Dynamics of Tangent-type Memory Hindmarsh-Rose Neural Networks under Electromagnetic Radiation

doi:10.19596/j.cnki.1001-246x.8847

Abstract

Abstract:

The electromagnetic environment around neurons is very complex, and it is important to study the effect of electromagnetic radiation on neuronal behavior. Electromagnetic radiation is simulated by the induced current of a magnetically controlled memristor to study the behavior of tangent-type memristor-coupled Hindmarsh-Rose (HR) neural network dynamics in a small world of NW under the effect of electromagnetic radiation. Numerical simulations have found that increasing the coupling strength promotes synchronization between neurons and alters their firing pattern; the neural network is sensitive to the initial value when the electromagnetic radiation effect is in effect, and it is also found that electromagnetic radiation enhances the energy required for neuronal firing by Hamiltonian energy. When amnesic coupling and electromagnetic radiation effect act together, the smaller the intensity of electromagnetic radiation, the more effective amnesic coupling can promote network synchronization. The experimental results show that the tangent-type amnesia-coupled neural network under the effect of electromagnetic radiation is sensitive to the state of the initial value, and the synchronization behavior and discharge activity of the neural network are related to the coupling strength and electromagnetic radiation.

Key words: Hindmarsh-Rose neurons, electromagnetic radiation effects, tangential amnesia, synchronized discharge

Zhiwei DAI, Duqu WEI. Dynamics of Tangent-type Memory Hindmarsh-Rose Neural Networks under Electromagnetic Radiation[J]. Chinese Journal of Computational Physics, 2025, 42(2): 243-252.

Figures/Tables 9

Fig.1 Time series and phase diagrams of two memristor at different k1 with HR neurons (i=40, 80) (k2=0) (a)、(d) k1=0.001;(b)、(e) k1=0.01;(c)、(f) k1=0.02

Fig.2 Spatial mode of HR neural network at different k1 (k2=0) (a) k1=0.001; (b) k1=0.01; (c) k1=0.02

Fig.3 Synchronization of HR neural networks at different memory parameter α (β=1)

Fig.4 Time series and phase diagrams of two memristor HR neurons at different k2 (i=40, 80, k1=0)(a) and (d) k2=0.3;(b) and (e) k2=0.4;(c) and (f) k2=0.5

Fig.5 Spatial mode of HR neural network at different k2 (k1=0) (a) k2=0.3; (b) k2=0.4; (c) k2=0.5

Fig.6 State switching with k1=0, k2=0.3, ϕ1=1 (a) non-negative maximum bifurcation diagram; (b) neuron membrane potential time series at ϕ2=-0.5; (c) neuron membrane potential time series at ϕ2=-0.4

Fig.7 Timing diagrams corresponding to Hamiltonian energy diagrams (a)nneuron membrane potential time series; (b)Hamilton energy map

Fig.8 Synchronization factor of (k1-k2) at two-parameters (a) ϕ2=1; (b) ϕ2=-1

Fig.9 The standard deviation of (k1-k2) σ at two-parameters (a) ϕ2=1; (b) ϕ2=-1

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