Xiaoyang Zhang

Intrinsically Stable Spiking Neural Networks: Overcoming the Performance Barrier in the Absence of Batch Normalization

The Intrinsically Stable SNN (IS-SNN) architecture is proposed, which removes activation-normalization layers by enforcing signal homeostasis through topology-aware weight standardization and modified residual connections and removes the runtime statistics tracking and multiplications introduced by activation normalization, restoring an accumulation-oriented inference datapath.