彩色图案揭示了C中的计算构建块。秀丽线虫脑
Colored motifs reveal computational building blocks in the C. elegans brain
摘要：
复杂网络通常可以分解为不太复杂的子网络，其结构可以暗示整个网络的功能组织。然而，这些结构图案只能讲述功能故事的一部分，因为在这种分析中，每个节点和边缘都被平等对待。在真实网络中，两个拓扑相同但其节点执行非常不同功能的基元将在网络中扮演非常不同的角色。在这里，我们结合了来自线虫C的神经网络的拓扑结构的结构信息。elegans与有关这些节点的生物功能的信息，从而着色节点的功能。我们发现，特定的着色图案是显着更丰富的蠕虫大脑比预期的机会，并具有特定的计算功能，强调前馈结构的信息处理在网络中，而逃避反馈回路。中间神经元在常见的基序中占很大比例，这支持了这些基序处理并将来自传感器神经元的信息传递给肌肉的观点。在寻找显着的彩色图案时发现的一些最常见的图案在控制蠕虫运动的神经元系统中起着至关重要的作用。根据彩色图案对复杂网络的分析结合了两个独立的数据集，以产生对这些网络的洞察力，这是单独使用任何一个数据集都无法获得的。该方法是通用的，只要布线和功能信息都可用，就可以将任何复杂网络分解为功能（而不是拓扑）图案。
英文摘要：
Complex networks can often be decomposed into less complex sub-networks whose structures can give hints about the functional organization of the network as a whole. However, these structural motifs can only tell one part of the functional story because in this analysis each node and edge is treated on an equal footing. In real networks, two motifs that are topologically identical but whose nodes perform very different functions will play very different roles in the network. Here, we combine structural information derived from the topology of the neuronal network of the nematode C. elegans with information about the biological function of these nodes, thus coloring nodes by function. We discover that particular colorations of motifs are significantly more abundant in the worm brain than expected by chance, and have particular computational functions that emphasize the feed-forward structure of information processing in the network, while evading feedback loops. Interneurons are strongly over-represented among the common motifs, supporting the notion that these motifs process and transduce the information from the sensor neurons towards the muscles. Some of the most common motifs identified in the search for significant colored motifs play a crucial role in the system of neurons controlling the worm's locomotion. The analysis of complex networks in terms of colored motifs combines two independent data sets to generate insight about these networks that cannot be obtained with either data set alone. The method is general and should allow a decomposition of any complex network into its functional (rather than topological) motifs as long as both wiring and functional information is available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
