MSF, or main stability function, is a method that uses Lyapunov exponent theory to determine the stability of synchronization states in complex homogeneous networks. A negative MSF value indicates that the network can synchronize. In this paper, a twovariable HR model is proposed to describe the synchronization behavior of neurons under the effect of an electric field by a simplified MSF method, with the neuron size and applied electric field as regulatory factors. The relationship among the main stability function MSF, the charge size and applied electric field is studied. The results show that the electric field has a rich effect on neural network synchronization. A strong constant electric field can promote network synchronization, while An alternating electric field can inhibit synchronization. In addition, the radius of the neuron also affects network synchronization. Under a larger radius of the neuron, the neural network will be easier to synchronize.