Runtime-Adaptive Temporal Integration Improves Sparsity and Accuracy in Spiking Neural Networks

Spiking neural networks inherently operate in an event-driven manner, yet their neurons typically rely on a fixed temporal integration scale during inference, regardless of changes in input conditions or their own activity states. Although some neuron models learn task-dependent membrane time constants during training, these time constants are typically kept fixed at inference, limiting the ability of SNNs to adapt their temporal integration to runtime conditions. Motivated by this observation, we study runtime-adaptive temporal integration as a computational perspective in which each neuron can adjust its temporal integration online according to local input conditions and recent firing activity. To instantiate this perspective, we develop RATI-LIF, a lightweight LIF-based neuron that provides a practical realization of runtime-adaptive temporal integration. RATI-LIF learns a task-dependent reference integration timescale through layer-wise base time constants and further modulates this timescale at runtime through neuron-level input-driven and output-spike-driven dynamics. Experimental results show that RATI-LIF substantially reduces neuronal firing rates and estimated energy consumption while improving classification accuracy. Taken together, these results suggest that runtime-adaptive temporal integration offers a promising direction for building sparser and more effective spiking neural networks, with RATI-LIF providing a lightweight and practical realization of this idea.