Neurobiological study on the relationship between biological growth environment and psychological resilience

This article aims to explore the neurobiological relationship between biological growth environment and psychological resilience, and adopts advanced neuroimaging technology and deep learning model as research methods. By combining Convolutional Neural Network (CNN) and recurrent neural network(RNN), we hope to capture the spatial characteristics and temporal dynamics of brain activity, and then deeply understand how the growing environment affects the individual's psychological resilience. In terms of methods, this study designed specific network parameters, including batch size, iteration period and initial learning rate, to optimize the learning effect of the network. Through the training and analysis of neuroimaging data, the accuracy and loss error rate of the network in training set and test set are evaluated. The results show that under the specific network settings, the network shows excellent learning performance, and the accuracy of training set and test set has reached a high level, while the loss error rate has remained at a low level. This shows that the network can effectively learn and generalize to unknown data. Through further research and exploration, it is expected to have a deeper understanding of the influence of the growing environment on psychological resilience.