal Effective Receptive Fields of Spiking Neural Networks
Jieyuan Zhang, Xiaolong Zhou, Wenjie Wei, Hanwen Liu, Qian Sun, Shuai Wang, Malu Zhang, Yang Yang, Haizhou Li
Spiking Neural Networks (SNNs) offer promising biological plausibility, energy efficiency, and event-driven processing for neuromorphic computing. Despite architectural and training advances, their complex spatiotemporal dynamics complicate understanding of intrinsic learning behaviors. We introduce Spatio-Temporal Effective Receptive Field (ST-ERF), extending traditional receptive field analysis to accommodate SNN temporal dynamics. Our analysis reveals two key ST-ERF properties: Gaussian-like spatial distributions and power-law temporal decay patterns.  Furthermore, examination of Transformer-based SNN architectures reveals significant receptive field variations, with several prominent models (SDT-V1, Meta-SDT, QKFormer) showing limited global receptive fields that impair visual long-sequence modeling. Identifying the channel-mixer component as the critical factor, we propose two novel designs: MLPixer and Splash-and-reconstruct Block (SR Block). These innovations enhance global spatial receptive fields in early network stages, significantly improving performance on challenging visual tasks. Experimental results validate our theoretical findings and demonstrate the effectiveness of our proposed architectures, advancing both theoretical understanding and practical applications of SNNs in complex visual processing.