Impact of noise and damage on collective dynamics of scale-free neuronal networks

TL;DR

This study investigates how scale-free network topology and noise influence neuronal collective dynamics, revealing enhanced noise effects, robustness to random damage, and vulnerability to targeted attacks.

Contribution

It provides analytical and simulation insights into how scale-free structures affect neuronal activity, especially under noise and damage conditions.

Findings

Noise strongly influences activity in scale-free networks with divergent degree distribution second moment.

Scale-free networks sustain oscillations despite random neuron removal.

Targeted removal of hubs disrupts network oscillations significantly.

Abstract

We study the role of scale-free structure and noise in collective dynamics of neuronal networks. For this purpose, we simulate and study analytically a cortical circuit model with stochastic neurons. We compare collective neuronal activity of networks with different topologies: classical random graphs and scale-free networks. We show that, in scale-free networks with divergent second moment of degree distribution, an influence of noise on neuronal activity is strongly enhanced in comparison with networks with a finite second moment. A very small noise level can stimulate spontaneous activity of a finite fraction of neurons and sustained network oscillations. We demonstrate tolerance of collective dynamics of the scale-free networks to random damage in a broad range of the number of randomly removed excitatory and inhibitory neurons. A random removal of neurons leads to gradual decrease of frequency of network oscillations similar to the slowing-down of the alpha rhythm in Alzheimer's disease. However, the networks are vulnerable to targeted attacks. A removal of a few excitatory or inhibitory hubs can impair sustained network oscillations.