Noise-induced up/down dynamics in scale-free neuronal networks

Abstract

Cortical neuronal networks are known to exhibit regimes of dynamical activity characterized by periods of elevated firing (up states) separated by silent phases (down states). Here, we show that up/down dynamics may emerge spontaneously in scale-free neuronal networks, provided an optimal amount of noise acts upon all network nodes. Our conclusions are drawn from numerical simulations of networks of subthreshold integrate-and-fire neurons, connected to each other according to a scale-free topology. We study the structure of the up/down regime both in time and in terms of the node degree. We also examine whether localized random perturbations applied to specific network nodes are able to generate up/down dynamics, showing that this regime arises when noisy inputs are applied to low-degree (nonhub) network nodes, but not when they act upon network hubs.