Researchers have introduced a novel pruning strategy for Spiking Neural Networks (SNNs) that adapts to the layer-specific magnitude of spiking activity, addressing the limitations of existing methods. The new approach accounts for temporal accumulation, non-uniform timestep contributions, and membrane stability, which are critical factors in SNNs. By doing so, it mitigates the severe performance degradation often associated with naive magnitude-based pruning strategies1. This breakthrough has significant implications for the deployment of SNNs, which offer energy-efficient computation but are hindered by dense connectivity and high spiking operation costs. The ability to efficiently prune SNNs enables their use in resource-constrained environments, making them more viable for a wide range of applications. This matters to practitioners because it enables the development of more efficient and effective SNNs, which can be used to improve performance in areas such as computer vision and natural language processing.