Impact of noise and damage on collective dynamics of scale-free neuronal networks
arXiv:1211.6894 · doi:10.1103/PhysRevE.87.032717
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.
12 pages, 10 figures
References in corpus (9)
- Statistical physics of social dynamics
- Critical phenomena in complex networks
- Scale-free brain functional networks
- Emergent complex neural dynamics
- The Rich-Club Phenomenon In The Internet Topology
- Navigability of Complex Networks
- Percolation in living neural networks
- The Physics of Living Neural Networks
- Evolution of scale-free random graphs: Potts model formulation