Zhao Hongfei, Shi Zhiguo, Gong Zhefeng, He Shibo
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.
Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province, Zhejiang University, Hangzhou 310058, China.
Entropy (Basel). 2022 Dec 27;25(1):51. doi: 10.3390/e25010051.
Knowledge of the structural properties of biological neural networks can help in understanding how particular responses and actions are generated. Recently, Witvliet et al. published the connectomes of eight isogenic hermaphrodites at different postembryonic ages, from birth to adulthood. We analyzed the basic structural properties of these biological neural networks. From birth to adulthood, the asymmetry between in-degrees and out-degrees over the neuronal network increased with age, in addition to an increase in the number of nodes and edges. The degree distributions were neither Poisson distributions nor pure power-law distributions. We have proposed a model of network evolution with different initial attractiveness for in-degrees and out-degrees of nodes and preferential attachment, which reproduces the asymmetry between in-degrees and out-degrees and similar degree distributions via the tuning of the initial attractiveness values. In this study, we present the well-preserved structural properties of neuronal networks across development, and provide some insight into understanding the evolutionary processes of biological neural networks through a simple network model.
了解生物神经网络的结构特性有助于理解特定反应和行为是如何产生的。最近，维特弗利特等人发表了八个不同胚胎后年龄（从出生到成年）的同基因雌雄同体生物的连接组。我们分析了这些生物神经网络的基本结构特性。从出生到成年，除了节点和边的数量增加外，神经网络中入度和出度之间的不对称性也随年龄增长。度分布既不是泊松分布也不是纯幂律分布。我们提出了一个网络进化模型，该模型对节点的入度和出度具有不同的初始吸引力，并进行优先连接，通过调整初始吸引力值来再现入度和出度之间的不对称性以及相似的度分布。在本研究中，我们展示了发育过程中神经网络保存完好的结构特性，并通过一个简单的网络模型为理解生物神经网络的进化过程提供了一些见解。
