大规模模体嵌入尖峰神经网络的随机动力学

中文摘要 AI 辅助

我们研究了局部模体结构和全局网络拓扑如何共同塑造随机神经网络中的尖峰动力学。使用具有 Erdős-Rényi (ER) 和无标度 (SF) 背景连接的 Izhikevich 神经元网络，我们在噪声驱动和刺激驱动协议下，将模体嵌入网络与突触数量匹配的非模体对照网络进行了比较。模体嵌入在两种拓扑中都增加了噪声诱导的相干性，并在信号传输方面提供了较小的改进，而基于 SF 的网络比基于 ER 的网络获得了更大的绝对相干性，并且信号传输更快、更可靠。模体重连实验表明，这种益处取决于模体内连接的具体排列，而非仅仅依赖于强的局部连接：随机化模体骨架总体上降低了相干性，尽管重连非递归前馈环和双平行模体可以增加相干性。定向消融表明，SF 优势部分依赖于枢纽完整性；移除高度兴奋性神经元对相干性和信号传输的损害大于匹配的随机节点或边移除。综合来看，这些结果表明，局部模体和全局枢纽对相干尖峰动力学做出了不同且互补的贡献。

英文摘要

We examine how local motif structure and global network topology jointly shape spiking dynamics in stochastic neuronal networks. Using networks of Izhikevich neurons with Erdős-Rényi (ER) and scale-free (SF) background connectivity, we compare motif-embedded networks with synapse-count-matched, non-motif controls under noise- and stimulus-driven protocols. Motif embedding increases noise-induced coherence in both topologies and provides a smaller improvement in signal transmission, while SF-based networks attain greater absolute coherence and transmit signals faster and more reliably than ER-based networks. Motif-rewiring experiments show that this benefit depends on the specific arrangement of intra-motif connections rather than on strong local connectivity alone: randomizing the motif skeleton reduces coherence overall, although rewiring the non-recurrent feed-forward loop and bi-parallel motif can increase it. Targeted ablation shows that the SF advantage depends in part on hub integrity; removing high-degree excitatory neurons impairs coherence and signal transmission more than matched random node or edge removal. Together, these results indicate that local motifs and global hubs make distinct, complementary contributions to coherent spiking dynamics.

发表机构

• Toronto Metropolitan University（多伦多都会大学）
• St. Michael’s Hospital, Unity Health Toronto（圣迈克尔医院，联合健康多伦多）
• Florida State University（佛罗里达州立大学）

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