Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China; Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, 200240, China.
Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China; Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, 200240, China.
Biochem Biophys Res Commun. 2022 Oct 8;624:112-119. doi: 10.1016/j.bbrc.2022.07.108. Epub 2022 Aug 1.
Revealing the organizing principles of developing neural networks is a difficult but significant task in neuroscience. As a creature with a rather compact and well-studied neural network, C. elegans is an ideal subject for neuroscience study. However, the researches on its developing neural network remain challenging. The changes in specific properties of neural network across development may uncover part of its principles. Motif is a typical structure property that can be well applied to various complex networks. Here, we study the motif changes in C. elegans neural network across development. By counting the occurrence number of all three-node subgraph motif structures in its neural network at different stages of C. elegans development, along with those in corresponding random networks, we determine which of these structures are motifs for C. elegans, finding out the regular changes of motifs during its development. Combined with the potential function of these subgraph motifs and synaptic information, we gain insight into the organizing principle of neural network during development, which may increase our understanding of neuroscience and inspire the construction of artificial neural network.
揭示神经网络的组织原则是神经科学中的一个难题,但也是一项重要任务。秀丽隐杆线虫(C. elegans)作为一种具有相对紧凑且研究良好的神经网络的生物,是神经科学研究的理想对象。然而,对其发育中的神经网络的研究仍然具有挑战性。神经网络在发育过程中特定性质的变化可能揭示其部分组织原则。模式是一种典型的结构性质,可以很好地应用于各种复杂网络。在这里,我们研究了秀丽隐杆线虫神经网络在发育过程中的模式变化。通过统计秀丽隐杆线虫发育过程中不同阶段其神经网络中所有三节点子图模式结构的出现次数,以及相应的随机网络中的出现次数,我们确定了哪些结构是秀丽隐杆线虫的模式,发现了模式在其发育过程中的有规律变化。结合这些子图模式的潜在功能和突触信息,我们深入了解了神经网络在发育过程中的组织原则,这可能会增进我们对神经科学的理解,并启发人工神经网络的构建。