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

Effiong Blessing; Chiung-Yi Tseng; Isaac Nkrumah; Junaid Rehman

cs.NEFeb 21, 2026

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

Authors: Effiong Blessing, Chiung-Yi Tseng, Isaac Nkrumah, Junaid Rehman

Organizations: Department Of Computer Science, Saint Louis University · Luxmuse AI · Saint Louis University · Independent Researcher

Abstract

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.

Figures & tables

Fig. 1 : Complete Memory-Augmented SNN Architecture. Three-stage pipeline integrating: Stage 1 (SNN Encoder, blue) processes dynamic vision sensor(DVS) spikes through Conv1+LIF, Conv2+LIF, and FC(512)+LIF feature extraction; Stage 2 (Memory Augmentation, green) applies Supervised Contrastive Learning for engram formation and Hopfield networks for associative memory; Stage 3 (HGRN Temporal Gating, red) performs selective information flow with forget/update/cell gates over T=25 steps.

Fig. 2 : LIF Neuron Temporal Dynamics and Circuit Model. Top: Membrane potential

U(t)

exhibits leaky integration with decay

\beta=0.9

, generating spikes when crossing threshold

\theta=1.0

. Bottom: Circuit model showing synaptic inputs converging at soma with membrane capacitance and leak, implementing biological integrate-and-fire dynamics.

Fig. 3 : Hopfield Network Module for Associative Memory. Top flowchart: Six-stage process from Input Pattern (blue, $h^{0}$ from SNN, 512-D with dot pattern visualization) through Weight Matrix (green, $\mathbf{W}=\sum\boldsymbol{\xi}^{p}(\boldsymbol{\xi}^{p})^{T}$ , outer-product learning), Energy (red, $E=-\frac{1}{2}\boldsymbol{h}^{T}\mathbf{W}\boldsymbol{h}$ , minimization to attractors), State Update (purple, $h^{t+1}=\text{sign}(\mathbf{W}h^{t})$ , recurrent iterations t=0,1,2), Convergence (yellow, check $||h^{t+1}-h^{t}||<\epsilon$ , max $T_{max}=5$ ), to Retrieved Pattern (blue, $h^{*}$ , 512-D). Purple dashed arrow shows recurrent loop. Bottom visualizations: Left shows energy landscape with global attractor (red star), local minima (blue triangles), and convergence paths (purple arrows) in 2D state space; Center shows stored pattern assemblies for digits 0-9 as colored clusters with overlap enabling generalization; Right shows convergence dynamics with pattern similarity (blue, 0 $\rightarrow$ 1.0) and energy (red, 12 $\rightarrow$ 1.2) over 5 iterations, demonstrating rapid convergence and energy minimization.

Fig. 4 : Information Flow Control Mechanism in HGRN. Vertical flowchart showing how Previous State $C_{t-1}$ (green) influences current processing through two pathways: Forget Gate $f_{t}$ (orange, left) determines what to selectively discard, and Update Gate $u_{t}$ (orange, right) determines what to selectively integrate. Both gates converge to Current State $c_{t}$ (yellow) through element-wise operations. Formula box (pink) shows the cell state update equation $c_{t}=f_{t}\odot c_{t-1}+u_{t}\odot\tilde{c}_{t}$ combining forget and update operations. Arrow flows to Next State $c_{t+1}$ (purple), completing the temporal recurrence.

Fig. 5 : HGRN Temporal Gating Mechanism - Detailed Module. Top: Information flow from Memory State (green, 512-D from Hopfield) through Forget Gate

f_{t}

(pink, controls what to discard) and Update Gate

u_{t}

(pink, controls what to integrate) to Cell State Update (yellow, gated information flow), Hidden State (blue, goal-directed representation), Hierarchical Coordination (gold, multi-level context integration), and Output (purple, to classifier). Purple dashed line shows temporal recurrence

h_{t-1}\rightarrow h_{t}

maintaining memory across time. Bottom panels: Left shows complete gate equations with sigmoid and element-wise operations; Center shows HGRN performance benefits including +1.01% accuracy improvement, 0.698 silhouette, 1.85 µJ energy (170.6

\times

vs ANNs), 97.0% sparsity, differentiable gradients, and temporal alignment with spike dynamics; Right explains why HGRN works through temporal domain operation, smooth gradients (unlike Hopfield sign function), adaptive temporal filtering, and consistent improvements across all metrics.

Model	Val Acc (%)	Test Acc (%)	Silhouette	Energy (µJ)
M1: Baseline (No SCL)	96.43	96.35	0.687	2.39
M2: Baseline + SCL	96.71	96.68	0.637	2.90
M3: SNN + SCL + Hopfield	96.21	96.15	0.695	$\sim$ 2.3
M4: SNN + SCL + HGRN	97.44	97.38	0.698	1.85
M5: Full Hybrid (All)	97.49	97.44	0.715	$\sim$ 1.9

TABLE I : Comprehensive Ablation Study Results on N-MNIST. Test set accuracies reported. Best validation accuracies: M1 (96.67%), M2 (96.94%), M3 (96.85%), M4 (97.57%), M5 (97.54%).

Fig. 6 : Training Dynamics Across Five Configurations. Validation accuracy (top-left) shows Models 4 and 5 achieving the highest performance with stable convergence. Training loss (top-right) demonstrates smooth optimization. Final validation accuracies: M1 (96.67%), M2 (96.94%), M3 (96.85%), M4 (97.57%), M5 (97.54%).

Fig. 7 : Memory Assembly Quality Comparison Across Configurations. Silhouette scores reveal unexpected component interactions: Baseline (M1) achieves good clustering (0.687) through spike-timing dynamics alone. SCL (M2) disrupts structure (0.637) despite improving accuracy. Memory mechanisms (M3/M4) partially restore quality (0.695-0.698). Full integration (M5) achieves excellent clustering (0.715, marked with star), exceeding the 0.7 “excellent” threshold and demonstrating synergistic effects through architectural balance.

Panel of Figure 9 — Fig. 8 : t-SNE Visualization of Memory Assembly Evolution. (a) Baseline (M1, Silhouette 0.687): SNNs naturally form well-separated class clusters through spike-timing dynamics, showing distinct but somewhat overlapping assemblies. (b) +SCL (M2, Silhouette 0.637): Contrastive learning disrupts existing structure, increasing cluster overlap as output-space optimization conflicts with spike-timing organization. (c) Full Hybrid (M5, Silhouette 0.715): Synergistic integration achieves tightly grouped, excellently separated clusters, demonstrating that architectural balance resolves individual component conflicts.(See M3, M4 in appendix)

Model	Energy/Inf	Total Energy	Sparsity	Reduction	Energy Saved	SynOps	MACs
	(µJ)	(µJ)	(%)	vs ANN	(%)	(M)	(M)
M1: Baseline	0.0030	4.72	98.73	403.1 $\times$	99.8	5.247	413.84
M2: +SCL	0.0036	5.79	98.44	328.6 $\times$	99.7	6.438	413.84
M3: +Hopfield	0.0039	6.30	98.31	302.0	99.7	7.004	413.84
M4: +HGRN	0.0020	3.15	99.15	603.9 $\times$	99.8	3.503	413.86
M5: Full Hybrid	$\sim$ 0.0020	$\sim$ 3.2	97.0	$\sim$ 600 $\times$	99.8	$\sim$ 3.5	$\sim$ 414

TABLE II : Comprehensive Energy Efficiency Analysis

Method	Accuracy (%)	Bio-plausible
Diehl & Cook (2015) [ 3 ]	$\sim$ 95.0	Yes (STDP)
SLAYER (2018) [ 12 ]	92.5	Yes
Wu et al. (2018) [ 13 ]	99.4	Partial
ANN-SNN (2021) [ 14 ]	98.5	Partial
Ours (M4: SNN+HGRN)	97.44	Yes
Ours (M5: Full Hybrid)	97.49	Yes

TABLE III : Comparison with State-of-the-Art Methods on N-MNIST

Appendix figures & tables1 asset

Supplementary material from the paper’s appendix.

Appendix

Panel of Figure 12 — Fig. 9 : Cluster Analysis for Models 3 and 4. (a) Hopfield integration partially restores structure (0.695 vs M2’s 0.637). (b) HGRN achieves better clustering (0.698), demonstrating temporal gating integrates naturally with spike processing. It shows detailed t-SNE visualizations for Models 3 and 4, complementing the main paper’s analysis of Models 1, 2, and 5.

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Abstract

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