Connectivity and Dynamics of Neuronal Networks as Defined by the Shape of Individual Neurons

TL;DR

This study explores how the shape of individual neurons influences the connectivity and dynamics of neuronal networks, revealing the impact of neuron geometry on network topology and activity propagation.

Contribution

It introduces a simplified neuron model to analyze the relationship between neuron shape and network properties, including connectivity and dynamical behavior.

Findings

Neuron shape affects degree distribution and clustering.

Long-range connections facilitate avalanche propagation.

Network topology varies with the number of dendritic processes.

Abstract

Neuronal networks constitute a special class of dynamical systems, as they are formed by individual geometrical components, namely the neurons. In the existing literature, relatively little attention has been given to the influence of neuron shape on the overall connectivity and dynamics of the emerging networks. The current work addresses this issue by considering simplified neuronal shapes consisting of circular regions (soma/axons) with spokes (dendrites). Networks are grown by placing these patterns randomly in the 2D plane and establishing connections whenever a piece of dendrite falls inside an axon. Several topological and dynamical properties of the resulting graph are measured, including the degree distribution, clustering coefficients, symmetry of connections, size of the largest connected component, as well as three hierarchical measurements of the local topology. By varying the number of processes of the individual basic patterns, we can quantify relationships between the individual neuronal shape and the topological and dynamical features of the networks. Integrate-and-fire dynamics on these networks is also investigated with respect to transient activation from a source node, indicating that long-range connections play an important role in the propagation of avalanches.