Prediction for breakup of spiral wave in a regular neuronal network

Ma, Jun; Xu, Ying; Ren, Guo-Dong; Wang, Chunni

doi:10.1007/s11071-015-2502-6

Cited by 65 publications

(10 citation statements)

References 36 publications

(42 reference statements)

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“…In addition, our future work will also include predicting power grid collapse through series analysis similar to the method presented in [60,61] such that effective measures can be taken in time to avoid system collapse.…”

Section: Resultsmentioning

confidence: 99%

Fixed-time dynamic surface high-order sliding mode control for chaotic oscillation in power system

Liu

et al. 2016

Nonlinear Dyn

130

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In this paper, a fixed-time dynamic surface high-order sliding mode control approach is presented for chaos suppression and voltage stabilization in three-bus power system via design of current source converter-based static synchronous compensator controller. The proposed control strategy constructs two high-order sliding mode surfaces to achieve control objective. By combining backstepping idea with dynamic surface control (DSC) technique, highorder sliding mode controller is designed and the inherent problem of "explosion of complexity" in backstepping design is avoided. Further, a new stability concept is introduced into DSC design to achieve semiglobal uniform ultimate boundedness of the signals in high-order sliding mode system within finite time independent of initial condition. In addition, stability analysis is provided to show that the proposed control scheme can achieve semi-globally fixed-timely uniformly ultimately bounded stabilization. Finally, simulation results are given to demonstrate the effectiveness of the proposed control scheme and the superior performance over conventional DSC.

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Section: Resultsmentioning

confidence: 99%

Fixed-time dynamic surface high-order sliding mode control for chaotic oscillation in power system

Liu

et al. 2016

Nonlinear Dyn

130

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“…Based on the wellknown neuron models, networks with different topological connections have been set to investigate the synchronization stability [36,37], pattern selection, and mode transition in collective behaviors [38][39][40][41][42][43][44][45]. Indeed, reliable and biophysical neuron model is critical and important for further investigation on neurodynamics and potential mechanism of some neuronal diseases [46].…”

Section: Complexitymentioning

confidence: 99%

Autaptic Modulation of Electrical Activity in a Network of Neuron-Coupled Astrocyte

Guo

Tang

et al. 2017

Complexity

Self Cite

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Autapse connection is considered on a biological neuron coupled by astrocyte, and the effect of autapse driving-induced response in electrical activities is investigated. In this paper, a simple network is developed on the Hodgkin-Huxley (HH) neuron coupled by astrocyte and the autapse effect is also considered. The modulation of autapse connected to HH neuron can change the membrane potential by applying time-delayed feedback along a close loop. It is found that the self-adaption of autapse driving can make the network of neuron-astrocyte generate different modes of electrical activities, and oscillating behavior of Ca 2+ and IP 3 setting is controlled. This new network model can give potential understanding about self-adaption of neuron to external forcing when the coupling of astrocyte and autapse is considered.

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“…Yamada and Kashimori [29] found that the learning of face stimuli makes the functional connections between these inferior temporal networks. Ma et al [30][31][32] investigated the death and breakup of spiral wave in the neuronal network. With the deepening of research development, more and more achievements are observed [33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Mixed Stimulus-Induced Mode Selection in Neural Activity Driven by High and Low Frequency Current under Electromagnetic Radiation

et al. 2017

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The electrical activities of neurons are dependent on the complex electrophysiological condition in neuronal system, the threevariable Hindmarsh-Rose (HR) neuron model is improved to describe the dynamical behaviors of neuronal activities with electromagnetic induction being considered, and the mode transition of electrical activities in neuron is detected when external electromagnetic radiation is imposed on the neuron. In this paper, different types of electrical stimulus impended with a highlow frequency current are imposed on new HR neuron model, and mixed stimulus-induced mode selection in neural activity is discussed in detail. It is found that mode selection of electrical activities stimulated by high-low frequency current, which also changes the excitability of neuron, can be triggered owing to adding the Gaussian white noise. Meanwhile, the mode selection of the neuron electrical activity is much dependent on the amplitude of the high frequency current under the same noise intensity, and the high frequency response is selected preferentially by applying appropriate parameters and noise intensity. Our results provide insights into the transmission of complex signals in nerve system, which is valuable in engineering prospective applications such as information encoding.

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Prediction for breakup of spiral wave in a regular neuronal network

Cited by 65 publications

References 36 publications

Fixed-time dynamic surface high-order sliding mode control for chaotic oscillation in power system

Fixed-time dynamic surface high-order sliding mode control for chaotic oscillation in power system

Autaptic Modulation of Electrical Activity in a Network of Neuron-Coupled Astrocyte

Mixed Stimulus-Induced Mode Selection in Neural Activity Driven by High and Low Frequency Current under Electromagnetic Radiation

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