Weight initialization significantly impacts the convergence and performance of neural networks. While traditional methods like Xavier and Kaiming initialization are widely used, they often fall short for spiking neural networks (SNNs), which have distinct requirements compared to artificial neural networks (ANNs). To address this, we introduce \textbf{IKUN}, a variance-stabilizing initialization method integrated with surrogate gradient functions, specifically designed for SNNs. \textbf{IKUN} stabilizes signal propagation, accelerates convergence, and enhances generalization. Experiments show \textbf{IKUN} improves training efficiency by up to \textbf{50\%}, achieving \textbf{95\%} training accuracy and \textbf{91\%} generalization accuracy. Hessian analysis reveals that \textbf{IKUN}-trained models converge to flatter minima, characterized by Hessian eigenvalues near zero on the positive side, promoting better generalization. The method is open-sourced for further exploration: \href{https://github.com/MaeChd/SurrogateVarStabe}{https://github.com/MaeChd/SurrogateVarStabe}.

本研究解决了传统权重初始化方法在脉冲神经网络（SNNs）中的不足，提出了一种新颖的IKUN初始化方法，采用了方差稳定的技术及替代梯度函数。IKUN能显著加速收敛，提高训练和泛化准确率，实验证明训练效率提高了50%，实现了95%的训练准确率和91%的泛化准确率。

IKUN：保持脉冲神经网络训练和泛化性能的初始化方法