让事件驱动系统学会动态记忆，高效处理长时序依赖。
FLAMES: A Hybrid Spiking-State Space Model for Adaptive Memory Retention in Event-Based Learning
- 用脉冲感知机制根据脉冲间隔动态调整记忆保留。
- 在长程任务上超越现有方法，计算复杂度从O(N²)降至O(Nr)。
- 适合神经形态计算、实时事件流处理等场景。
我们提出一种新型混合框架FLAMES（Fast Long-range Adaptive Memory for Event-based Systems），融合结构化状态空间动态与事件驱动计算。核心为脉冲感知HiPPO（SA-HiPPO）机制，根据脉冲间隔动态调整记忆保留，有效捕捉短程与长程依赖。为保持计算效率，引入正态加低秩（NPLR）分解，将复杂度从𝑂(𝑁²)降至𝑂(𝑁𝑟)。FLAMES在长程基准测试及事件数据集HAR-DVS和Celex-HAR上达到当前最优性能。该模型打通神经形态计算与结构化序列建模的鸿沟，实现事件驱动系统中的可扩展长程推理。
原文摘要 · Abstract (English)
We propose \textbf{FLAMES (Fast Long-range Adaptive Memory for Event-based Systems)}, a novel hybrid framework integrating structured state-space dynamics with event-driven computation. At its core, the \textit{Spike-Aware HiPPO (SA-HiPPO) mechanism} dynamically adjusts memory retention based on inter-spike intervals, preserving both short- and long-range dependencies. To maintain computational efficiency, we introduce a normal-plus-low-rank (NPLR) decomposition, reducing complexity from $\mathcal{O}(N^2)$ to $\mathcal{O}(Nr)$. FLAMES achieves state-of-the-art results on the Long Range Arena benchmark and event datasets like HAR-DVS and Celex-HAR. By bridging neuromorphic computing and structured sequence modeling, FLAMES enables scalable long-range reasoning in event-driven systems.
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