Bursting and Synchronization of Coupled Neurons under Electromagnetic Radiation

Hu, Xiaoyu; Liu, Chongxin

doi:10.1155/2019/4835379

Cited by 12 publications

(4 citation statements)

References 52 publications

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“…proposed a modified ML model with a slow variable [9]. This modified ML model is described as follows:…”

Section: Modelmentioning

confidence: 99%

“…Also, there are other reasonable ways to introduce slow variables. For example, Hu et al proposed a modified ML model in which the magnetic flux is introduced to be a slow variable [9]. In addition, a complete electronic circuit of the 2D ML neuron model has been established and discussed [10], which provides a novel way to study ML neuron dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Synchronization mode transitions induced by chaos in modified Morris–Lecar neural systems with weak coupling

Wang

et al. 2022

Nonlinear Dyn

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Based on a modified Morris-Lecar neural model, the synchronization modes transitions between two coupled neurons or starcoupled neural network connected by weak electrical and chemical coupling are respectively investigated. For the two coupled neurons, by increasing the calcium conductivity, it is found that the period2 synchronization of action potential of neurons is transformed to desynchronization first, and then to period3 synchronization. By increasing the potassium conductivity, however, the synchronization mode transition is a reversal direction process as mentioned above. The bifurcation analysis of interspike interval shows that the synchronization modes transition is induced by the chaos. The stronger the coupling strength is, the smaller the period2 synchronization region in the parameters plane is, while the larger the period3 synchronization region will be. For the starcoupled neural network, in the presence of weak electrical coupling, it can exhibit the complete synchronization, desynchronization, and drum head mode states under different parameter values, respectively. In the presence of chemical synapse, however, the completely synchronized state can not be observed in the starcoupled neural network. Our results might provide novel insights into synchronization modes transition and related biological experiments.

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“…proposed a modified ML model with a slow variable [9]. This modified ML model is described as follows:…”

Section: Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Synchronization mode transitions induced by chaos in modified Morris–Lecar neural systems with weak coupling

Wang

et al. 2022

Nonlinear Dyn

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show abstract

“…A large number of physical and biological experiments have confirmed that many neurological diseases such as Alzheimer and epilepsy [7] are caused by abnormal discharge of neurons [8,9], and appropriate stimulus helps to reduce the symptoms of psychiatric patients, which is conducive to their health [10]. Hence, studying the firing behavior of neurons under external stimulus plays a positive role in understanding the pathogenesis of neuronal diseases, then contributes to the treatment and prevention of such neurological diseases.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of stimuli-based fractional-order memristor-coupled tabu learning two-neuron model and its engineering applications

et al. 2022

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External stimulus has an impact on the functional behavior of the biological nervous system, and appropriate stimulus helps the organism to maintain neural function. Inspired by this, the effects of different external stimuli on the dynamic behaviors of neuron model are studied in this paper. Firstly, a fractional-order(FO) memristor-coupled tabu learning two-neuron model whose equilibrium points are symmetric about the origin and unstable is constructed. The dynamic behaviors of this neuron model under different stimuli is further discussed, which are no external stimulus, external forced current stimulus and electromagnetic radiation (EMR) respectively. The neuron model without external stimulus has periodic attractors, but applying external stimulus to one of the neurons can produce abundant two-scroll chaotic attractors and multistability. Especially, when the neuron model is stimulated by EMR, it can generate hyperchaotic attractors that have not been observed in the tabu learning neuron model before. Besides, the transient transition behaviors of the model under different stimuli are also studied. Then, a pseudo-random number generator (PRNG) is designed and its random performance is tested with NIST suite. Finally, it is applied to voice encryption, and the result shows that it has good encryption effect. Therefore, it can be said that the FO memristor-coupled tabu learning two-neuron model has superior randomness and is suitable for chaotic-based engineering applications.

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“…There are also some works investigating the phenomenon of firing synchronization in coupled bineuron networks in recent years [42][43][44][45]. For example, in [46], bursting synchronization of two coupled ML neurons under electromagnetic radiation was studied. Chaotic firing synchronization and synchronization transition of memristorbased coupled HR neurons was investigated in [47].…”

Section: Introductionmentioning

confidence: 99%

Neural Bursting and Synchronization Emulated by Neural Networks and Circuits

Lin

Wang

Chen

et al. 2021

IEEE Trans. Circuits Syst. I

102

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Nowadays, research, modeling, simulation and realization of brain-like systems to reproduce brain behaviors have become urgent requirements. In this paper, neural bursting and synchronization are imitated by modeling two neural network models based on the Hopfield neural network (HNN). The first neural network model consists of four neurons, which correspond to realizing neural bursting firings. Theoretical analysis and numerical simulation show that the simple neural network can generate abundant bursting dynamics including multiple periodic bursting firings with different spikes per burst, multiple coexisting bursting firings, as well as multiple chaotic bursting firings with different amplitudes. The second neural network model simulates neural synchronization using a coupling neural network composed of two above small neural networks. The synchronization dynamics of the coupling neural network is theoretically proved based on the Lyapunov stability theory. Extensive simulation results show that the coupling neural network can produce different types of synchronous behaviors dependent on synaptic coupling strength, such as anti-phase bursting synchronization, anti-phase spiking synchronization, and complete bursting synchronization. Finally, two neural network circuits are designed and implemented to show the effectiveness and potential of the constructed neural networks.

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Bursting and Synchronization of Coupled Neurons under Electromagnetic Radiation

Cited by 12 publications

References 52 publications

Synchronization mode transitions induced by chaos in modified Morris–Lecar neural systems with weak coupling

Synchronization mode transitions induced by chaos in modified Morris–Lecar neural systems with weak coupling

Dynamics of stimuli-based fractional-order memristor-coupled tabu learning two-neuron model and its engineering applications

Neural Bursting and Synchronization Emulated by Neural Networks and Circuits

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