Firing and synchronization modes in neural network under magnetic stimulation

Etémé, Armand Sylvin; Tabi, Conrad Bertrand; Mohamadou, Alidou

doi:10.1016/j.cnsns.2019.01.004

Cited by 44 publications

(25 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Especially, complex networks are useful for analyzing problems involving physical, biological, chemical, engineering, and even social perspectives [1,2]. In this way, coupled oscillators are able to investigate a large class of dynamical systems in a theoretical, computational or even experimental field [3][4][5][6]. As an example, neural networks can be understood as coupled oscillators where it is possible to associate the bursting neuron to a phase oscillator [7].…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…At present, most of the researches on self-adaptive regulation are firing synchronization and neural coding in SNNs under external stimulation. Etémé et al found that electromagnetic stimulation induces not only regular firing activity of the neuron with spiking and bursting regimes but also synchronous neuronal modes in neural network under magnetic stimulation [35]. In our previous work, the responses of the time coding and the rate coding of the smallworld SNN both showed respective specificity under white Gaussian noise and impulse noise [36].…”

Section: Introductionmentioning

confidence: 71%

Noise suppression ability and its mechanism analysis of scale-free spiking neural network under white Gaussian noise

Guo

Kan

et al. 2020

PLoS ONE

View full text Add to dashboard Cite

With the continuous improvement of automation and informatization, the electromagnetic environment has become increasingly complex. Traditional protection methods for electronic systems are facing with serious challenges. Biological nervous system has the self-adaptive advantages under the regulation of the nervous system. It is necessary to explore a new thought on electromagnetic protection by drawing from the self-adaptive advantage of the biological nervous system. In this study, the scale-free spiking neural network (SFSNN) is constructed, in which the Izhikevich neuron model is employed as a node, and the synaptic plasticity model including excitatory and inhibitory synapses is employed as an edge. Under white Gaussian noise, the noise suppression abilities of the SFSNNs with the high average clustering coefficient (ACC) and the SFSNNs with the low ACC are studied comparatively. The noise suppression mechanism of the SFSNN is explored. The experiment results demonstrate that the following. (1) The SFSNN has a certain degree of noise suppression ability, and the SFSNNs with the high ACC have higher noise suppression performance than the SFSNNs with the low ACC. (2) The neural information processing of the SFSNN is the linkage effect of dynamic changes in neuron firing, synaptic weight and topological characteristics. (3) The synaptic plasticity is the intrinsic factor of the noise suppression ability of the SFSNN.

show abstract

“…Indeed, during the past few decades, many investigations have been devoted to achieving the synchronization of neural networks by using various control approaches [37][38][39][40][41]. There are also some works investigating the phenomenon of firing synchronization in coupled bineuron networks in recent years [42][43][44][45]. For example, in [46], bursting synchronization of two coupled ML neurons under electromagnetic radiation was studied.…”

Section: Introductionmentioning

confidence: 99%

Neural Bursting and Synchronization Emulated by Neural Networks and Circuits

Lin

Wang

Chen

et al. 2021

IEEE Trans. Circuits Syst. I

View full text Add to dashboard Cite

Nowadays, research, modeling, simulation and realization of brain-like systems to reproduce brain behaviors have become urgent requirements. In this paper, neural bursting and synchronization are imitated by modeling two neural network models based on the Hopfield neural network (HNN). The first neural network model consists of four neurons, which correspond to realizing neural bursting firings. Theoretical analysis and numerical simulation show that the simple neural network can generate abundant bursting dynamics including multiple periodic bursting firings with different spikes per burst, multiple coexisting bursting firings, as well as multiple chaotic bursting firings with different amplitudes. The second neural network model simulates neural synchronization using a coupling neural network composed of two above small neural networks. The synchronization dynamics of the coupling neural network is theoretically proved based on the Lyapunov stability theory. Extensive simulation results show that the coupling neural network can produce different types of synchronous behaviors dependent on synaptic coupling strength, such as anti-phase bursting synchronization, anti-phase spiking synchronization, and complete bursting synchronization. Finally, two neural network circuits are designed and implemented to show the effectiveness and potential of the constructed neural networks.

show abstract

Firing and synchronization modes in neural network under magnetic stimulation

Cited by 44 publications

References 28 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

Noise suppression ability and its mechanism analysis of scale-free spiking neural network under white Gaussian noise

Neural Bursting and Synchronization Emulated by Neural Networks and Circuits

Contact Info

Product

Resources

About