The field of neural networks is moving towards a deeper understanding of how these networks learn and represent features. Recent research has focused on developing new theoretical frameworks and tools to analyze the behavior of neural networks, particularly in relation to their ability to store and retrieve information. One key area of advancement is in the development of new learning algorithms and models that can capture complex representations and dynamics, such as those found in stochastic dynamical systems. Another important direction is the study of neural networks through the lens of associative memories, which provides a new perspective on the computation and representation of information in these networks. Noteworthy papers in this area include the introduction of the Features At Convergence Theorem, which provides a new understanding of how neural networks learn and represent features, and the development of KPFlow, which offers a novel operator-based perspective on dynamic collapse under gradient descent training of recurrent networks. Additionally, research has shown that neural networks can intrinsically discover and represent beliefs over quantum and post-quantum low-dimensional generative models of their training data.