Firing Activity of Memristive Izhikevich Neural Network Under Electromagnetic Field Coupling

doi:10.19596/j.cnki.1001-246x.8582

Abstract

Abstract:

The electrophysiological environment inside and outside the neuron will generate an electric field due to the transmission and concentration of ions, which will then excite the magnetic field, and the formed electromagnetic field will work together to regulate the electrical activity of the neuron. Therefore, considering the influence of electromagnetic field, this paper introduces electric field variables and magnetic flux variables into the traditional neuron model and uses electrical synaptic coupling to construct neuron network, then studies the collective dynamic behavior of memristive Izhikevich neural network under electromagnetic field coupling. Through numerical simulation, it is found that the increase of the electrical synaptic coupling value will change the firing pattern of neurons, and make the neural network achieve synchronization. Increasing the coupling value of magnetic field can increase the firing activity of neurons, and have a beneficial effect on the network synchronization, while increasing the electric field can inhibit the electrical activity of neurons. In addition, when electrical synaptic and magnetic field coupling work together, the smaller value of the magnetic field coupling, the more effective the electrical synaptic coupling can promote network synchronization. The electric field is more effective in suppressing electrical activity given the strength of the electrical synaptic coupling. The findings are expected to provide new perspectives for understanding signal encoding and transmission in the nervous system.

Key words: Izhikevich neuron, electrical synapse, electromagnetic induction, synchronization

Limei LU, Duqu WEI. Firing Activity of Memristive Izhikevich Neural Network Under Electromagnetic Field Coupling[J]. Chinese Journal of Computational Physics, 2023, 40(4): 490-499.

Figures/Tables 9

Fig.1 The firing activity of neurons (i=40, 160) for different gb with gc=0, r=0. (a)~(c) are the time series of membrane potential of neurons when gb=0.005, 0.015, 0.05, respectively. (d)~(f) are the phase plans of neurons when gb=0.005, 0.015, 0.05, respectively.

Fig.2 Developed spatial patterns of network for different gb with gc=0, r=0 (a)gb=0.005; (b)gb=0.015; (c)gb=0.05

Fig.3 The firing activity of neurons (i=40, 160) for different gc with gb=0, r=0. (a)~(c) are the time series of membrane potential of neurons when gc=0.005, 0.01, 0.1, respectively; (d)~(f) are the phase plans of neurons when gc=0.005, 0.01, 0.1, respectively.

Fig.4 Developed spatial patterns of network for different gc with gb=0, r=0 (a)gc=0.005; (b)gc=0.01; (c)gc=0.1

Fig.5 Degree of synchronization computed in the two-parameter space (gb-gc)

Fig.6 The time series of membrane potential of neurons(i=40, 160) for different r with gb=0.02, gc=0.01 (a) r=0.000 01; (b) r=0.000 05; (c) r=0.000 08; (d) r=0.000 1

Fig.7 Developed spatial patterns of network for different r with gb=0.02, gc=0.01 (a) r=0.000 01; (b) r=0.000 05; (c) r=0.000 08; (d) r=0.000 1

Fig.8 Degree of standard deviation computed in the two-parameter space (a)r-gb; (b)r-gc

Fig.9 Degree of synchronization computed in the two-parameter space (r-gb)

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