14 results on '"Wang, Chunni"'
Search Results
2. Synchronization realization between two nonlinear circuits via an induction coil coupling
- Author
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Yao, Zhao, Ma, Jun, Yao, Yuangen, and Wang, Chunni
- Published
- 2019
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3. Prediction for breakup of spiral wave in a regular neuronal network
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Ma, Jun, Xu, Ying, Ren, Guodong, and Wang, Chunni
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- 2016
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4. Dynamics of electric activities in neuron and neurons of network induced by autapses
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Qin, HuiXin, Ma, Jun, Jin, WuYin, and Wang, ChunNi
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- 2014
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5. Phase synchronization between nonlinear circuits by capturing electromagnetic field energy.
- Author
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Wang, Chunni, Yao, Zhao, Xu, Wenkang, and Ren, Guodong
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ELECTROMAGNETIC waves , *ELECTROMAGNETIC fields , *ELECTROMAGNETIC radiation , *SYNCHRONIZATION , *NEURAL circuitry , *MEMRISTORS - Abstract
Smart nonlinear circuits can be tamed to reproduce the main dynamical properties in neural activities and thus neural circuits are built to estimate the occurrence of multiple modes in electric activities. In the presence of electromagnetic radiation, the cardiac tissue, brain and neural circuits are influenced because field energy is injected and captured when induction field and current are generated in the media and system. In this paper, an isolated Chua circuit is exposed to external electromagnetic field and energy capturing is estimated for nonlinear analysis from physical viewpoint. Furthermore, two Chua circuits without direct variable coupling are exposed to the same electromagnetic field for energy capturing. Periodical and noise-like radiations are imposed on the Chua circuits which can capture the magnetic field energy via the induction coil. It is found that the two Chua circuits (periodical or chaotic) can reach phase synchronization and phase lock in the presence of periodical radiation. On the other hand, noise-like radiation can realize complete synchronization between two chaotic Chua circuits while phase lock occurs between two Chua circuits in periodical oscillation. It gives some important clues to control the collective behaviors of neural activities under external field. [ABSTRACT FROM AUTHOR]
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- 2020
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6. Control and synchronization in nonlinear circuits by using a thermistor.
- Author
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Zhang, Xiufang, Wang, Chunni, Ma, Jun, and Ren, Guodong
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THERMISTORS , *NONLINEAR dynamical systems , *CHAOS synchronization , *SYNCHRONIZATION , *ORDINARY differential equations , *CHAOTIC communication - Abstract
The survival and occurrence of chaos are much dependent on the intrinsic nonlinearity and parameters region for deterministic nonlinear systems, which are often represented by ordinary differential equations and maps. When nonlinear circuits are mapped into dimensional dynamical systems for further nonlinear analysis, the physical parameters of electric components, e.g. capacitor, inductor, resistance, memristor, can also be replaced by dynamical parameters for possible adjustment. Slight change for some bifurcation parameters can induce distinct mode transition and dynamics change in the chaotic systems only when the parameter is adjustable and controllable. In this paper, a thermistor is included into the chaotic Chua circuit and the temperature effect is considered by investigating the mode transition in oscillation and the dependence of Hamilton energy on parameters setting in thermistor. Furthermore, the temperature of thermistor is adjusted for finding possible synchronization between two chaotic Chua circuits connected by a thermistor. When the coupling channel via thermistor connection is activated, two identical Chua circuits (periodical or chaotic oscillation) can reach complete synchronization. In particular, two periodical Chua circuits can be coupled to present chaotic synchronization by taming parameters in thermistor of coupling channel. However, phase synchronization is reached while complete synchronization becomes difficult when the coupling channel is activated to coupling a periodical Chua circuit and a chaotic Chua circuit. It can give guidance for further control of firing behaviors in neural circuits when the thermistor can capture the heat effectively. [ABSTRACT FROM AUTHOR]
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- 2020
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7. Field coupling-induced pattern formation in two-layer neuronal network.
- Author
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Qin, Huixin, Wang, Chunni, Cai, Ning, An, Xinlei, and Alzahrani, Faris
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PATTERN formation (Physical sciences) , *IONS , *MEMBRANE potential , *ELECTROMAGNETIC induction , *NEURONS - Abstract
The exchange of charged ions across membrane can generate fluctuation of membrane potential and also complex effect of electromagnetic induction. Diversity in excitability of neurons induces different modes selection and dynamical responses to external stimuli. Based on a neuron model with electromagnetic induction, which is described by magnetic flux and memristor, a two-layer network is proposed to discuss the pattern control and wave propagation in the network. In each layer, gap junction coupling is applied to connect the neurons, while field coupling is considered between two layers of the network. The field coupling is approached by using coupling of magnetic flux, which is associated with distribution of electromagnetic field. It is found that appropriate intensity of field coupling can enhance wave propagation from one layer to another one, and beautiful spatial patterns are formed. The developed target wave in the second layer shows some difference from target wave triggered in the first layer of the network when two layers are considered by different excitabilities. The potential mechanism could be pacemaker-like driving from the first layer will be encoded by the second layer. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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8. A review and guidance for pattern selection in spatiotemporal system.
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Wang, Chunni and Ma, Jun
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SPATIOTEMPORAL processes , *REACTION-diffusion equations , *MULTIAGENT systems , *BIFURCATION theory , *ELECTROMAGNETIC induction - Abstract
Pattern estimation and selection in media can give important clues to understand the collective response to external stimulus by detecting the observable variables. Both reaction-diffusion systems (RDs) and neuronal networks can be treated as multi-agent systems from molecular level, intrinsic cooperation, competition. An external stimulus or attack can cause collapse of spatial order and distribution, while appropriate noise can enhance the consensus in the spatiotemporal systems. Pattern formation and synchronization stability can bridge isolated oscillators and the network by coupling these nodes with appropriate connection types. As a result, the dynamical behaviors can be detected and discussed by developing different spatial patterns and realizing network synchronization. Indeed, the collective response of network and multi-agent system depends on the local kinetics of nodes and cells. It is better to know the standard bifurcation analysis and stability control schemes before dealing with network problems. In this review, dynamics discussion and synchronization control on low-dimensional systems, pattern formation and synchronization stability on network, wave stability in RDs and neuronal network are summarized. Finally, possible guidance is presented when some physical effects such as polarization field and electromagnetic induction are considered. [ABSTRACT FROM AUTHOR]
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- 2018
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9. Dynamical responses in a new neuron model subjected to electromagnetic induction and phase noise.
- Author
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Wu, Fuqiang, Wang, Chunni, Jin, Wuyin, and Ma, Jun
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NEURONS , *ELECTROMAGNETIC induction , *PHASE noise , *DYNAMICAL systems , *TIME-varying systems , *ELECTROMAGNETIC fields - Abstract
Complex electrical activities in neuron can induce time-varying electromagnetic field and the effect of various electromagnetic inductions should be considered in dealing with electrical activities of neuron. Based on an improved neuron model, the effect of electromagnetic induction is described by using magnetic flux, and the modulation of magnetic flux on membrane potential is realized by using memristor coupling. Furthermore, additive phase noise is imposed on the neuron to detect the dynamical response of neuron and phase transition in modes. The dynamical properties of electrical activities are detected and discussed, and double coherence resonance behavior is observed, respectively. Furthermore, multiple modes of electrical activities can be observed in the sampled time series for membrane potential of the neuron model. [ABSTRACT FROM AUTHOR]
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- 2017
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10. Investigation of dynamical behaviors of neurons driven by memristive synapse.
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Zhang, Ge, Wang, Chunni, Alzahrani, Faris, Wu, Fuqiang, and An, Xinlei
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SYNAPSES , *MEMRISTORS , *SIGNAL processing , *ELECTROMAGNETIC induction , *NEURON analysis - Abstract
Synapse is an important bridge for receiving and encoding signals, and the description for synapse current is critical for further signal processing. This paper investigates the dynamical characteristic in isolated neuron and chain neuronal networks with memristive autapse or synapses, respectively. Autapse plays important role in modulating the electrical activities, and thus the information encoding is enhanced. Within the improved neuron model, memristor is used to map the modulation of synapse current. Within an isolated new neuron model with memory, the modes in electrical activities can be controlled by the synapse current completely. Bifurcation analysis is carried out and mode transition is discussed. Furthermore, the modulation of synapse current on chain network is investigated, and the dependence of wave propagation on intensity of synapse is discussed. The diversity in synapse current can suppress the synchronization approach on the network. [ABSTRACT FROM AUTHOR]
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- 2018
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11. Regulating synchronous patterns in neurons and networks via field coupling.
- Author
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Yao, Zhao, Wang, Chunni, Zhou, Ping, and Ma, Jun
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NEURONS , *MAGNETIC coupling , *INDUCTION coils , *NEURAL circuitry , *COLLECTIVE behavior - Abstract
Both electrical and chemical synapses play important role in receiving and propagating signals between biological neurons. The activation of electrical synapse coupling is generated via gap junction connection while chemical synapse coupling depends on the release of neurotransmitter for triggering stable propagation of ions. In a practical way, voltage coupling via resistor connection between artificial neural circuits is often used to describe the gap junction coupling via electrical synapse between neurons. Magnetic field coupling via induction coil between neural circuits can be effective to estimate the chemical synapse coupling when ions are propagated and released in the cell. In this paper, the physical properties of magnetic field coupling between neural circuits are discussed. For simplicity, two Hindmarsh–Rose neurons are connected to trigger magnetic field coupling and the coupling channel is tamed to detect possible occurrence of synchronization. Two neurons are stimulated with different stimuli, and it is confirmed that the synchronous firing mode is decided by the neuron with higher forcing amplitude. Furthermore, the collective behaviors of HR neuron in the small-world network and scale free network are investigated by changing the coupling intensity and diversity in external stimulus, respectively. Statistical synchronization factor is calculated to estimate the dependence of synchronization on coupling gain and the number of bursting neurons in the network. In case of small-world connection, synchronization approach becomes difficult. However, complete synchronization can be stabilized on the scale free network under magnetic field coupling. [ABSTRACT FROM AUTHOR]
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- 2021
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12. Stability of target waves in excitable media under electromagnetic induction and radiation.
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Zhang, Yin, Wu, Fuqiang, Wang, Chunni, and Ma, Jun
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MEMRISTORS , *ELECTROMAGNETIC induction , *ELECTROMAGNETIC radiation , *BIFURCATION theory , *WHITE noise - Abstract
Abstract Local periodic stimulus can trigger stable pulse and target wave in the excitable media, the potential mechanism is that each cell is activated by continuous driving to keep pace with the external forcing. Indeed, local diversity in excitability and heterogeneity are induced when external forcing with diversity is applied on the media. When target wave is formed and propagated, the media will be modulated, e.g. it can emit electric signal from sinoatrial node and develops target wave in the cardiac tissue, and then the heartbeat is controlled completely. In fact, the distribution of electromagnetic field in the media is changed when external electric stimulus is applied on the media; therefore, the effect of electromagnetic induction becomes important during the information encoding and signal propagation. In this paper, the Morris–Lecar model is used to describe the local kinetic of the media and an induction current is considered thus the effect of electromagnetic induction can be described. Then the artificial heterogeneity is triggered to investigate the formation and development of target waves in the excitable media with electromagnetic induction and even radiation, which is described by using Gaussian white noise. Diversity in excitability and conductance of ion channels is considered, respectively, thus different kinds of artificial heterogeneity are developed to find the stability of target waves. It is found that the target wave encounters breakup with increasing of the intensity of electromagnetic radiation. Highlights • Biological neuron model is improved to consider the effect of electromagnetic induction. • Heterogeneity formation is explained on conductance and excitability diversity. • Electromagnetic radiation can suppress propagation of target wave. • Breakup of target wave can be induced by electromagnetic radiation. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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13. Mode selection in electrical activities of myocardial cell exposed to electromagnetic radiation.
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Ma, Jun, Wang, Ya, Wang, Chunni, Xu, Ying, and Ren, Guodong
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ELECTROMAGNETIC radiation , *BIFURCATION theory , *HEART cells , *MAGNETIC flux , *MAGNETIZATION - Abstract
Based on the Fitzhugh–Nagumo neuron model, the effect of electromagnetic induction is considered and external electromagnetic radiation is imposed to detect the mode transition of electrical activities in a myocardial cell. Appropriate dynamical and functional responses can be observed in the sampled series for membrane potentials by setting different feedback modulation on the membrane potential in presence of electromagnetic radiation. The electromagnetic radiation is described by a periodical forcing on the magnetic flux, and it is found that the response frequency can keep pace with the frequency of external forcing. However, mismatch of frequency occurs by further increasing the frequency of external forcing, it could account for the information encoding of neuron. The dynamical response could be associated with the magnetization and polarization of the media, thus the outputs of membrane potential can become quiescent and/or bursting as well. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
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14. Coupling synchronization between photoelectric neurons by using memristive synapse.
- Author
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Guo, Yeye, Zhu, Zhigang, Wang, Chunni, and Ren, Guodong
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SYNCHRONIZATION , *ELECTRONIC equipment , *NEURAL circuitry , *NEURONS , *PHOTOCONDUCTIVE cells - Abstract
Phototube can capture and transmit external illumination with high frequency into photocurrent in the nonlinear circuit, which can be tamed to produce the main dynamical properties of biological neurons. In fact, the involvement of functional electronic components can enhance the biophysical function of artificial neural circuits. In this paper, a photocell is connected to FitzHugh-Nagumo neural circuits, which can be excited by the photocurrent generated from the photocell. Furthermore, a memristor is proposed to couple two light-dependent neural circuits and the synchronization stability is investigated on the coupled dynamical systems by using standard scale transformation on the physical variables and parameters. It is found that phase synchronization and complete synchronization can be obtained by regulating the coupling channel connected by the memristor. The propagation and pumping of field energy are also calculated to predict the occurrence of synchronization. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
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