Neuronal motifs reveal backbone structure and influential neurons of neural network in C. elegans

Abstract

Neural network elements such as motif, backbone and influential nodes play important roles in neural network computation. Increasing researches have been applying complex network methods in order to identify different essential structures within complex neural networks. However, the distinct properties of synapses that build the neural network are often neglected, such as the difference between chemical synapses and electrical synapses. By separating these distinct synapses, we can identify a novel repertoire of neural motifs and greatly expand neural motif families in neural systems. Based on the expanded motif families, we further propose a novel neural-motif-based algorithm to extract the backbone in the neural network. The backbone circuit we extracted from Caenorhabditis elegans connectome controls an essential motor behaviour in C. elegans. Furthermore, we develop a novel neural-motif-based algorithm to identify influential neurons. Compared with the influential neurons identified using existing methods, the neurons identified in this work provide more information in related to their functions. These methods have been successfully applied to identify a series of network features in C. elegans, providing a biologically interpretable way of exploring the structure of neural network.