STAER: Temporal Aligned Rehearsal for Continual Spiking Neural Network

• URL: http://arxiv.org/abs/2601.20870v1
• Date: Fri, 16 Jan 2026 09:54:10 GMT
• Title: STAER: Temporal Aligned Rehearsal for Continual Spiking Neural Network
• Authors: Matteo Gianferrari, Omayma Moussadek, Riccardo Salami, Cosimo Fiorini, Lorenzo Tartarini, Daniela Gandolfi, Simone Calderara,
• Abstract summary: Spiking Neural Networks (SNNs) are inherently suited for continuous learning due to their event-driven temporal dynamics.<n>We introduce Spiking Temporal Alignment with Experience Replay (STAER) to bridge the performance gap between SNNs and ANNs.<n>Our approach integrates a differentiable Soft-DTW alignment loss to maintain spike timing fidelity and employs a temporal expansion and contraction mechanism on output logits to enforce robust representation learning.
• Score: 11.986684053664087
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Spiking Neural Networks (SNNs) are inherently suited for continuous learning due to their event-driven temporal dynamics; however, their application to Class-Incremental Learning (CIL) has been hindered by catastrophic forgetting and the temporal misalignment of spike patterns. In this work, we introduce Spiking Temporal Alignment with Experience Replay (STAER), a novel framework that explicitly preserves temporal structure to bridge the performance gap between SNNs and ANNs. Our approach integrates a differentiable Soft-DTW alignment loss to maintain spike timing fidelity and employs a temporal expansion and contraction mechanism on output logits to enforce robust representation learning. Implemented on a deep ResNet19 spiking backbone, STAER achieves state-of-the-art performance on Sequential-MNIST and Sequential-CIFAR10. Empirical results demonstrate that our method matches or outperforms strong ANN baselines (ER, DER++) while preserving biologically plausible dynamics. Ablation studies further confirm that explicit temporal alignment is critical for representational stability, positioning STAER as a scalable solution for spike-native lifelong learning. Code is available at https://github.com/matteogianferrari/staer.

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