Energy balance regulates synchronization of photosensitive neural network

Neuron is the basic unit of the structure and function of the nervous system. It has the function of receiving stimulus, producing excitement and conducting excitement. After receiving external stimulation and absorbing energy, neurons can form various firing modes. In this work, a Fitzhugh–Nagumo neuron circuit which can capture external magnetic field is constructed, and the dynamic behaviors of individual neuronal circuits and neuronal networks are analyzed by numerical simulation. It is shown that the transmission of signals between neurons must be accompanied by energy consumption. By coupling two photosensitive neurons with different initial values with a memristor, the energy balance between the coupled systems is achieved; resulting in saturation of coupling intensity, and the system achieves complete synchronization. However, for neurons in different magnetic field environments, only phase locking can be achieved. Then, the chain neural network based on memristor connection is discussed. It is found that the neural network formed by bursting discharge and spiking discharge mode can achieve complete synchronization, but the neural network of chaotic discharge cannot achieve complete synchronization.