The current research in neural network activation functions and sparse autoencoders is notably advancing the field by focusing on more efficient and effective methods for both training and inference. A significant trend is the exploration of non-traditional activation functions that address common issues like the 'dying ReLU' problem while maintaining computational efficiency. Additionally, there is a growing emphasis on integrating gradient information into sparse autoencoders to better capture the downstream effects of activations, thereby improving feature extraction and model performance. The field is also witnessing innovative approaches to designing activation functions through integration techniques, which offer new ways to introduce non-linearities and potentially enhance model performance. Notably, some papers stand out for their contributions: the introduction of the Hysteresis Rectified Linear Unit (HeLU) for efficient inference and the development of Gradient Sparse Autoencoders (g-SAEs) for improved dictionary learning.