In neuronal systems, inhibition contributes to stabilizing dynamics and regulating pattern
formation. Through developing mean-field theories of neuronal models, using complete …

In neuronal systems, inhibition contributes to stabilizing dynamics and regulating pattern
formation. Through developing mean-field theories of neuronal models, using complete …

A certain degree of inhibition is a common trait of dynamical networks in nature, ranging
from neuronal and biochemical networks, to social and technological networks. We study …

The small-world property in the context of complex networks implies structural benefits to the
processes taking place within a network, such as optimal information transmission and …

We study the mean-field limit of a model of biological neuron networks based on the so-
called stochastic integrate-and-fire (IF) dynamics. Our approach allows to derive a …

Neuronal network models often assume a fixed probability of connection between neurons.
This assumption leads to random networks with binomial in-degree and out-degree …

Inhibition is a key aspect of neural dynamics playing a fundamental role for the emergence
of neural rhythms and the implementation of various information coding strategies. Inhibitory …

Up to now, it still remains an open question about the relation between the structure of brain
networks and their functions. The effects of structure on the dynamics of neural networks are …

This article presents a biological neural network model driven by inhomogeneous Poisson
processes accounting for the intrinsic randomness of synapses. The main novelty is the …

We study the dynamics of networks with inhibitory and excitatory leak-integrate-and-fire
neurons with short-term synaptic plasticity in the presence of depressive and facilitating …