Morphological analysis and feature extraction of neurons assisted by artificial intelligence

Traditional morphological analysis of neurons relies on manual observation and measurement, which is time-consuming, laborious and limited by subjective judgment. In this study, an automatic recognition and analysis system of neuron morphology based on artificial intelligence (AI) is developed, aiming at improving the efficiency and accuracy of analysis, providing quantitative data support for neurobiological research by extracting key morphological features such as dendrite length and synapse number, and exploring the potential relationship between these morphological features and neuron function and neural network activity. In this paper, the deep convolutional neural network (DCNN) is used as the core feature extraction algorithm, and a DCNN architecture is designed specifically for neuron morphological analysis. The local features in the image are captured by multi-layer convolution operation, and the feature map size is reduced by maximum pooling operation, while the most important feature information is retained. The loss function combines regression loss and classification loss to optimize the network's ability to predict morphological characteristics and classify neuron types. The experimental results show that DCNN model performs well in feature extraction, with an average MSE of 0.02, especially in the prediction accuracy of dendrite length and branch number, and the accuracy of neuron type classification task reaches 92%. The performance evaluation on the test set shows that the model has good generalization ability, the MSE of feature extraction is 0.025, the classification accuracy is 90%, and the R value of morphological feature prediction is 0.88. This study has proved the advantages of automation, high efficiency and high accuracy of AI technology in neuron morphological analysis, and provided a powerful tool for neuroscience research.