Suppression of bursting synchronization in clustered scale-free (rich-club) neuronal networks

Abstract

Functional brain networks are composed of cortical areas that are anatomically and functionally connected. One of the cortical networks for which more information is available in the literature is the cat cerebral cortex. Statistical analyses of the latter suggest that its structure can be described as a clustered network, in which each cluster is a scale-free network possessing highly connected hubs. Those hubs are, on their hand, connected together in a strong fashion ("rich-club" network). We have built a clustered scale-free network inspired in the cat cortex structure so as to study their dynamical properties. In this article, we focus on the synchronization of bursting activity of the cortical areas and how it can be suppressed by means of neuron deactivation through suitably applied light pulses. We show that it is possible to effectively suppress bursting synchronization by acting on a single, yet suitably chosen neuron, as long as it is highly connected, thanks to the "rich-club" structure of the network.

Robust synchronization of bursting Hodgkin–Huxley neuronal systems coupled by delayed chemical synapses

“…Usually people use Kuramoto's order parameter as diagnostic of bursting synchronization [28][29][30]. This order parameter could show the occurrence of total synchronization as well as partial synchronization, but could not tell where the boundary is between them.…”

Effects of time-periodic intercoupling strength on burst synchronization of a clustered neuronal network

“…At some situations, we need to suppress it. Lameu and Batista et al [33] investigated how to suppress burst synchronization in clustered scalefree neuronal network. They found that bursting synchronization could be suppressed by deactivating a single neuron which is highly connected in the network.…”

Channel noise effects on neural synchronization

“…However, simulating such a Markov Chain is computationally exhaustive as one must compute the state of each channel individually, which takes a long time when dealing with thousands of channels. In order to account for perturbations in the Hodgkin-Huxley model, some studies have added an external perturbation to the Hodgkin-Huxley equations to assess the role of noise in synchronization [4,25,28]. This method, however, lacks justification that it accurately models the stochastic opening and closing of channels.…”