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

• URL: http://arxiv.org/abs/2504.01257v1
• Date: Wed, 02 Apr 2025 00:08:19 GMT
• Title: FLAMES: A Hybrid Spiking-State Space Model for Adaptive Memory Retention in Event-Based Learning
• Authors: Biswadeep Chakraborty, Saibal Mukhopadhyay,
• Abstract summary: FLAMES is a hybrid framework integrating structured state-space dynamics with event-driven computation.<n>By bridging neuromorphic computing and structured sequence modeling, FLAMES enables scalable long-range reasoning in event-driven systems.
• Score: 16.60622265961373
• License: http://creativecommons.org/licenses/by/4.0/
• 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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