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Background/Objectives: Under the background of educational informatization, the realization of education modernization and information education is the inevitable trend of future education development. As the backbone of the future teacher team, the level of information technology application ability of normal university students is the key to the modernization of future education. Therefore, the ministry of education proposed to strengthen the cultivation of information literacy of normal university students. Methods/Statistical analysis: As the cradle of basic education teachers in yanbian prefecture, yanbian university should strengthen the cultivation of information technology application ability of normal university students and study the current situation of the cultivation. In this paper, two questionnaires were conducted before and after the training of normal university students in yanbian university. Findings: Through the comparison and analysis of the two data by SPSS software, it is found that there is no specific training for normal university students in the training of information technology application ability of normal university students in yanbian university. Teaching less theoretical knowledge; Students have fewer opportunities to practice the problem. Improvements/Applications: Finally, the author puts forward some strategies for the future cultivation of information technology application ability of normal university students in yanbian university. For the training of excellent information teachers to contribute.

Abstract. Statistical analysis results have shown that small-world characteristics are widespread in the cerebral cortex neuron network, which has long been proven an effective method to investigate the brain anatomy and functions. From the perspective of hardware, we built the FHN neural network model with small-world connectivity using FPGA （Field Programmable Gate Array） . As a result, we observe the properties of neuron membrane potential are in line with the small-world characteristic of FHN neuron network model when impressed sinusoidal current with different frequencies. By the aforementioned rules, this paper demonstrates the effectiveness, and may underlie the construction of cerebral cortex network in the future.