RETRACTED ARTICLE: Monitoring of mountain ecological environment based on Bayesian estimation and testing of motor memory function in mice

Abstract

In this paper, the Bayesian estimation method is used to estimate the coefficients of wavelet transform to reconstruct the desired signal and reduce the noise. Using SNR and RMS error as noise reduction estimation indexes, we compare Bayesian estimation algorithm and global threshold algorithm, which improves the shortcomings of threshold algorithm and adaptive threshold algorithm. According to Bayesian estimation, according to the geological vulnerability characteristics of the mountain ecological environment, the geological change and its promotion mechanism of the mountain environment are selected. The paper analyzes the influence of LUCC remote sensing monitoring and dynamic changes, such as rainfall erosion and topography, and the influence of individual natural management factors, and analyzes the propulsion effect of nearby residential areas. It combines natural management with artificial management, and discusses deeply in various aspects, including the assessment and analysis of environmental geological vulnerability, and obtains relevant research results. This paper studies the function of motor memory in mice experimental model, and provides experimental basis for PD treatment in GTP. The methods were 25-30g male mice were randomly divided into normal group, control group, low dose GTP group, and high dose GTP group. After modeling mice in the control group, low dose GTP group, and high dose GTP group, climbing bar, and other experiments were carried out to detect. By studying Bayesian estimation and mountain ecological environment monitoring, it was applied to the detection of motor memory function in mice, which promoted the development of motor memory function.