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.

翻译：脉冲神经网络（SNNs）当前面临一个关键瓶颈：尽管单个神经元展现出动态的生物特性，但其宏观架构仍局限于传统静态、分层的连接模式。神经元层面的动态性与网络层面固定连接性之间的这种差异，消除了关键的类脑横向相互作用，限制了在变化环境中的适应能力。为解决此问题，我们提出了MorphSNN，一个受生物非突触扩散和结构可塑性启发的骨干框架。具体而言，我们引入了一种图扩散（GD）机制，以促进高效的无向信号传播，从而补充前馈层次结构。此外，该框架还融合了一种时空结构可塑性（STSP）机制，赋予网络针对特定实例进行动态拓扑重组的能力，从而克服了固定拓扑结构的局限。实验表明，MorphSNN在静态数据集和神经形态数据集上均达到了最先进的准确率；例如，在N-Caltech101数据集上仅用5个时间步即达到83.35%的准确率。更重要的是，其自演化的拓扑结构作为一种内在的分布指纹，能够在无需辅助训练的情况下实现卓越的分布外（OOD）检测。代码发布于 anonymous.4open.science/r/MorphSNN-B0BC。