Loeffler Alon, Zhu Ruomin, Hochstetter Joel, Li Mike, Fu Kaiwei, Diaz-Alvarez Adrian, Nakayama Tomonobu, Shine James M, Kuncic Zdenka
School of Physics, The University of Sydney, Sydney, NSW, Australia.
Central Clinical School, The University of Sydney, Sydney, NSW, Australia.
Front Neurosci. 2020 Mar 6;14:184. doi: 10.3389/fnins.2020.00184. eCollection 2020.
Graph theory has been extensively applied to the topological mapping of complex networks, ranging from social networks to biological systems. Graph theory has increasingly been applied to neuroscience as a method to explore the fundamental structural and functional properties of human neural networks. Here, we apply graph theory to a model of a novel neuromorphic system constructed from self-assembled nanowires, whose structure and function may mimic that of human neural networks. Simulations of neuromorphic nanowire networks allow us to directly examine their topology at the individual nanowire-node scale. This type of investigation is currently extremely difficult experimentally. We then apply network cartographic approaches to compare neuromorphic nanowire networks with: random networks (including an untrained artificial neural network); grid-like networks and the structural network of . Our results demonstrate that neuromorphic nanowire networks exhibit a small-world architecture similar to the biological system of , and significantly different from random and grid-like networks. Furthermore, neuromorphic nanowire networks appear more segregated and modular than random, grid-like and simple biological networks and more clustered than artificial neural networks. Given the inextricable link between structure and function in neural networks, these results may have important implications for mimicking cognitive functions in neuromorphic nanowire networks.
图论已被广泛应用于复杂网络的拓扑映射，从社交网络到生物系统。图论作为一种探索人类神经网络基本结构和功能特性的方法，在神经科学中的应用越来越多。在此，我们将图论应用于一个由自组装纳米线构建的新型神经形态系统模型，该系统的结构和功能可能模拟人类神经网络。对神经形态纳米线网络的模拟使我们能够在单个纳米线节点尺度上直接检查其拓扑结构。目前，这种类型的研究在实验上极其困难。然后，我们应用网络制图方法将神经形态纳米线网络与以下网络进行比较：随机网络（包括未经训练的人工神经网络）；网格状网络以及[此处原文缺失相关网络名称]的结构网络。我们的结果表明，神经形态纳米线网络呈现出与[此处原文缺失相关生物系统名称]生物系统相似的小世界架构，且与随机网络和网格状网络有显著差异。此外，神经形态纳米线网络比随机网络、网格状网络和简单生物网络显得更具隔离性和模块化，比人工神经网络更具聚类性。鉴于神经网络中结构与功能之间存在密不可分的联系，这些结果可能对模拟神经形态纳米线网络中的认知功能具有重要意义。
