Structual Vulnerability of the Nematode Worm Neural Graph

TL;DR

This paper investigates the structural robustness and vulnerability of the C. elegans neural graph under various removal strategies, revealing the impact of different connection types and comparing with canonical models.

Contribution

It provides a novel analysis of the C. elegans neural network's robustness and the effects of different connection types on its connectivity.

Findings

Adding undirected edges reduces components significantly.

The neural graph shows remarkable robustness under random removals.

Comparison with canonical models highlights unique structural features.

Abstract

The number of connected components and the size of the largest connected component are studied under node and edge removal in the connectivity graph of the C. elegans nervous system. By studying the two subgraphs - the directed graph of chemical synapses and the undirected graph of electrical junctions - we observe that adding a small number of undirected edges dramatically reduces the number of components in the complete graph. Under random node and edge removal, the C. elegans graph displays a remarkable structural robustness. We then compare these results with the vulnerability of a number of canonical graph models.