Resolving the Timestep Scaling Paradox in Spiking Neural Networks with a Timestep-Scalable Neuron Model

Spiking Neural Networks (SNNs) have garnered increasing attention for their biological plausibility, energy efficiency, and temporal modeling capability. Due to the non-differentiability of spike generation, a widely used supervised training method for SNNs is backpropagation through time with surrogate gradients, which achieves competitive performance with a small number of timesteps. Intuitively, scaling timesteps should further improve performance by enriching temporal dynamics. However, we observe timestep scaling paradox (TSP), a counter-intuitive degradation in accuracy when scaling timesteps. We investigate TSP and link it to long-term temporal gradient vanishing and weakened cross-timestep dependencies. To address this, we propose the Timestep-Scalable (TS) neuron model. It introduces long-term memory reconsolidation to enhance cross-timestep information flow and enable effective learning with more timesteps. In parallel, a temporal forgetting mechanism periodically truncates the accumulation path, suppressing excessive temporal information buildup and improving training stability. Supported by theoretical analysis and extensive experiments, TS consistently improves performance when scaling timesteps. Beyond gains from timestep scaling, it attains state-of-the-art results on EEG signals, event-based recognition, and time-series forecasting, while remaining strong on conventional image classification and object detection datasets.