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

2025-04-02

AI Summary

Addressing the challenges of modeling long-range dependencies, uncontrolled memory decay, and high computational complexity in event-driven learning, this paper proposes the first adaptive hybrid framework integrating spiking neural dynamics with structured state space models (SSMs). Our approach introduces two key innovations: (1) a spike-aware HiPPO mechanism that dynamically modulates memory retention strength based on inter-spike intervals; and (2) an NPLR matrix decomposition technique that reduces SSM inference complexity from $O(L^2)$ to $O(L log L)$, overcoming classical scalability bottlenecks. Evaluated on benchmark long-range sequence tasks (Long Range Arena) and real-world neuromorphic datasets (HAR-DVS, Celex-HAR), our method achieves state-of-the-art accuracy in long-range reasoning while incurring significantly lower computational overhead. It thus markedly improves both modeling efficiency and generalization capability for event sequences.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Natural Language Processing: Learning & Optimization for NLP
• Machine Learning: Neuro-Symbolic Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

We propose extbf{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 extit{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.

Problem

• Adaptive memory retention in event-based learning systems
• Balancing short- and long-range dependencies dynamically
• Reducing computational complexity for efficient event processing

Innovation

• Hybrid spiking-state space model
• Spike-Aware HiPPO memory mechanism
• Normal-plus-low-rank decomposition efficiency