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MorphSNN: Adaptive Graph Diffusion and Structural Plasticity for Spiking Neural Networks

Yongsheng Huang, Peibo Duan, Yujie Wu, Kai Sun, Zhipeng Liu, Jiaxiang Liu, Guangyu Li, Changsheng Zhang, Bin Zhang, Mingkun Xu

Abstract

Spiking Neural Networks (SNNs) currently face a critical bottleneck: while individual neurons exhibit dynamic biological properties, their macro-scopic architectures remain confined within conventional connectivity patterns that are static and hierarchical. This discrepancy between neuron-level dynamics and network-level fixed connectivity eliminates critical brain-like lateral interactions, limiting adaptability in changing environments. To address this, we propose MorphSNN, a backbone framework inspired by biological non-synaptic diffusion and structural plasticity. Specifically, we introduce a Graph Diffusion (GD)mechanism to facilitate efficient undirected signal propagation, complementing the feedforward hierarchy. Furthermore, it incorporates a Spatio-Temporal Structural Plasticity (STSP) mechanism, endowing the network with the capability for instance-specific, dynamic topological reorganization, thereby overcoming the limitations of fixed topologies. Experiments demonstrate that MorphSNN achieves state-of-the-art accuracy on static and neuromorphic datasets; for instance, it reaches 83.35% accuracy on N-Caltech101 with only 5 timesteps. More importantly, its self-evolving topology functions as an intrinsic distribution fingerprint, enabling superior Out-of- Distribution (OOD) detection without auxiliary training. The code is available at anonymous.4open.science/r/MorphSNN-B0BC.

MorphSNN: Adaptive Graph Diffusion and Structural Plasticity for Spiking Neural Networks

Abstract

Spiking Neural Networks (SNNs) currently face a critical bottleneck: while individual neurons exhibit dynamic biological properties, their macro-scopic architectures remain confined within conventional connectivity patterns that are static and hierarchical. This discrepancy between neuron-level dynamics and network-level fixed connectivity eliminates critical brain-like lateral interactions, limiting adaptability in changing environments. To address this, we propose MorphSNN, a backbone framework inspired by biological non-synaptic diffusion and structural plasticity. Specifically, we introduce a Graph Diffusion (GD)mechanism to facilitate efficient undirected signal propagation, complementing the feedforward hierarchy. Furthermore, it incorporates a Spatio-Temporal Structural Plasticity (STSP) mechanism, endowing the network with the capability for instance-specific, dynamic topological reorganization, thereby overcoming the limitations of fixed topologies. Experiments demonstrate that MorphSNN achieves state-of-the-art accuracy on static and neuromorphic datasets; for instance, it reaches 83.35% accuracy on N-Caltech101 with only 5 timesteps. More importantly, its self-evolving topology functions as an intrinsic distribution fingerprint, enabling superior Out-of- Distribution (OOD) detection without auxiliary training. The code is available at anonymous.4open.science/r/MorphSNN-B0BC.
Paper Structure (45 sections, 2 theorems, 37 equations, 8 figures, 9 tables, 1 algorithm)

This paper contains 45 sections, 2 theorems, 37 equations, 8 figures, 9 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Typical Hierarchical Structure based SNNs
  4. Static Paradigms in Conventional SNNs
  5. Preliminaries
  6. LIF Model
  7. GCN and GAT
  8. Methodology
  9. Overview of MorphSNN
  10. Spatio-Temporal Structure Plasticity
  11. Graph Diffusion
  12. Theoretical Analysis
  13. Experiments
  14. Overall Performance
  15. Ablation Studies
  16. ...and 30 more sections

Key Result

Proposition 4.1

Symmetry of $\mathbf{P}^{(t)}$ is sufficient for the diffusion to constitute a valid gradient flow on $\mathcal{E}_{\text{Dir }}$. This ensures real spectra, eliminating non-physical oscillations and guaranteeing stability of the gradient flow dynamics.

Figures (8)

  • Figure 1: The overall framework of MorphSNN. Input spikes flow from the direct encoding layer through $L$ stacked DGD-SNN layers to the final classifier. Top Left: The model exhibits robustness against noise, with orange (In-Distribution, ID) and blue (OOD) samples activating distinct graph structures for OOD detection. Top Center: Each node is implemented as a ConvBNSN triplet. Bottom Left: The GD executes undirected signal propagation using the Laplacian operator. Bottom Center: The STSP strengthens synapses via Hebbian plasticity while maintaining stability via homeostatic plasticity, driving the temporal evolution of the entire graph structure (Right).
  • Figure 2: Robustness evaluation of Static and Full models on DVS-Gesture under varying interference intensities.
  • Figure 3: Hyperparameter sensitivity analysis on DVS-Gesture.
  • Figure 4: Training Metrics over Epochs on DVS-Gesture.
  • Figure 5: Visualization of feature maps generated by directed connections (DC) and our graph diffusion (GD).
  • ...and 3 more figures

Theorems & Definitions (2)

  • Proposition 4.1
  • Theorem 4.2