发表机构
Toronto Metropolitan University; University of Toronto; Florida State University; Institute of Molecular Biophysics, Florida State University; St. Michael’s Hospital, Unity Health Toronto（多伦多都会大学; 多伦多大学; 佛罗里达州立大学; 佛罗里达州立大学分子生物物理研究所; 圣迈克尔医院（联合健康多伦多））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究构建六类模体的脉冲网络，发现中等噪声下相干共振最强，双向耦合对和递归前馈环模体高相干，重连模体显著降相干，表明局部连接排列在嵌入后仍影响群体同步。
AI 中文摘要
神经元网络表现出由连接性和随机输入塑造的复杂动力学。实证研究表明，神经元网络包含重复出现的子图，即模体，但不同模体类型在嵌入大型随机网络后的集体影响仍不甚明了。我们构建了一个由六种代表性结构类别组成的脉冲神经网络，并考察了内在噪声、耦合强度、模体间连接性、网络大小和神经元异质性如何塑造同步。模体层面和网络层面的相干性均在中等噪声强度时达到峰值，这与相干共振一致。双向耦合对（M2）和2型递归前馈环（M3c）始终表现出高的模体层面相干性。在保持局部突触数量和强度的情况下，重新连接这些模体可产生网络相干性的一些最大降幅，表明连接排列的贡献超出了强局部耦合本身。M2和M3c还表现出频繁的尖峰双峰，将短尖峰间隔与升高的相干性联系起来。相对于度和权重匹配的随机对照，模体结构网络在较弱噪声下达到更高的相干性。增大网络大小会增强相干性，直至响应开始饱和，而施加电流的异质性会降低峰值相干性并将最优值移向更强噪声，但不会消除模体类别间的相对差异。这些结果表明，局部连接排列在嵌入后仍具有动力学意义，并可在群体层面塑造噪声驱动的相干性。
英文摘要
Neuronal networks exhibit complex dynamics shaped by connectivity and stochastic input. Empirical studies show that neuronal networks contain recurring subgraphs, or motifs, but the collective influence of different motif types after embedding in large stochastic networks remains less well understood. We construct a spiking network composed of six representative structural classes and examine how intrinsic noise, coupling strength, inter-motif connectivity, network size, and neuronal heterogeneity shape synchronization. Both motif- and network-level coherence peak at intermediate noise intensities, consistent with coherence resonance. Bidirectionally coupled pairs (M2) and the type-2 recurrent feed-forward loop (M3c) consistently exhibit high motif-level coherence. Rewiring these motifs while preserving local synapse number and strength produces some of the largest reductions in network coherence, showing that connection arrangement contributes beyond strong local coupling alone. M2 and M3c also exhibit frequent spike doublets, linking short inter-spike intervals with elevated coherence. Relative to a degree- and weight-matched random control, the motif-structured network reaches greater coherence at weaker noise. Increasing network size enhances coherence until the response begins to saturate, whereas applied-current heterogeneity lowers peak coherence and shifts the optimum toward stronger noise without erasing the relative differences among motif classes. These results show that local connection arrangement remains dynamically consequential after embedding and can shape noise-driven coherence at the population level.
