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arXiv · 2504.01257

FLAMES: A Hybrid Spiking-State Space Model for Adaptive Memory Retention in Event-Based Learning

Biswadeep Chakraborty·Saibal Mukhopadhyay
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Abstract

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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Biswadeep Chakraborty, Saibal Mukhopadhyay. 2025-04-02. FLAMES: A Hybrid Spiking-State Space Model for Adaptive Memory Retention in Event-Based Learning. https://arxiv.org/abs/2504.01257

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