Balanced condition in networks leads to Weibull statistics

Abstract

The importance of the balance in inhibitory and excitatory couplings in the brain has increasingly been realized. Despite the key role played by inhibitory-excitatory couplings in the functioning of brain networks, the impact of a balanced condition on the stability properties of underlying networks remains largely unknown. We investigate properties of the largest eigenvalues of networks having such couplings, and find that they follow completely different statistics when in the balanced situation. Based on numerical simulations, we demonstrate that the transition from Weibull to Fréchet via the Gumbel distribution can be controlled by the variance of the column sum of the adjacency matrix, which depends monotonically on the denseness of the underlying network. As a balanced condition is imposed, the largest real part of the eigenvalue emulates a transition to the generalized extreme value statistics, independent of the inhibitory connection probability. Furthermore, the transition to the Weibull statistics and the small-world transition occur at the same rewiring probability, reflecting a more stable system.

7 pages, 10 figures

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Cited by in corpus (5)

• Optimized evolution of networks for principal eigenvector localization
• Localization of multilayer networks by the optimized single-layer rewiring
• Emergence of (bi)multi-partiteness in networks having inhibitory and excitatory couplings
• Interplay of inhibition and multiplexing : Largest eigenvalue statistics
• Extreme matrices or how an exponential map links classical and free extreme laws