TY - JOUR
T1 - Synaptic Plasticity Engineering for Neural Precision, Temporal Learning, and Scalable Neuromorphic Systems
AU - Liu, Zhengjun
AU - Fang, Yuxiao
AU - Liu, Qing
AU - Tian, Bobo
AU - Zhao, Chun
N1 - Publisher Copyright:
© The Author(s) 2026.
PY - 2026/12
Y1 - 2026/12
N2 - 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.
AB - 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.
KW - Edge artificial intelligence
KW - Neuromorphic hardware
KW - Synaptic plasticity
UR - https://www.scopus.com/pages/publications/105026449177
U2 - 10.1007/s40820-025-02028-0
DO - 10.1007/s40820-025-02028-0
M3 - Review article
AN - SCOPUS:105026449177
SN - 2311-6706
VL - 18
JO - Nano-Micro Letters
JF - Nano-Micro Letters
IS - 1
M1 - 196
ER -