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

arXiv: https://arxiv.org/abs/2603.08730 · HTML: https://mlsanity.com/paper/2603.08730/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 21 Feb 2026.

## Builds on (most cited first, 11 of 11)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Hopfield Networks is All You Need](https://mlsanity.com/paper/2008.02217/) (2020)
- [Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks](https://mlsanity.com/paper/1706.02609/) (2017)
- [Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades](https://mlsanity.com/paper/1507.07629/) (2015)
- [SLAYER: Spike Layer Error Reassignment in Time](https://mlsanity.com/paper/1810.08646/) (2018)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks](https://mlsanity.com/paper/2103.00476/) (2021)
- [Hierarchically Gated Recurrent Neural Network for Sequence Modeling](https://mlsanity.com/paper/2311.04823/) (2023)
- [Temporal Contrastive Learning for Spiking Neural Networks](https://mlsanity.com/paper/2305.13909/) (2023)
- [A Hybrid Multi-Well Hopfield-CNN with Feature Extraction and K-Means for MNIST Classification](https://mlsanity.com/paper/2507.08766/) (2025)

## Abstract (arXiv)

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 ± 0.012. Individual augmentations introduce trade-offs: SCL improves accuracy by 0.28% but reduces clustering (silhouette score 0.637 ± 0.015), while HGRN yields consistent gains in both accuracy (+1.01%) and computational efficiency (170.6×). Full integration achieves a balanced improvement across metrics, reaching a silhouette score of 0.715 ± 0.008, classification accuracy of 97.49 ± 0.10%, energy consumption of 1.85 ± 0.06,μJ, 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.