Hidden dynamics and control of a Filippov memristive hybrid neuron model

Qiao, Shuai; Gao, Chenghua; An, Xinlei

doi:10.1007/s11071-023-08393-y

Cited by 7 publications

(1 citation statement)

References 81 publications

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“…[10,[21][22][23][24] According to a non-smooth feedback strategy, a Filippov hybrid neuron model with electromagnetic induction can show sliding mode dynamics and coexisting attractors. [25] A Hindmarsh-Rose neuron model is improved by introducing magnetic flux and electric field variables, and the model has abundant dynamics and is applied in image encryption. [26] Neuronal models with electromagnetic induction play a significant role in exploring nonlinear dynamics and biophysical mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Dynamics and synchronization of neural models with memristive membranes under energy coupling

Wan 万,

Wu 吴,

Ma 马

et al. 2024

Chinese Phys. B

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Dynamical modeling of neural systems plays an important role in explaining and predicting some features of biophysical mechanisms. The electrophysiological environment inside and outside of the nerve cell is different. Given the continuous and periodical properties of electromagnetic fields in the cell during its working, electronic components involving two capacitors and a memristor are effective in mimicking the physical features. In this paper, a neural circuit is reconstructed by two capacitors connected by a memristor with periodical mem-conductance. It is found that the memristive neural circuit can present abundant firing patterns without stimulus. The Hamilton energy function is deduced by using the Helmholtz theorem. Further, the neuronal network consisting of memristive neurons is proposed by introducing energy coupling. The controllability and flexibility of parameters make the model have the ability to describe the dynamics and synchronization behaviors.

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