Structual Vulnerability of the Nematode Worm Neural Graph

2012/08/16 by Michael Rudolph, Alain Destexhe, Rudolph-Lilith, Michelle +3
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Computational Drug Discovery Methods #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Biological sciences #FOS: Physical sciences #Gene Regulatory Network Analysis #Neurons and Cognition (q-bio.NC) #Origins and Evolution of Life #Physics and Society (physics.soc-ph)

paper · pdf · doi:10.48550/arxiv.1208.3383

openalex publication_date 2012/08/16 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28

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.

Related

• A Joint Graph Inference Case Study: the C.elegans Chemical and Electrical Connectomes
• The distribution of inhibitory neurons in the C. elegans connectome\n facilitates self-optimization of coordinated neural activity
• Analysing properties of the C. Elegans neural network: mathematically\n modeling a biological system
• The growing topology of the C. elegans connectome
• The structure of the nervous system of the nematode Caenorhabditis elegans