Suppression of Phase Synchronization in Scale-Free Neural Networks Using External Pulsed Current Protocols

Boaretto, B. R. R.; Budzinski, Roberto C.; Prado, Thiago de Lima; Lopes, S. R.

doi:10.3390/mca24020046

Cited by 3 publications

(1 citation statement)

References 52 publications

(97 reference statements)

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“…Networks with the HB model have already had their synchronization characteristics studied in detail for different coupling strengths or temperatures in a small-world and scale-free topologies [29,30,31,32,33,34,35,36]. A transition from desynchronization, for very low coupling, to chaotic burst synchronization, for sufficiently strong coupling, was generally observed.…”

Section: Introductionmentioning

confidence: 99%

Effects of neuronal variability on phase synchronization of neural networks

Rossi,

Budzisnki,

Silveira

et al. 2020

Preprint

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An important idea in neural information processing is the communication-through-coherence hypothesis, according to which communication between two brain regions is effective only if they are phase-locked. Also of importance is neuronal variability, a phenomenon in which a single neuron's inter-firing times may be highly variable. In this work, we aim to connect these two ideas by studying the effects of that variability on the capability of neurons to reach phase synchronization. We simulate a network of modified-Hodgkin-Huxley-bursting neurons possessing a small-world topology. First, variability is shown to be correlated with the average degree of phase synchronization of the network. Next, restricting to spatial variability -which measures the deviation of firing times between all neurons in the network -we show that it is positively correlated to a behavior we call promiscuity, which is the tendency of neurons to to have their relative phases change with time. This relation is observed in all cases we tested, regardless of the degree of synchronization or the strength of the inter-neuronal coupling: high variability implies high promiscuity (low duration of phase-locking), even if the network as a whole is synchronized and the coupling is strong. We argue that spatial variability actually generates promiscuity. Therefore, we conclude that variability has a strong influence on both the degree and the manner in which neurons phase synchronize, which is another reason for its relevance in neural communication.

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Section: Introductionmentioning

confidence: 99%

Effects of neuronal variability on phase synchronization of neural networks

Rossi,

Budzisnki,

Silveira

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

Analysis of the dynamical behavior of discrete memristor-coupled scale-free neural networks

Deng,

2024

Chinese Journal of Physics

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Phase-locking intermittency induced by dynamical heterogeneity in networks of thermosensitive neurons

Rossi

Budzinski

Boaretto

et al. 2021

Chaos: An Interdisciplinary Journal of Nonlinear Science

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In this work, we study the phase synchronization of a neural network and explore how the heterogeneity in the neurons’ dynamics can lead their phases to intermittently phase-lock and unlock. The neurons are connected through chemical excitatory connections in a sparse random topology, feel no noise or external inputs, and have identical parameters except for different in-degrees. They follow a modification of the Hodgkin–Huxley model, which adds details like temperature dependence, and can burst either periodically or chaotically when uncoupled. Coupling makes them chaotic in all cases but each individual mode leads to different transitions to phase synchronization in the networks due to increasing synaptic strength. In almost all cases, neurons’ inter-burst intervals differ among themselves, which indicates their dynamical heterogeneity and leads to their intermittent phase-locking. We argue then that this behavior occurs here because of their chaotic dynamics and their differing initial conditions. We also investigate how this intermittency affects the formation of clusters of neurons in the network and show that the clusters’ compositions change at a rate following the degree of intermittency. Finally, we discuss how these results relate to studies in the neuroscience literature, especially regarding metastability.

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Suppression of Phase Synchronization in Scale-Free Neural Networks Using External Pulsed Current Protocols

Cited by 3 publications

References 52 publications

Effects of neuronal variability on phase synchronization of neural networks

Effects of neuronal variability on phase synchronization of neural networks

Analysis of the dynamical behavior of discrete memristor-coupled scale-free neural networks

Phase-locking intermittency induced by dynamical heterogeneity in networks of thermosensitive neurons

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