The field of neural population dynamics and EEG decoding is rapidly advancing, with a focus on developing more efficient and accurate models for understanding brain function and behavior. Recent work has centered on improving the computational efficiency and fidelity of neural population models, with the introduction of novel energy-based and diffusion-based frameworks. Additionally, there has been a growing interest in developing more robust and generalizable EEG decoding methods, including the use of spatial embedding, differential Mamba, and prototype-guided continual learning. These advances have the potential to significantly improve our understanding of brain function and behavior, with applications in neuroscience research, neural engineering, and brain-computer interfaces. Noteworthy papers include: Energy-based Autoregressive Generation for Neural Population Dynamics, which achieves state-of-the-art generation quality with substantial computational efficiency improvements. MultiDiffNet, a diffusion-based framework that bypasses generative augmentation entirely by learning a compact latent space optimized for multiple objectives, achieving state-of-the-art generalization across various neural decoding tasks.