Synaptic Plasticity Engineering for Neural Precision, Temporal Learning, and Scalable Neuromorphic Systems

This review provides an in-depth discussion of computing-unit optimization through synaptic plasticity engineering, enabling precise weight modulation in spatial models and effective temporal information processing in dynamic neural networks. It delves into algorithmic advancement through plasticity modulation, improving accuracy, stability, and convergence in neuromorphic computing models. It explores resource-efficient neuromorphic architectures, integrating multifunctional devices, multimodal fusion, and heterogeneous arrays for scalable, low-power, and generalizable intelligent systems.