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.

Based on the improved electromagnetic induction neuron model, the coherent resonance (CR) phenomenon of the Memristor Hindmarsh-Rose (HR) network is studied. In addition to electrical coupling connecting gaps between adjacent neurons, magnetic coupling is used to describe the effect of field coupling between neurons. The dynamic analysis of the stable point is performed using the bifurcation diagram and phase diagram, and the potential dynamic mechanism of the emergence of CR and the change of discharge mode are explained. It is found that white Gaussian noise can induce CR in the resting state near the subcritical Hopf bifurcation of memristor neurons, and the occurrence of CR is related to the change of firing mode caused by the increase of noise amplitude.

In this paper, chaos prediction of motor system is realized based on the next generation reservoir computing, and unknown variable data can be predicted based on existing data. Compared with the traditional reservoir computing, the next generation reservoir computing uses direct connection of data itself and requires smaller training data sets. And the next generation reservoir computing avoids the complex parameter optimization calculation of traditional reserve pool network through high-dimensional conversion, which greatly improves the computing speed. This research result provides a new research idea for chaotic prediction of motor systems.

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.

We investigate collective dynamics of memristor Rulkov neural networks depend on electrical synapses and chemical synapses. It is found that for two memristive Rulkov neurons, the system can be synchronized regardless of coupling mode. At different coupling strengths, the neurons present different firing patterns, such as square wave, triangular wave, pulse firing, etc. As electrical synapses and chemical synapses coexist, synchronization of the system is more dependent on the strength of electrical coupling. Synchronization of globally coupled memristive Rulkov neural networks is studied. It is shown that as chemical synapses act alone, synchronization occurs within a certain region of coupling parameters. The synchronization is disrupted as the chemical coupling strength exceeds a certain threshold. As electrical synapses act alone, the system can reach a synchronized state quickly. It is also found that electrical coupling strength is the key factor to determine whether neurons are at rest or firing. As electrical coupling strength increases, firing frequency and amplitude of neuron increase. As electrical and chemical couplings coexist, the increase of coupling strength makes the neurons turn into arc discharge and reach synchronization. It provides a possible way to control firing patterns and synchronization of neural networks by adjusting coupling pattern and coupling strength.

A deep learning algorithm using deep long short-term memory and genetic attention mechanism (DLSTM-GA) is proposed for the prediction of chaotic behavior of power system. With shared parameters, attention mechanism is added to optimize DLSTM model based on genetic algorithm. One can find potential characteristics in time sequence and avoid the local optimization. Inspired by evolutionary computation of optimization method, the method is a good way to learn parameters in the attention layer. It shows that the trained DLSTM-GA network not only has higher prediction accuracy than the reference model, but also has long-term prediction ability.