Memory-Augmented Spiking Networks: Synergistic Integration of Complementary Mechanisms for Neuromorphic Vision
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arXiv
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| Format: | Preprint |
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2026
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| author | Blessing, Effiong Tseng, Chiung-Yi Nkrumah, Isaac Rehman, Junaid |
| author_facet | Blessing, Effiong Tseng, Chiung-Yi Nkrumah, Isaac Rehman, Junaid |
| contents | Spiking Neural Networks (SNNs) provide biological plausibility and energy efficiency, yet systematic investigations of memory augmentation strategies remain limited. We conduct a five-model ablation study integrating Leaky Integrate-and-Fire neurons, Supervised Contrastive Learning (SCL), Hopfield networks, and Hierarchical Gated Recurrent Networks (HGRN) on the N-MNIST dataset. Baseline SNNs exhibit organized neuronal groupings, or structured assemblies, characterized by a silhouette score of $0.687 \pm 0.012$. Individual augmentations introduce trade-offs: SCL improves accuracy by $0.28\%$ but reduces clustering (silhouette score $0.637 \pm 0.015$), while HGRN yields consistent gains in both accuracy ($+1.01\%$) and computational efficiency ($170.6\times$). Full integration achieves a balanced improvement across metrics, reaching a silhouette score of $0.715 \pm 0.008$, classification accuracy of $97.49 \pm 0.10\%$, energy consumption of $1.85 \pm 0.06\,μ\mathrm{J}$, and sparsity of $97.0\%$. These results indicate that optimal performance emerges from architectural balance rather than isolated optimization, establishing design principles for memory-augmented neuromorphic systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_08730 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Memory-Augmented Spiking Networks: Synergistic Integration of Complementary Mechanisms for Neuromorphic Vision Blessing, Effiong Tseng, Chiung-Yi Nkrumah, Isaac Rehman, Junaid Neural and Evolutionary Computing Machine Learning Spiking Neural Networks (SNNs) provide biological plausibility and energy efficiency, yet systematic investigations of memory augmentation strategies remain limited. We conduct a five-model ablation study integrating Leaky Integrate-and-Fire neurons, Supervised Contrastive Learning (SCL), Hopfield networks, and Hierarchical Gated Recurrent Networks (HGRN) on the N-MNIST dataset. Baseline SNNs exhibit organized neuronal groupings, or structured assemblies, characterized by a silhouette score of $0.687 \pm 0.012$. Individual augmentations introduce trade-offs: SCL improves accuracy by $0.28\%$ but reduces clustering (silhouette score $0.637 \pm 0.015$), while HGRN yields consistent gains in both accuracy ($+1.01\%$) and computational efficiency ($170.6\times$). Full integration achieves a balanced improvement across metrics, reaching a silhouette score of $0.715 \pm 0.008$, classification accuracy of $97.49 \pm 0.10\%$, energy consumption of $1.85 \pm 0.06\,μ\mathrm{J}$, and sparsity of $97.0\%$. These results indicate that optimal performance emerges from architectural balance rather than isolated optimization, establishing design principles for memory-augmented neuromorphic systems. |
| title | Memory-Augmented Spiking Networks: Synergistic Integration of Complementary Mechanisms for Neuromorphic Vision |
| topic | Neural and Evolutionary Computing Machine Learning |
| url | https://arxiv.org/abs/2603.08730 |