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

Protachevicz, Paulo R.; Borges, Rafael R.; Reis, Adriane S.; Borges, Fernando S.; Iarosz, Kelly C.; Caldas, Iberê L.; Lameu, Ewandson L.; Macau, Elbert E. N.; Viana, Ricardo L.; Sokolov, I. M.; Ferrari, Fernanda; Kurths, Jürgen; Batista, Antônio M.; Lo, Chun Yi Zac; He, Yong; Lin, Congping

doi:10.1088/1361-6579/aace91

Cited by 25 publications

(15 citation statements)

References 36 publications

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“…In the internal connection scheme, each network node can be understood as a neuron and their connections as the edges [8], which are able to simulate a single network, as used in many works [9][10][11][12][13]. On the other hand, considering the inter-networks connection scheme, it is possible to consider a neural system composed of different sub-areas, so each sub-network can be understood as a node and their connections as the edges, building a network of networks [14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Details in the Transition to Synchronization in Complex Networks by Using Recurrence Analysis

Budzinski

Boaretto

Prado

et al. 2019

MCA

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The study of synchronization in complex networks is useful for understanding a variety of systems, including neural systems. However, the properties of the transition to synchronization are still not well known. In this work, we analyze the details of the transition to synchronization in complex networks composed of bursting oscillators under small-world and scale-free topologies using recurrence quantification analysis, specifically the determinism. We demonstrate the existence of non-stationarity states in the transition region. In the small-world network, the transition region denounces the existence of two-state intermittency.

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Section: Introductionmentioning

confidence: 99%

Investigation of Details in the Transition to Synchronization in Complex Networks by Using Recurrence Analysis

Budzinski

Boaretto

Prado

et al. 2019

MCA

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“…where σ ISI and ISI are the standard deviation and the mean value of ISI, respectively. We identify spike activities when CV < 0.5 and burst activities when CV ≥ 0.5 (Protachevicz et al, 2018). We calculate the mean firing frequency F(Hz) of the neuronal network by mean of the expression…”

Section: Model and Methodsmentioning

confidence: 99%

“…where σ ISI and

are the standard deviation and the mean value of ISI, respectively. We identify spike activities when

05

and burst activities when

05

(Protachevicz et al, 2018 ).…”

Section: Model and Methodsmentioning

confidence: 99%

Influence of Delayed Conductance on Neuronal Synchronization

et al. 2020

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“…We consider the adaptive, exponential integrateand-fire (AdEx) [38] model with cortical neurons modelled as regular spiking (RS) cells with spike frequency adaptation and fast-spiking (FS) cells with a negligible level of adaptation. In such networks, depending on the excitatory synaptic strength, neurons can exhibit a transition from spiking to bursting synchronisation [39,40] and bistable firing patterns [41]. We find conditions in which unstructured, sparsely connected random networks of AdEx neurons can display low frequency, self-sustained activity.…”

Section: Introductionmentioning

confidence: 93%

Self-sustained activity of low firing rate in balanced networks

Borges

Protachevicz

Pena

et al. 2020

Physica A: Statistical Mechanics and its Applications

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stimuli and contributes to signal propagation, neural coding, and dynamic stability. It also plays an important role in cognitive processes. In this work, by means of studying intracellular recordings from CA1 neurons in rats and results from numerical simulations, we demonstrate that self-sustained activity presents high variability of patterns, such as low neural firing rates and activity in the form of small-bursts in distinct neurons. In our numerical simulations, we consider random networks composed of coupled, adaptive exponential integrate-and-fire neurons. The neural dynamics in the random networks simulates regular spiking (excitatory) and fast spiking (inhibitory) neurons. We show that both the connection probability and network size are fundamental properties that give rise to self-sustained activity in qualitative agreement with our experimental results. Finally, we provide a more detailed description of self-sustained activity in terms of lifetime distributions, synaptic conductances, and synaptic currents.

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

Abstract: 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.

Cited by 25 publications

References 36 publications

Investigation of Details in the Transition to Synchronization in Complex Networks by Using Recurrence Analysis

Investigation of Details in the Transition to Synchronization in Complex Networks by Using Recurrence Analysis

Influence of Delayed Conductance on Neuronal Synchronization

Self-sustained activity of low firing rate in balanced networks

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