Memory-Augmented Spiking Networks: Synergistic Integration of Complementary Mechanisms for Neuromorphic Vision

2026-03-11 · Source: cs.NE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Neuromorphic Computing · Depth: Advanced, extended

Summary

A systematic investigation into memory-augmented Spiking Neural Networks (SNNs) on the N-MNIST dataset reveals that integrating complementary memory mechanisms achieves superior performance over individual optimizations. The study conducted five-model ablation studies, combining Leaky Integrate-and-Fire (LIF) neurons, Supervised Contrastive Learning (SCL), Hopfield networks, and Hierarchical Gated Recurrent Networks (HGRN). Baseline SNNs naturally form structured neuron assemblies with a silhouette score of 0.687. While SCL improved accuracy by 0.28% to 96.71%, it disrupted clustering quality (silhouette 0.637). HGRN provided consistent gains, increasing accuracy by 1.01% to 97.44% and improving energy efficiency by 170.6x. The full integration of all components achieved an optimal balance, yielding a silhouette score of 0.715, 97.49% accuracy, 1.85 µJ energy consumption, and 97.0% sparsity, demonstrating synergistic effects.

Key takeaway

For Computer Vision Engineers developing neuromorphic systems, this research indicates that a holistic architectural approach to memory augmentation in SNNs is critical. You should prioritize integrating complementary mechanisms like HGRN, SCL, and Hopfield networks, rather than optimizing individual components, to achieve superior accuracy, energy efficiency, and memory assembly quality. Your design strategy should focus on balancing these elements to resolve inherent trade-offs and unlock synergistic benefits for practical neuromorphic deployment.

Key insights

Architectural balance of memory mechanisms in SNNs yields synergistic performance gains over individual optimizations.

Principles

• SNNs inherently structure representations.
• Individual augmentations introduce trade-offs.
• Compatible integration is crucial for synergy.

Method

The study used five-model ablation on N-MNIST, integrating LIF neurons, SCL, Hopfield networks, and HGRN to evaluate their synergistic effects on accuracy, clustering, and energy efficiency.

In practice

• Use HGRN for significant SNN efficiency gains.
• Combine SCL, Hopfield, and HGRN for optimal SNN performance.
• Prioritize temporal gating for spike-based processing.

Topics

• Spiking Neural Networks
• Neuromorphic Vision
• Memory Augmentation
• Hierarchical Gated Recurrent Networks
• Supervised Contrastive Learning

Best for: Computer Vision Engineer, Research Scientist, AI Researcher, AI Scientist, Deep Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.NE updates on arXiv.org.