让脉冲神经网络自适应调节记忆时间，高效处理长时序依赖。
ChronoPlastic Spiking Neural Networks
- 通过动态调节突触衰减率实现时间信用分配
- 在长间隔时序任务上训练速度与可靠性显著提升
- 适合需要低功耗时序建模的神经形态计算场景
脉冲神经网络（SNNs）提供了生物合理且节能的替代架构，但受限于固定突触和膜时间常数，难以处理长程时序依赖。本文提出时空可塑脉冲神经网络（CPSNNs），一种新架构原理，通过根据网络状态动态调节突触衰减率，实现自适应的时间信用分配。CPSNNs 维持多个内部时间轨迹，并学习连续的时间扭曲函数，选择性保留任务相关信息，同时快速遗忘噪声。与基于自适应膜常数、注意力机制或外部记忆的先前方法不同，CPSNNs 将时间控制直接嵌入局部突触动力学，保持线性时间复杂度和神经形态兼容性。我们给出了模型的形式描述，分析其计算特性，并实证表明，相比标准 SNN 基线，CPSNNs 在学习长间隙时序依赖方面更快且更可靠。结果表明，自适应时间调制是可扩展时序学习中缺失的关键要素。
原文摘要 · Abstract (English)
Spiking neural networks (SNNs) offer a biologically grounded and energy-efficient alternative to conventional neural architectures; however, they struggle with long-range temporal dependencies due to fixed synaptic and membrane time constants. This paper introduces ChronoPlastic Spiking Neural Networks (CPSNNs), a novel architectural principle that enables adaptive temporal credit assignment by dynamically modulating synaptic decay rates conditioned on the state of the network. CPSNNs maintain multiple internal temporal traces and learn a continuous time-warping function that selectively preserves task-relevant information while rapidly forgetting noise. Unlike prior approaches based on adaptive membrane constants, attention mechanisms, or external memory, CPSNNs embed temporal control directly within local synaptic dynamics, preserving linear-time complexity and neuromorphic compatibility. We provide a formal description of the model, analyze its computational properties, and demonstrate empirically that CPSNNs learn long-gap temporal dependencies significantly faster and more reliably than standard SNN baselines. Our results suggest that adaptive temporal modulation is a key missing ingredient for scalable temporal learning in spiking systems.
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