The field of artificial intelligence is witnessing significant advancements in continual learning and adaptive systems, with a growing emphasis on neuroscience-inspired approaches. Recent developments have focused on mitigating catastrophic forgetting, improving task retention, and enhancing transfer efficiency across multiple benchmark datasets. The integration of biologically-inspired mechanisms, such as predictive coding and associative memory, has shown promising results in addressing these challenges. Furthermore, the development of modular cognitive architectures and multimodal memory frameworks is enabling more versatile and adaptive AI systems. Noteworthy papers in this area include Neuroscience-Inspired Memory Replay for Continual Learning, which demonstrates the effectiveness of predictive coding-based generative replay in mitigating forgetting, and MemVerse, a model-agnostic memory framework that enables scalable and adaptive multimodal intelligence. Additionally, the Nemosine Framework presents a modular cognitive architecture for assisted reasoning, while Directed Evolution Algorithm drives neural prediction and Forget Less, Retain More proposes a lightweight regularizer for rehearsal-based continual learning. These innovative approaches are advancing the field of AI and paving the way for more efficient and adaptive systems.