The field of spiking neural networks (SNNs) and neuromorphic computing is rapidly advancing, with a focus on improving energy efficiency and performance. Recent research has explored novel spiking neuron models, such as the Integer Binary-Range Alignment Leaky Integrate-and-Fire, which has been shown to exponentially expand the information expression capacity of spiking neurons. Other works have investigated the integration of SNNs with complexity measures, such as Lempel-Ziv Complexity, to enhance interpretability and accuracy in medical image recognition tasks. Furthermore, there is a growing interest in developing practical guides for tuning SNN dynamics and designing energy-aware neuromorphic implantables. Noteworthy papers in this area include the proposal of a novel breast cancer classification approach using SNNs and Lempel-Ziv Complexity, which achieved an accuracy of 98.25% on the Breast Cancer Wisconsin dataset, and the development of a lightweight and interpretable spiking neural model for Alzheimer's disease diagnosis, which demonstrated competitive performance with substantially improved efficiency and stability. Additionally, research on formalizing neuromorphic control systems and optimizing sparse matrix reordering for sparse matrix-vector multiplication has also shown promising results. Overall, the field is moving towards more efficient, flexible, and biologically plausible neural network models and architectures.