The Dormant Spiking Neuron: A State-Driven Mechanism for Efficient Spiking Neural Networks

Spiking neural networks (SNNs) offer a compelling pathway toward energy-efficient artificial intelligence, fundamentally anchored in presynaptic input-driven sparsity. However, a substantial yet chronically overlooked source of computational overhead persists: the continuous subthreshold dynamics of postsynaptic neurons. Quantitative analysis reveals that up to 44\% of neurons in an ImageNet-trained ResNet-18 model reside in a deeply inhibited state, imposing a persistent computational burden despite possessing a negligible probability of firing. To eradicate this inefficiency, the Dormant Spiking Neuron (DSN) model is introduced. By incorporating a state-driven dynamic gate, the DSN allows a neuron to enter a dormant state when its membrane potential falls below a predefined negative threshold, dynamically pruning redundant computations at the instance level. Crucially, this dormancy mechanism is directly integrated into the neuronal dynamics and trained end-to-end via backpropagation through time using surrogate gradients, enabling the network to actively learn a robust dormancy policy that adaptively preserves critical information flow. Extensive experiments across static and neuromorphic datasets demonstrate that the DSN framework consistently reduces computational energy by 40\% to over 70\%. Notably, on the large-scale ImageNet dataset, the DSN outperforms state-of-the-art static pruning techniques by achieving equivalent energy savings while simultaneously improving upon the baseline accuracy by 1.34\%. By establishing a trainable, state-driven sparsity paradigm orthogonal to conventional input-driven methods, the DSN unlocks synergistic efficiency gains, presenting a highly potent approach for advancing neuromorphic computing. Source code will be made publicly available.