Memristor synapse-coupled memristive neuron network: synchronization transition and occurrence of chimera

Bao, Han; Zhang, Yunzhen; Liu, Yanyi; Bao, Bocheng

doi:10.1007/s11071-020-05529-2

Cited by 144 publications

(29 citation statements)

References 43 publications

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“…However, the dynamical effects of the state initials on synchronization in the memristor-coupled systems were rarely concerned in the published literatures [37,169,170]. e first reason is that the state initials are implicit parameters and cannot be expressed explicitly in the state equations.…”

Section: Synchronization Application Of Memristor-coupled Systemmentioning

confidence: 99%

Recent Advances in Dimensionality Reduction Modeling and Multistability Reconstitution of Memristive Circuit

2021

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Due to the introduction of memristors, the memristor-based nonlinear oscillator circuits readily present the state initial-dependent multistability (or extreme multistability), i.e., coexisting multiple attractors (or coexisting infinitely many attractors). The dimensionality reduction modeling for a memristive circuit is carried out to realize accurate prediction, quantitative analysis, and physical control of its multistability, which has become one of the hottest research topics in the field of information science. Based on these considerations, this paper briefly reviews the specific multistability phenomenon generating from the memristive circuit in the voltage-current domain and expounds the multistability control strategy. Then, this paper introduces the accurate flux-charge constitutive relation of memristors. Afterwards, the dimensionality reduction modeling method of the memristive circuits, i.e., the incremental flux-charge analysis method, is emphatically introduced, whose core idea is to implement the explicit expressions of the initial conditions in the flux-charge model and to discuss the feasibility and effectiveness of the multistability reconstitution of the memristive circuits using their flux-charge models. Furthermore, the incremental integral transformation method for modeling of the memristive system is reviewed by following the idea of the incremental flux-charge analysis method. The theory and application promotion of the dimensionality reduction modeling and multistability reconstitution are proceeded, and the application prospect is prospected by taking the synchronization application of the memristor-coupled system as an example.

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Section: Synchronization Application Of Memristor-coupled Systemmentioning

confidence: 99%

Recent Advances in Dimensionality Reduction Modeling and Multistability Reconstitution of Memristive Circuit

2021

Self Cite

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“…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]. However, to the best of our knowledge, up to now, there are few results on the firing synchronization of two coupled neural networks with multiple neurons.…”

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

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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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“…Furthermore, because of the potential difference among neurons in the nervous system, the complex electromagnetic field induced by electromagnetic induction can be detected, similarly, and the flux-controlled memristor can be utilized as the coupled synapse to represent the coupling relationships among neurons based on the MEI theorem [26][27][28]. With the construction of a threshold memristor, Bao proposed a memristor synapse-coupled neuron network and discovered abundant firing phenomena, then achieved the complete exponential synchronization between two identical HR neurons [29]. To investigate the characteristics of the new proposed bistable threshold locally active memristor based neuron network, this new memristor is introduced to the modified FitzHugh-Nagumo(FHN) nervous system, which was proposed by [30], as a coupled synapse.…”

Section: Introductionmentioning

confidence: 99%

A new locally active memristive synapse-coupled neuron model

Wang

Dong

2021

Nonlinear Dyn

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In this paper, a new type of non-volatile locally active memristor with bi-stability is proposed by injecting appropriate voltage pulses to realize a switching mechanism between two stable states. It is found that the memristive parameters of the new memristor can affect the local activity, which has been rarely reported, and this phenomenon is explained based on mathematical analyses and numerical simulations. Then, a locally active memristive coupled neuron model is constructed using the proposed memristor as a connecting synapse. The parameter-associated dynamical behaviors are revealed by bifurcation plots, phase plane portraits and dynamical evolution maps. Moreover, the bi-stability phenomenon of the new coupled neuron model is disclosed by local attraction basins, and the periodic burster and multi-scroll chaotic burster are found if a multi-level pulse current is used to imitate a periodical external stimulus on the neurons. The Hamiltonian energy function is calculated and analyzed with or without external excitation. Finally, the neuronal circuit is designed and implemented, which can mimic electrical activity of the neurons and is useful for physical applications. The experimental results captured from the

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Memristor synapse-coupled memristive neuron network: synchronization transition and occurrence of chimera

Cited by 144 publications

References 43 publications

Recent Advances in Dimensionality Reduction Modeling and Multistability Reconstitution of Memristive Circuit

Recent Advances in Dimensionality Reduction Modeling and Multistability Reconstitution of Memristive Circuit

Neural Bursting and Synchronization Emulated by Neural Networks and Circuits

A new locally active memristive synapse-coupled neuron model

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