Rafael R. Borges

Suppression of phase synchronisation in network based on cat's brain

We have studied the effects of perturbations on the cats cerebral cortex. According to the literature, this cortex structure can be described by a clustered network. This way, we construct a clustered network with the same number of areas as in the cat matrix, where each area is described as a sub-network with a small-world property. We focus on the suppression of neuronal phase synchronisation considering different kinds of perturbations. Among the various controlling interventions, we choose three methods: delayed feedback control, external time-periodic driving, and activation of selected neurons. We simulate these interventions to provide a procedure to suppress undesired and pathological abnormal rhythms that can be associated with many forms of synchronisation. In our simulations, we have verified that the efficiency of synchronisation suppression by delayed feedback control is higher than external time-periodic driving and activation of selected neurons of the cats cerebral cortex with the same coupling strengths.

Spike timing-dependent plasticity induces non-trivial topology in the brain

We study the capacity of Hodgkin-Huxley neuron in a network to change temporarily or permanently their connections and behavior, the so called spike timing-dependent plasticity (STDP), as a function of their synchronous behavior. We consider STDP of excitatory and inhibitory synapses driven by Hebbian rules. We show that the final state of networks evolved by a STDP depend on the initial network configuration. Specifically, an initial all-to-all topology evolves to a complex topology. Moreover, external perturbations can induce co-existence of clusters, those whose neurons are synchronous and those whose neurons are desynchronous. This work reveals that STDP based on Hebbian rules leads to a change in the direction of the synapses between high and low frequency neurons, and therefore, Hebbian learning can be explained in terms of preferential attachment between these two diverse communities of neurons, those with low-frequency spiking neurons, and those with higher-frequency spiking neurons.

Recurrence quantification analysis for the identification of burst phase synchronisation

In this work, we apply the spatial recurrence quantification analysis (RQA) to identify chaotic burst phase synchronisation in networks. We consider one neural network with small-world topology and another one composed of small-world subnetworks. The neuron dynamics is described by the Rulkov map, which is a two-dimensional map that has been used to model chaotic bursting neurons. We show that with the use of spatial RQA, it is possible to identify groups of synchronised neurons and determine their size. For the single network, we obtain an analytical expression for the spatial recurrence rate using a Gaussian approximation. In clustered networks, the spatial RQA allows the identification of phase synchronisation among neurons within and between the subnetworks. Our results imply that RQA can serve as a useful tool for studying phase synchronisation even in networks of networks.

Synchronous behaviour in network model based on human cortico-cortical connections

OBJECTIVE We consider a network topology according to the cortico-cortical connection network of the human brain, where each cortical area is composed of a random network of adaptive exponential integrate-and-fire neurons. APPROACH Depending on the parameters, this neuron model can exhibit spike or burst patterns. As a diagnostic tool to identify spike and burst patterns we utilise the coefficient of variation of the neuronal inter-spike interval. MAIN RESULTS In our neuronal network, we verify the existence of spike and burst synchronisation in different cortical areas. SIGNIFICANCE Our simulations show that the network arrangement, i.e. its rich-club organisation, plays an important role in the transition of the areas from desynchronous to synchronous behaviours.

Sincronização de disparos em redes neuronais com plasticidade sináptica

In this paper, we investigated the neural spikes synchronisation in a neural network with synaptic plasticity and external perturbation. In the simulations the neural dynamics is described by the Hodgkin Huxley model considering chemical synapses (excitatory) among neurons. According to neural spikes synchronisation is expected that a perturbation produce non synchronised regimes. However, in the literature there are works showing that the combination of synaptic plasticity and external perturbation may generate synchronised regime. This article describes the effect of the synaptic plasticity on the synchronisation, where we consider a perturbation with a uniform distribution. This study is relevant to researches of neural disorders control.

Taxa de disparos em uma rede neural modelada por autômato celular

Estudamos a taxa de disparos em um modelo de automato celular para uma rede neural com sinapses eletricas e quimicas. Foi proposto um modelo em que as conexoes locais na rede representam as sinapses eletricas e, as nao locais simulam as sinapses quimicas. As conexoes nao locais ou atalhos foram inseridos de acordo com o modelo de rede complexa mundo pequeno com uma probabilidade de conexao especificada, tambem sendo possivel a adicao de um tempo de atraso nas simulacoes. O tempo de atraso representa o intervalo de tempo necessario para atualizar a variavel de estado de um neuronio afetado. O mecanismo com sinapses quimicas fornece um comportamento autossustentavel de disparos neuronais, que pode ser periodico ou nao, dependendo da insercao do tempo de atraso. Considerando somente as sinapses eletricas, a dinâmica de disparos decai rapidamente a zero.