Phase synchronization between neurons under nonlinear coupling via hybrid synapse

Zhou, Ping; Ma, Jun; Xu, Ying

doi:10.1016/j.chaos.2023.113238

Cited by 21 publications

(3 citation statements)

References 55 publications

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“…Hamilton energy is often used to describe equiavlent dimensionless energy of a dynamic system. To determine the energy of our memristive system which can be expressed as a function of, according to [33,34] and system (6) and we have…”

Section: Lyapunov Exponents and And Hamilton Energymentioning

confidence: 99%

Generating rotationally hidden attractive sea via a new chaotic system with two mixed memristors

Zhou,

2023

Phys. Scr.

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In this work, a novel 3D memristive chaotic system which has an exponential function is proposed. Especially, the sum of Lyapunov exponents in the proposed system is 0. It indicates that the system can generate attractive sea not attractor. In comparison with some other 3D chaotic systems, this type of chaotic system is relatively rare. In particular, the proposed system has non-equilibrium point, and it can produce hidden sea. Furthermore, the perpetual point of the proposed system is caculated. It is considered to be potentially related to the generation of hidden dynamics. By using the dynamic analysis tool such as 0-1 test and 2D dynamical map, the dynamic behaviors with different control parameters are analyzed. And then, based on the proposed 3D chaotic system, two new system models are reconstructed. The new model can produce the rotational hidden attractive sea with different angles. DSP implementation shows the feasibility of the system for industrial applications.

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Section: Lyapunov Exponents and And Hamilton Energymentioning

confidence: 99%

Generating rotationally hidden attractive sea via a new chaotic system with two mixed memristors

Zhou,

2023

Phys. Scr.

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“…Hence, researchers have explored the impact of various scenarios on synchronization, such as alterations in network structure [23], the choice of coupling methods, such as adaptive, linear [24] or nonlinear couplings [25,26], bidirectional [24] or unidirectional couplings [27,28], time-varying coupling function [29,30], the introduction of time delays [31,32], and the addition of noise [33,34]. Diffusive coupling is one of the most popular couplings between systems, which is used in many studies [35][36][37][38][39]. This coupling plays an essential role in the synchronization of neural activity by ions' diffusion between cells, and mixing various reactants in the chemical reactions [37,38].…”

mentioning

confidence: 99%

“…This type of coupling introduces complex dynamics and behaviors into the network, which can create various interesting phenomena, including chaos, pattern formation, and bifurcations [42][43][44]. Moreover, the nonlinear coupling is more likely to induce various forms of synchronization [25,36,45,46]. Also, proper design of nonlinear couplings between nonlinear systems can lead to amplitude and oscillation death [47,48].…”

mentioning

confidence: 99%

The effect of nonlinear diffusive coupling on the synchronization of coupled oscillators

Massihi,

Parastesh,

Towhidkhah

et al. 2024

EPL

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This paper examines the impact of nonlinear coupling on the synchronization of interconnected oscillators. Various powers of diffusive coupling are explored to introduce nonlinear effects, and the results are contrasted with those of linear diffusive coupling. The study employs three representative chaoic systems—namely, the Lorenz, R¨ossler, and Hindmarsh-Rose systems. Findings indicate that nonlinear couplings with power below one result in synchronization at lower coupling strengths. Additionally, the critical coupling strength reduces as the coupling power decreases. However, the synchronization region undergoes changes and becomes bounded.Conversely, for powers exceeding one, networks are either unable to synchronize or require higher coupling strengths compared to linear coupling.

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Dynamics in a memristor-coupled heterogeneous neuron network under electromagnetic radiation

Cheng

Wang

et al. 2023

Nonlinear Dyn

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The electromagnetic environment around neurons is very complex, and studying the effect of electromagnetic radiation on the ring behavior of neurons is of great signi cance. In this paper, we establish a memristor coupled heterogeneous neuron network composed of a HR neuron and a FHN neuron, where the effect of electromagnetic radiation is modeled by the induced current of the ux-controlled memristor. The ring behaviors of the network are studied through phase diagrams, time series, bifurcation diagrams, Lyapunov exponent spectrums, and local attraction basins. It is found that under different initial conditions, the network exhibits different bifurcation routes by varying the coupling strength, resulting in the coexistence of multiple ring patterns. More interestingly, the network, under different initials, appears completely opposite bifurcation routes when the electromagnetic radiation intensity varies. In addition, synchronous ring behavior between two heterogeneous neurons is also explored. It is observed that both neurons can achieve phase synchronization when the coupling strength decreases to a negative value. Finally, the numerical analysis is veri ed by the Multisim circuit.

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Phase synchronization between neurons under nonlinear coupling via hybrid synapse

Cited by 21 publications

References 55 publications

Generating rotationally hidden attractive sea via a new chaotic system with two mixed memristors

Generating rotationally hidden attractive sea via a new chaotic system with two mixed memristors

The effect of nonlinear diffusive coupling on the synchronization of coupled oscillators

Dynamics in a memristor-coupled heterogeneous neuron network under electromagnetic radiation

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