Dynamical effects of memristive electromagnetic induction on a 2D Wilson neuron model

Xu, Quan; Wang, Kai; Shan, Yufan; Wu, Huagan; Chen, Mo; Wang, Ning

doi:10.1007/s11571-023-10014-8

Cited by 19 publications

(1 citation statement)

References 49 publications

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“…The nervous system is an extremely complex nonlinear dynamic system composed of countless neurons and synapses. In order to further investigate the significant impact of neuronal interactions and mock the functional structure of the brain's nervous system, scientists have constructed various artificial neural network models, including Hodgkin-Huxley (HH) neuron model [1], Hindmarsh-Rose (HR) neuron model [2], FitzHugh-Nagumo (FHN) neuron model [3,4], Morris-Lecar (ML) neuron model [5], Wilson neuron modell [6], Hopfield neural network (HNN) [7]. Among them, Hopfield neural network has been widely studied due to its straightforward mathematical model and complicated dynamic behavior, including chaos [8], hyperchaos [9], asymmetric attractor [10], hidden attractor [11], transient chaos [12], coexisting attractors [13], and other rich dynamic phenomena.…”

Section: Introductionmentioning

confidence: 99%

A novel memristor Hopfield neural network with homogeneous coexisting multi-scroll attractors

Huang,

Chen,

Yang

et al. 2024

Phys. Scr.

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Compared to conventional single-scroll or double-scroll attractors, multi-scroll chaotic attractors possess wide potential for application due to their adjustability and complex topology. However, neural network models for generating multiple scrolls are often implemented using memristors with piecewise nonlinear functions. To further explore multi-scroll attractors with different working mechanisms,a unique memristor containing a group of hyperbolic tangent functions is designed and then applied in a three-dimensional Hopfield neural network (HNN). The proposed memristive Hopfield neural network (MHNN) has multi-scroll chaotic attractors, where the number and parity of the scrolls be changed by adjusting the control parameters of the memristor. The complex dynamical behaviors of MHNN are studied by utilizing diverse numerical modeling approaches like bifurcation diagrams, Lyapunov exponents and phase plot. In addition, the proposed MHNN also has a complicated offset boosting coexisting behavior. By selecting suitable parameters, multiple coexisting chaotic attractors could be obtained. Homogeneous coexisting multi-scroll attractors can be shifted in multiple directions including unidirectional, planar and spatial ones. Moreover, theoretically speaking, there could be an infinite number of coexisting attractors. Finally, experimental results are validated through numerical simulations and circuit experiments to confirm the feasibility of the proposed MHNN model.

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