9 results on '"Wang, Chunni"'
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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
- Full Text
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3. Phase synchronization of memristive systems by using saturation gain method.
- Author
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Ma, Siyu, Zhou, Ping, Ma, Jun, and Wang, Chunni
- Subjects
MEMRISTORS ,SYNCHRONIZATION ,ELECTRIC resistors ,INDUCTION coils ,CHAOS synchronization ,ENERGY consumption - Abstract
A variety of electric components can be used to bridge connection to the nonlinear circuits, and continuous pumping and consumption of energy are critical for voltage balance between the output end. The realization and stability of synchronization are mainly dependent on the physical properties of coupling channel, which can be built by using different electric components such as resistor, capacitor, induction coil and even memristor. In this paper, a memristive nonlinear circuit developed from Chua circuit is presented for investigation of synchronization, and capacitor, induction coil are jointed with resistor for building artificial synapse which connects one output of two identical memristive circuits. The capacitance and inductance of the coupling channel are carefully adjusted with slight step increase to estimate the threshold of coupling intensity supporting complete synchronization. As a result, the saturation gain method applied to realize the synchronization between chaotic circuits and physical mechanism is presented. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
4. Synchronization between neural circuits connected by hybrid synapse.
- Author
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Liu, Zhilong, Wang, Chunni, Zhang, Ge, and Zhang, Yin
- Subjects
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NEURAL circuitry , *HYBRID integrated circuits , *INDUCTION coils , *SYNCHRONIZATION , *SYNAPSES , *DYNAMICAL systems - Abstract
Reliable neural circuits provide feasible ways to estimate the information encoding and signal exchange between neurons so that more physical effects can be considered and explained. Electrical synapse benefits signal propagation and neurons can give rapid response selection under an external stimulus. Chemical synapse plays a standing biological function by the continuous release of neurotransmitter. Gap junction coupling can estimate the effect of electrical synapse coupling as resistor-based voltage coupling while chemical synapse coupling shows a complex form in synapse currents. In this paper, two FitzHugh–Nagumo neuron circuits are connected by resistor and induction coil, and thus field coupling is activated to realize synchronization. It gives possible guidance to explain the synapse function from physical view. These circuit equations are obtained and scale transformation is applied to get the dimensionless dynamical systems. Furthermore, the same scheme is used to discuss synchronization realization between Hindmarsh–Rose neuron models. It is found that synchronization can be suppressed when the coupling resistor is in series with the induction coil in which only one coupling channel is open. However, complete synchronization can be enhanced when the coupling resistor is paralleled with the induction coil because two coupling channels are activated simultaneously. As a result, appropriate adjustment and selection for the coupling electric devices can induce transition from synchronization to desynchronization between neural circuits, which indicates the self-adaption of synapse when two types of synapse are activated together. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
5. 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
- Subjects
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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
- Full Text
- View/download PDF
6. Capacitive coupling memristive systems for energy balance.
- Author
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Wang, Chunni, Sun, Guoping, Yang, Feifei, and Ma, Jun
- Subjects
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MEMRISTORS , *ENERGY function , *SYSTEM dynamics - Abstract
The dynamics of a memristive system is dependent on the initial values and thus the threshold of coupling intensity for synchronization approach becomes unknown and unpredictable. Joule heat is consumed before reaching complete synchronization when variable coupling is applied for triggering voltage coupling via resistor connection. In this paper, a capacitor is used to couple two memristive circuits and the coupling intensity is increased in exponential way until the two memristive systems reach energy balance adaptively. The regulation of coupling intensity is controlled by using the Heaviside function when the Hamilton energy functions for the memristive systems are available. It is confirmed that two memristive systems can reach complete synchronization, phase lock when the coupling intensity is regulated in an adaptive way. It also explains why energy diversity enables creation of channel connection and coupling between nonlinear circuits, and then the coupling channel is tamed to realize energy balance between memristive systems without confirming exact threshold for coupling intensity. The scheme shows distinct self-adaptive property because the coupling intensity is controlled by the energy flow along the coupling channel. In particular, this kind of field coupling is controllable and external energy injection is helpful to regulate the coupling channel adaptively. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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7. Control the collective behaviors in a functional neural network.
- Author
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Yao, Zhao and Wang, Chunni
- Subjects
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COLLECTIVE behavior , *NEURAL circuitry , *INDUCTION coils , *NERVOUS system , *NEURAL stimulation , *AUDITORY neurons - Abstract
• Three kinds of functional neurons are presented. • Magnetic field coupling is the main bridge for different functional neurons. • Electric synapse coupling and electric field coupling corrupt synchronization stability between different neurons. Specific biophysical neurons are presented to detect different stimuli, and these external exciting signals are encoded to trigger appropriate firing modes and action potentials for signal propagation between neurons in the network. A thermosensitive neuron can estimate the effect of temperature changes on the excitability and firing modes in nervous system, a photocurrent-dependent neuron can be sensitive to the changes of external illumination or light, and an auditory neuron can perceive acoustic wave when the vibration energy is absorbed and converted into field energy in the loop of neural circuits. From biophysical viewpoint, some specific electric components such as thermistor, phototube, and piezoelectric ceramics can be incorporated into neural circuits for activating specific functions, and thus the external stimuli such as heat, light and vibration can be detected because these energy injections can be converted to intrinsic field energy in the neural circuits. In this paper, three kinds of different neural circuits are coupled in a close loop, energy pumping and the stability of phase synchronization are investigated by regulating the properties of coupling channels, furthermore, the noise effect is also estimated. When induction coil is used to couple the neural circuits, phase stability is controlled under magnetic field coupling, and the activation of noise can change the stability of phase synchronization. The intrinsic field energy in the light-dependent neuron is increased with the increase of coupling intensity when voltage coupling via resistor and magnetic field coupling via induction coil are switched on. In case of electric field coupling via capacitor, the energy in the light-dependent neural circuit keeps oscillatory with small amplitude. These results indicate that magnetic field can be the most suitable way for realizing synchronous information encoding between different functional neurons than the electric synapse coupling because continuous pumping of ions can induce magnetic field in the cell, and all the neurons are controlled completely by effective pumping in field energy. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
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8. Regulating synchronous patterns in neurons and networks via field coupling.
- Author
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Yao, Zhao, Wang, Chunni, Zhou, Ping, and Ma, Jun
- Subjects
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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]
- Published
- 2021
- Full Text
- View/download PDF
9. Energy encoding in a biophysical neuron and adaptive energy balance under field coupling.
- Author
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Sun, Guoping, Yang, Feifei, Ren, Guodong, and Wang, Chunni
- Subjects
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BIOENERGETICS , *NEURONS , *STOCHASTIC resonance , *INDUCTION coils , *COLLECTIVE behavior - Abstract
Some nonlinear circuits can present similar dynamical properties in the outputs as biological neurons by taming the external stimulus and bifurcation parameter, and they can be used for further investigation on neural activities. In this work, a photoelectric converter such as phototube is incorporated into the Fitzhugh-Nagumo neural circuit and a light-sensitive neuron is obtained to describe the nonlinear response in visual neuron. Furthermore, an additive induction coil is also connected to the phototube in this branch circuit of this neural circuit for detecting the disturbance from external magnetic field, and this functional neuron can perceive external illumination and changes of external magnetic field synchronously. Scale transformation is applied for the circuit equation and equivalent photoelectric neuron model is obtained. The intrinsic field energy of the neural circuit is described by equivalent Hamilton energy and the dependence firing modes on energy is clarified. When neurons are clustered closely, energy diversity can trigger field coupling and the synaptic connection will be regulated in the coupling intensity adaptively. An artificial hybrid is developed by combining capacitor and inductor in the coupling channel, and synchronization approach and energy balance is controlled completely. These results provide potential explanation for knowing the growth mechanism for synapse connection and external energy injection can be effective to control the collective behaviors of neurons. • A functional neural circuit is designed to detect external illumination and magnetic field. • Hamilton energy for biological neuron is obtained. • Stochastic resonance is induced in the biophysical neuron under noisy magnetic field. • Hybrid synapse is controllable under energy flow. • Biophysical mechanism for synapse controllability is explained. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
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