The recent developments in the field of spiking neural networks (SNNs) and neuromorphic computing have shown a significant shift towards enhancing both computational efficiency and task accuracy. Researchers are increasingly focusing on integrating biologically inspired principles with advanced machine learning techniques to address the unique challenges of processing spatio-temporal data on edge devices. Innovations in network architectures, such as the incorporation of explicit temporal delay connections and the development of joint embedding predictive architectures, are paving the way for more efficient and accurate models. Additionally, methods that leverage noise-injected low-bit artificial neural networks for constructing SNNs are demonstrating improvements in accuracy and latency. The field is also witnessing advancements in initialization techniques tailored specifically for SNNs, which are crucial for stabilizing signal propagation and enhancing generalization. These developments collectively aim to bridge the performance gap between SNNs and traditional neural networks, particularly in complex visual tasks like object detection and image classification. The integration of attention mechanisms and optimized pulse modules in SNN architectures is further enhancing the computational efficacy of neuromorphic systems. Overall, the trend is towards creating more energy-efficient, high-performance models that can be deployed in real-world applications, especially in energy-constrained environments.