A piezoelectric sensing neuron and resonance synchronization between auditory neurons under stimulus

Zhou, Ping; Yao, Zhao; Ma, Jun; Zhu, Zhigang

doi:10.1016/j.chaos.2021.110751

Cited by 111 publications

(32 citation statements)

References 75 publications

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“…This discussion on energy is generally focused on a single neuron or homogeneous neural network. Recall that this Hamiltonian method has been widely used by Ma and collaborators to estimate the energy released by neurons during electrical activity [34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Hamiltonian energy computation and complex behavior of a small heterogeneous network of three neurons: circuit implementation

et al. 2021

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Brain functions are sometimes emulated using some analog integrated circuits based on the organizational principle of natural neural networks. Neuromorphic engineering is the research branch devoted to the study and realization of such circuits with striking features. In this contribution, a novel small network of three neurons is introduced and investigated. The model is built from the coupling between two 2D Hindmarsh–Rose neurons through a 2D FitzHugh–Nagumo neuron. Thus, a heterogeneous coupled network is obtained. The biophysical energy released by the network during each electrical activity is evaluated. In addition, nonlinear analysis tools such as two-parameter Lyapunov exponent, bifurcation diagrams, the graph of the largest Lyapunov exponent, phase portraits, time series, as well as the basin of attractions are used to numerically investigate the network. It is found that the model can experience hysteresis justified by the simultaneous existence of three distinct electrical activities using the same set of parameters. Finally, the circuit implementation of the network is addressed in PSPICE to further support the obtained results.

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Section: Introductionmentioning

confidence: 99%

Hamiltonian energy computation and complex behavior of a small heterogeneous network of three neurons: circuit implementation

et al. 2021

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“…For example, the synchronization approach between thermosensitive neuron and light-dependent neuron are helpful to detect the changes in environment and heat source exactly [ 21 ]. And two piezoelectric sensing neurons (PSNs) were driven by the same external voice for detecting possible synchronization approach without any synapse coupling, which found that two identical PSNs driven by the same periodical stimuli (external forces) could reach synchronous bursting, spiking, and periodical firings, and the synchronization stability was dependent on the external forcing applied on the two PSNs in case of chaotic firing [ 22 ]. Except for functional neuron models, field coupling was also considered in the neural circuits recently.…”

Section: Introductionmentioning

confidence: 99%

Correlation Analysis of Synchronization Type and Degree in Respiratory Neural Network

Yuan

2021

Computational Intelligence and Neuroscience

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Pre-Bötzinger complex (PBC) is a necessary condition for the generation of respiratory rhythm. Due to the existence of synaptic gaps, delay plays a key role in the synchronous operation of coupled neurons. In this study, the relationship between synchronization and correlation degree is established for the first time by using ISI bifurcation and correlation coefficient, and the relationship between synchronization and correlation degree is discussed under the conditions of no delay, symmetric delay, and asymmetric delay. The results show that the phase synchronization of two coupling PBCs is closely related to the weak correlation, that is, the weak phase synchronization may occur under the condition of incomplete synchronization. Moreover, the time delay and coupling strength are controlled in the modified PBC network model, which not only reveals the law of PBC firing transition but also reveals the complex synchronization behavior in the coupled chaotic neurons. Especially, when the two coupled neurons are nonidentical, the complete synchronization will disappear. These results fully reveal the dynamic behavior of the PBC neural system, which is helpful to explore the signal transmission and coding of PBC neurons and provide theoretical value for further understanding respiratory rhythm.

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“…On the other hand, many chaotic systems and synchronization strategies have been realized by electronic circuits [16][17][18]. So, synchronization between chaotic systems can be transferred to the synchronization between chaotic electronic circuits.…”

Section: Introductionmentioning

confidence: 99%

Circuit Implementation Synchronization between Two Modified Fractional-Order Lorenz Chaotic Systems via a Linear Resistor and Fractional-Order Capacitor in Parallel Coupling

Liu

Cheng

Zhou

2021

Mathematical Problems in Engineering

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In this study, a modified fractional-order Lorenz chaotic system is proposed, and the chaotic attractors are obtained. Meanwhile, we construct one electronic circuit to realize the modified fractional-order Lorenz chaotic system. Most importantly, using a linear resistor and a fractional-order capacitor in parallel coupling, we suggested one chaos synchronization scheme for this modified fractional-order Lorenz chaotic system. The electronic circuit of chaos synchronization for modified fractional-order Lorenz chaotic has been given. The simulation results verify that synchronization scheme is viable.

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A piezoelectric sensing neuron and resonance synchronization between auditory neurons under stimulus

Cited by 111 publications

References 75 publications

Hamiltonian energy computation and complex behavior of a small heterogeneous network of three neurons: circuit implementation

Hamiltonian energy computation and complex behavior of a small heterogeneous network of three neurons: circuit implementation

Correlation Analysis of Synchronization Type and Degree in Respiratory Neural Network

Circuit Implementation Synchronization between Two Modified Fractional-Order Lorenz Chaotic Systems via a Linear Resistor and Fractional-Order Capacitor in Parallel Coupling

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