The research landscape in Few-Shot Class-Incremental Learning (FSCIL) is witnessing significant advancements aimed at addressing the challenges of evolving data distributions and mitigating catastrophic forgetting. Innovations are focusing on enhancing knowledge distillation techniques, with a particular emphasis on incorporating structural information and displacement knowledge to improve feature representation quality. Dual distillation networks and adaptive logit alignment methods are emerging as powerful strategies to balance performance between base and novel classes, ensuring robust learning with minimal data. Additionally, the integration of bio-inspired neural network architectures, such as spiking neural networks with dynamic structures, is offering energy-efficient solutions for real-time data processing. These approaches not only improve generalization across diverse domains but also provide flexible frameworks that can be easily adapted to existing FSCIL methods. Notably, the use of representation space guided inversion and structured Hebbian plasticity is demonstrating promising results in specialized applications like physiological signal analysis, where data scarcity is a common challenge. Overall, the field is progressing towards more adaptive, efficient, and privacy-conscious learning models that can seamlessly integrate new information while preserving prior knowledge.