Temperature dependence of phase and spike synchronization of neural networks

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

doi:10.1016/j.chaos.2019.03.039

Cited by 18 publications

(17 citation statements)

References 40 publications

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“…In fact, this kind of behavior has also been observed in non-identical coupled Rösller oscillators [32]. Recently, the mechanism behind the abnormal synchronization in a neural network composed of bursting neurons is explored and the relationship between the individual neuron behavior and the network synchronization helps to understand the phenomenon [33,34]. In [33], it is shown that the occurring of abnormal synchronization is related to the periodic inter-burst interval of the uncoupled neuron.…”

Section: Introductionmentioning

confidence: 87%

“…In [33], it is shown that the occurring of abnormal synchronization is related to the periodic inter-burst interval of the uncoupled neuron. Besides that, in [34], it is observed that the abnormal synchronization occurs due to an interplay between the periodic individual behavior and the influence of coupling strength, which is strong enough to induce the network to phase synchronization without destroying the influence of individual periodic behavior.…”

Section: Introductionmentioning

confidence: 99%

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

Boaretto

Budzinski

Prado

et al. 2019

MCA

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The synchronization of neurons is fundamental for the functioning of the brain since its lack or excess may be related to neurological disorders, such as autism, Parkinson’s and neuropathies such as epilepsy. In this way, the study of synchronization, as well as its suppression in coupled neurons systems, consists of an important multidisciplinary research field where there are still questions to be answered. Here, through mathematical modeling and numerical approach, we simulated a neural network composed of 5000 bursting neurons in a scale-free connection scheme where non-trivial synchronization phenomenon is observed. We proposed two different protocols to the suppression of phase synchronization, which is related to deep brain stimulation and delayed feedback control. Through an optimization process, it is possible to suppression the abnormal synchronization in the neural network.

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Section: Introductionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

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

Boaretto

Budzinski

Prado

et al. 2019

MCA

Self Cite

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“…The exact way in which the brain enables thought is one of the great mysteries of science. It has been appreciated for thousands of years that the brain is somehow involved in thought [30,31].…”

Section: Knowledge-based Expert Systemsmentioning

confidence: 99%

An Expert System Optimization Model for Desktop Computers

Guanzon¹,

Caberte²,

Rosa³

2019

IJATCSE

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This study is about the collection of knowledge from experts in the field of computer diagnostics and storing it into an accessible database where it is available for others to use. The availability of expertise in diagnostics is needed by the non-experts.The program serves as a guide tool for computer shop owners to give them the capability to repair defective computers without having the physical presence of a technician by using the stored knowledge in the database and following the step-by-step procedures.

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“…[1,2,7,15,16] extended this model to study the chaotic phase synchronization and burst synchronization and proved that discrete maps are superior to study the network behaviour of neuronal system. The major challenge is, system stability not matching with the map stability and results with restriction to study the delay nature and temperature effects on the neuron synapse and network [17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A Discrete Huber-braun Neuron Model: From Nodal Properties to Network Performance

Rajagopal²,

Karthikeyan

et al. 2021

Preprint

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Many of the well-known neuron models are continuous time systems with complex mathematical definitions. Literatures have shown that a discrete mathematical model can effectively replicate the complete dynamical behaviour of a neuron with much reduced complexity. Hence, we propose a new discrete neuron model derived from the Huber-Braun neuron with two additional slow and subthreshold currents alongside the ion channel currents. We have also introduced temperature dependent ion channels to study its effects on the firing pattern of the neuron. With bifurcation and Lyapunov exponents we showed the chaotic and periodic regions of the discrete model. Further to study the complexity of the neuron model, we have used the sample entropy algorithm. Though the individual neuron analysis gives us an idea about the dynamical properties, it’s the collective behaviour which decides the overall behavioural pattern of the neuron. Hence, we investigate the spatiotemporal behaviour of the discrete neuron model in single- and two-layer network. We have considered noise and obstacles as the two important factor which changes the excitability of the neurons in the network. When there is no noise or obstacle, the network display simple wave propagation with highly excitable neurons. Literatures have shown that spiral waves can play a positive role in breaking through quiescent areas of the brain as a pacemaker by creating a coherence resonance behaviour. Hence, we are interested in studying the induced spiral waves in the network. In this condition when an obstacle is introduced the wave propagation is disturbed and we could see multiple wave re-entry and spiral waves. Now when we consider only noise with no obstacle, for selected noise variances the network supports wave re-entry. By introducing an obstacle in this noisy network, the re-entry soon disappears, and the network soon enters idle state with no resetting. In a two-layer network when the obstacle is considered only in one layer and stimulus applied to the layer having the obstacle, the wave re-entry is seen in both the layer though the other layer is not exposed to obstacle. But when both the layers are inserted with an obstacle and stimuli also applied to the layers, they behave like independent layers with no coupling effect. This in a two-layer network stimulus play an important role in spatiotemporal dynamics of the network. Similar noise effects like the single layer network are also seen in the two-layer network.

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Temperature dependence of phase and spike synchronization of neural networks

Cited by 18 publications

References 40 publications

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

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

An Expert System Optimization Model for Desktop Computers

A Discrete Huber-braun Neuron Model: From Nodal Properties to Network Performance

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