Hocking Alex, Vogelsang Lena, Jiang Frank, Le Hung, Morgan Kerri, Parsons Nicholas, Poudel Govinda, Shelyag Sergiy, Ugon Julien
School of IT, Faculty of Science, Engineering & Built Environment, Deakin University, Melbourne, Australia.
Applied AI Institute, Deakin University, Geelong, Australia.
Sci Rep. 2025 Jun 3;15(1):19480. doi: 10.1038/s41598-025-04347-z.
A major question in neuroscience is how the brain structure facilitates its complex functions via the synchronous activity of separate parts of the brain. Recent research has indicated that the mechanisms underlying this facilitation are based on local interactions between neurons. We investigated whether it is possible in a network structured like the human brain to facilitate communication between functionally connected parts of the brain. We observed that using a combination of local criteria to direct it, a simple greedy algorithm can achieve similar efficiency (within 80% and 90% of the shortest paths) to the propagation mechanisms proposed in the literature, without relying on global explanations.
神经科学中的一个主要问题是大脑结构如何通过大脑不同部分的同步活动来促进其复杂功能。最近的研究表明，这种促进作用的潜在机制基于神经元之间的局部相互作用。我们研究了在一个类似于人类大脑结构的网络中，是否有可能促进大脑功能连接部分之间的通信。我们观察到，使用局部标准的组合来引导它，一种简单的贪婪算法可以达到与文献中提出的传播机制相似的效率（在最短路径的80%到90%之间），而无需依赖全局解释。
