神经系统持续调整连接强度并重构其结构，以形成并维持具有重尾权重分布的复杂连接模式。我们提出一个简洁模型，其中结构可塑性与突触可塑性均由共同的扩散动力学驱动。仅靠突触可塑性即可生成重尾权重分布，但前提是神经活动传播主要局限于局部区域；而当突触可塑性与通过适应性重连实现的结构可塑性协同作用时，模型亦可在更广泛的活动传播范围内生成此类分布。此外，适应性重连可产生具有汇聚-发散回路的复杂网络结构。这类回路包含在神经系统中普遍存在的基序，负责情境敏感的信号传递及信噪比提升。本模型在多种动力学状态下均稳健地复现上述结果，并能捕捉秀丽隐杆线虫（C. elegans）与小鼠脑网络的关键连接特征。这些发现表明，其潜在原理在不同复杂度的物种间具有共通性。

The nervous system continuously adjusts connection strengths and reorganizes its structure to form and maintain complex connectivity patterns with heavy-tailed weight distributions. We propose a parsimonious model in which structural and synaptic plasticity are driven by common diffusion dynamics. Synaptic plasticity alone generates heavy-tailed weight distributions, but only when activity spreading remains predominantly local. However, when combined with structural plasticity through adaptive rewiring, the model also generates these distributions with more extensive activity flow. Furthermore, adaptive rewiring produces complex network structures with convergent-divergent circuits. These circuits contain motifs that are pervasive in nervous systems and are responsible for context-sensitive signal propagation and enhanced signal to noise ratios. Our model robustly reproduces these results across diverse dynamical regimes while capturing key connectivity features of both C. elegans and mouse brain networks. These findings suggest that the underlying principles are shared across species of varying complexity.