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Adaptive rewiring evolves brain-like structure in weighted networks.
Scientific Reports ( IF 4.6 ) Pub Date : 2020-04-08 , DOI: 10.1038/s41598-020-62204-7
Ilias Rentzeperis 1 , Cees van Leeuwen 1, 2
Affiliation  

Activity-dependent plasticity refers to a range of mechanisms for adaptively reshaping neuronal connections. We model their common principle in terms of adaptive rewiring of network connectivity, while representing neural activity by diffusion on the network: Where diffusion is intensive, shortcut connections are established, while underused connections are pruned. In binary networks, this process is known to steer initially random networks robustly to high levels of structural complexity, reflecting the global characteristics of brain anatomy: modular or centralized small world topologies. We investigate whether this result extends to more realistic, weighted networks. Both normally- and lognormally-distributed weighted networks evolve either modular or centralized topologies. Which of these prevails depends on a single control parameter, representing global homeostatic or normalizing regulation mechanisms. Intermediate control parameter values exhibit the greatest levels of network complexity, incorporating both modular and centralized tendencies. The simulation results allow us to propose diffusion based adaptive rewiring as a parsimonious model for activity-dependent reshaping of brain connectivity structure.



中文翻译:

自适应重布线在加权网络中演化出类似于大脑的结构。

依赖活动的可塑性是指适应性重塑神经元连接的一系列机制。我们根据网络连接的自适应重新布线来建模它们的通用原理,同时通过网络上的扩散表示神经活动:在扩散密集的地方,建立快捷连接,而修剪未充分使用的连接。在二进制网络中,众所周知,此过程可将最初的随机网络稳固地引导到高级别的结构复杂性,从而反映出大脑解剖结构的全局特征:模块化或集中式小世界拓扑。我们调查此结果是否扩展到更现实,更加权的网络。正态分布和对数正态分布的加权网络都可以发展模块化或集中式拓扑。其中哪种优先取决于单个控制参数,代表全球稳态或规范化的调节机制。中间控制参数值表现出最大程度的网络复杂性,结合了模块化和集中式趋势。仿真结果使我们能够提出基于扩散的自适应重布线作为用于大脑连接结构的活动依赖重塑的简约模型。

更新日期:2020-04-08
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