Analysing properties of the C. Elegans neural network: mathematically modeling a biological system

TL;DR

This paper analyzes the structural properties of the C. elegans neural network using mathematical tools like Laplacian matrices, eigenvalue analysis, and visualization to understand its fractal-like features and small-world characteristics.

Contribution

The study applies eigenvalue counting, visualization, and comparative analysis to reveal fractal and small-world properties of the C. elegans neural network, advancing structural understanding.

Findings

Identification of fractal-like self similarity in the neural network

Evidence of small-world properties such as short average path length and high clustering

Eigenfunction localization and network visualization insights

Abstract

The brain is one of the most studied and highly complex systems in the biological world. It is the information center behind all vertebrate and most invertebrate life, and thus has become a major focus in current research. While many of these studies have concentrated on studying the brain directly, our focus is the structure of the brain itself: at its core an interconnected network of nodes (neurons). A better understanding of the structural aspects of the brain should elucidate some of its functional properties. In this paper we analyze the brain of the nematode Caenorhabditis elegans. Consisting of only 302 neurons, it is one of the better-understood neural networks. Using a Laplacian matrix of the 279-neuron "giant component" of the network, we use an eigenvalue counting function to look for fractal-like self similarity. This matrix representation is also used to plot (in eigenfunction coordinates) both 2 and 3 dimensional visualizations of the neural network. Further analysis examines the small-world properties of the system, including average path length and clustering coefficient. We then test for localization of eigenfunctions, using graph energy and spacial variance. To better understand these results, all of these calculations are also performed on random networks, branching trees, and known fractals, as well as fractals which have been "rewired" to have small-world properties. This analysis is one of many stepping-stones in the research of neural networks. While many of the structures and functions within the brain are known, understanding how the two interact is also important. A firmer grasp on the structural properties of the neural network is a key step in this process