A Joint Graph Inference Case Study: the C.elegans Chemical and Electrical Connectomes

TL;DR

This paper explores joint inference methods for the chemical and electrical connectomes of C. elegans, demonstrating that combined analysis enhances understanding of neural connectivity with implications for neuroscience.

Contribution

It introduces a joint graph inference framework for analyzing paired connectomes, combining seeded graph matching and vertex classification techniques.

Findings

Joint inference improves connectome analysis accuracy

Analysis in joint space reveals new neuroscientific insights

Method applicable to multi-layer neural networks

Abstract

We investigate joint graph inference for the chemical and electrical connectomes of the \textit{Caenorhabditis elegans} roundworm. The \textit{C.elegans} connectomes consist of $253$ non-isolated neurons with known functional attributes, and there are two types of synaptic connectomes, resulting in a pair of graphs. We formulate our joint graph inference from the perspectives of seeded graph matching and joint vertex classification. Our results suggest that connectomic inference should proceed in the joint space of the two connectomes, which has significant neuroscientific implications.