Deep neural networks can extract a hierarchy of relevant features from the data that can be used both for classification and generation task, reaching state-of-the-art performance in object/speech recognition and language translation. However, many characteristics of the way these networks process the information or, more in general, the reason why they work so well are still unclear. In this work we analyze some features of a deep belief network during training across different layers in an unsupervised setting. First of all we study how the plasticity varies across the network’s layers, computing the variation of the architecture’s weights when the dataset to be learned is changed.