Narrowing the ANN–SNN Gap for Continuous 1D Temporal Signal Classification with Multi-Scale Temporal Encoding and Sparsity-Regularized Transform Encoding

Spiking neural networks (SNNs) promise energyefficient inference, yet on static vision benchmarks, they often trail matched ANNs under short simulation horizons. Under a matchedbackbone and matched-budget protocol without extra tricks, we find that this ANN-SNN accuracy gap is consistently smaller on representative continuous 1D temporal signal classification benchmarks than on image benchmarks. We attribute this to a mechanism-level mismatch: leaky integration naturally implements causal evidence accumulation over time for native temporal signals, while static images typically require amplitude-to-spike encoding, whose finitewindow estimation error becomes non-negligible at short horizons. Guided by this view, we propose a plug-and-play framework that combines Multi-Scale Temporal Encoding (MTE) and Sparsity-Regularized Transform Encoding (STE). MTE replaces naive repetition with multi-scale streams and allocates scale-aligned multi-bit integer spikes to increase per-step information density, and STE replaces a controllable fraction of LIF units with a transform-encoding neuron trained using auxiliary reconstruction and sparsity regularization, with a synthesis branch used only during training. Across diverse 1D datasets and backbone families, MTE×STE consistently improves the accuracy-efficiency trade-off over standard SNN baselines and matches or occasionally surpasses ANN counterparts. The source code is available at https://github.com/ Sun7-7/Signal_Code_SNN