Spiking neural networks (SNNs) have garnered significant attention as a central paradigm in neuromorphic computing, owing to their energy efficiency and biological plausibility. However, training deep SNNs has critically depended on explicit normalization schemes, leading to a trade-off between performance and biological realism. To resolve this conflict, we propose a normalization-free learning framework that incorporates lateral inhibition inspired by cortical circuits. Our framework replaces the traditional feedforward SNN layer with a circuit of distinct excitatory (E) and inhibitory (I) neurons that captures the features of the canonical architecture of cortical E-I circuits. The circuit dynamically regulates neuronal activity through subtractive and divisive inhibition, which respectively control the activity and the gain of excitatory neurons. To enable and stabilize end-to-end training of the biologically constrained SNN, we propose two key techniques: E-I Init and E-I Prop. E-I Init is a dynamic parameter initialization scheme that balances excitatory and inhibitory inputs while performing gain control. E-I Prop decouples the backpropagation of the E-I circuits from the forward pass and regulates gradient flow. Experiments across multiple datasets and network architectures demonstrate that our framework enables stable training of deep normalization-free SNNs with biological realism and achieves competitive performance without resorting to explicit normalization schemes. Therefore, our work not only provides a solution to training deep SNNs but also serves as a computational platform for further exploring the functions of E-I interactions in large-scale cortical computation.

翻译：脉冲神经网络（SNNs）作为神经形态计算的核心范式，因其能效优势和生物学合理性而受到广泛关注。然而，训练深度SNNs严重依赖于显式归一化方案，导致性能与生物真实性之间存在权衡。为解决这一矛盾，我们提出了一种受皮层回路启发的、结合侧向抑制的无归一化学习框架。该框架将传统的前馈SNN层替换为由不同的兴奋性（E）和抑制性（I）神经元组成的回路，该回路捕捉了皮层E-I回路的典型结构特征。该回路通过减法和除法抑制动态调节神经元活动，分别控制兴奋性神经元的活动和增益。为实现并稳定生物约束SNN的端到端训练，我们提出了两项关键技术：E-I初始化和E-I传播。E-I初始化是一种动态参数初始化方案，在执行增益控制的同时平衡兴奋性和抑制性输入。E-I传播将E-I回路的反向传播与前向传递解耦，并调节梯度流。在多个数据集和网络架构上的实验表明，我们的框架能够稳定训练具有生物真实性的深度无归一化SNNs，并在不依赖显式归一化方案的情况下取得有竞争力的性能。因此，我们的工作不仅为训练深度SNNs提供了解决方案，也为进一步探索E-I相互作用在大规模皮层计算中的功能提供了一个计算平台。