Training Deep Normalization-Free Spiking Neural Networks with Lateral Inhibition.

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.