The field of time series classification and neural network interpretability is moving towards developing more efficient and transparent models. Researchers are focusing on creating methods that not only achieve high accuracy but also provide insights into the decision-making process of the models. This is particularly important in critical domains such as industry and medicine, where decisions made by models can have significant consequences. Recent developments have led to the creation of novel approaches that leverage techniques such as decision trees, Hadamard convolutional transforms, and prototypical parts to improve model interpretability and efficiency. These advancements have resulted in state-of-the-art performance on various datasets, including the UCR time series dataset, while also reducing computational complexity and training time. Noteworthy papers include: Automatically Finding Rule-Based Neurons in OthelloGPT, which presents an automated approach to identify and interpret neurons that encode rule-based game logic. HIT-ROCKET: Hadamard-vector Inner-product Transformer for ROCKET, which proposes a feature extraction approach that boosts computational efficiency, robustness, and adaptability. ProtoTSNet: Interpretable Multivariate Time Series Classification With Prototypical Parts, which introduces a novel approach to interpretable classification of multivariate time series data.