The recent developments in the research area of network dynamics and learning mechanisms have shown significant advancements in both theoretical and practical aspects. The field is moving towards more biologically plausible models and methods that can handle complex, dynamic systems. One notable trend is the integration of neural networks with symbolic regression techniques to better understand and predict the dynamics of complex networks. This approach leverages neural networks for trajectory augmentation and denoising, combined with genetic programming for symbolic expression derivation, leading to improved performance in recovering network dynamics. Another emerging direction is the application of network representation learning to biophysical neural networks, which aims to uncover correlations between neuronal and synaptic dynamics. This involves the use of attention mechanisms and computational graphs to capture intricate network features and information flows. Additionally, there is a growing interest in alternative learning mechanisms that do not rely on gradient descent, such as the Ornstein-Uhlenbeck adaptation, which leverages noise and global reinforcement signals to balance exploration and exploitation in learning. This method shows promise in neuromorphic computing and could provide insights into noise-driven learning in biological systems. Furthermore, the use of Koopman theory to interpret temporal graph neural networks is gaining traction, offering new ways to explain the dynamics learned by these models and identify relevant patterns in spatiotemporal data. Overall, the field is advancing towards more interpretable, biologically inspired models that can handle the complexities of dynamic systems.