The field of spiking neural networks (SNNs) is rapidly advancing, with a focus on developing more efficient and adaptive computing models. Recent research has explored the potential of SNNs for various applications, including robotic manipulation, stereo image restoration, and multi-object tracking. A key trend in this area is the development of novel training methods and architectures that can effectively harness the strengths of SNNs, such as their low power consumption and ability to process temporal data. Notably, researchers are investigating the use of multi-plasticity synergy, self-ensemble inspired approaches, and reinforcement learning-based optimization to improve the performance and robustness of SNNs. These innovations have the potential to enable real-time, low-power applications in areas such as computer vision and robotics. Noteworthy papers in this area include: Fully Spiking Actor-Critic Neural Network for Robotic Manipulation, which proposes a hybrid curriculum reinforcement learning framework for robotic arms. SMTrack: End-to-End Trained Spiking Neural Networks for Multi-Object Tracking in RGB Videos, which introduces an adaptive and scale-aware loss function for multi-object tracking tasks.