Issue

Vol. 18 (2026)

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

https://doi.org/10.1007/s40820-025-02028-0
Zhengjun Liu
School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou 215123, People’s Republic of China
Yuxiao Fang
School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou 215123, People’s Republic of China
Qing Liu
School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou 215123, People’s Republic of China
Bobo Tian
Chongqing Key Laboratory of Precision Optics, Chongqing Institute of East China Normal University, Chongqing 401120, People’s Republic of China
Chun Zhao
School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou 215123, People’s Republic of China

Corresponding Author: Chun Zhao

Chun.Zhao@xjtlu.edu.cn

Nano-Micro Letters, Vol. 18 (2026), Article Number: 196

Abstract

Manipulating the expression of synaptic plasticity in neuromorphic devices provides essential foundations for developing intelligent, adaptive hardware systems. In recent years, advances have shifted from static emulation toward dynamic, network-oriented plasticity design, offering enhanced computational accuracy and functional relevance. This review highlights how diversified plasticity behaviors, including multilevel long-term potentiation and depression for spatial models, tunable short-term memory for temporal models, as well as wavelength-selective response, excitatory and inhibitory synergy, and adaptive threshold modulation, collectively support key tasks such as stable learning, temporal processing, and context-aware adaptation. Beyond behavioral innovations, strategies such as multifunctional single-device integration, multimodal fusion, and heterogeneous system assembly enable compact, energy-efficient, and versatile neuromorphic architectures. Recent developments at the array level further demonstrate high-performance scalability and system-level applicability. Despite notable progress, current modulation strategies remain constrained in flexibility, diversity, and large-scale coordination. Future research should focus on enriching the behavioral repertoire of plasticity, advancing cross-modal convergence, and improving array-level uniformity, paving the way toward deployable, high-efficiency neuromorphic intelligence.


Highlights:
1 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.
2 It delves into algorithmic advancement through plasticity modulation, improving accuracy, stability, and convergence in neuromorphic computing models.
3 It explores resource-efficient neuromorphic architectures, integrating multifunctional devices, multimodal fusion, and heterogeneous arrays for scalable, low-power, and generalizable intelligent systems.

Keywords

Neuromorphic hardware Synaptic plasticity Edge artificial intelligence
Liu, Zhengjun, Yuxiao Fang, Qing Liu, Bobo Tian, and Chun Zhao. 2026. “Synaptic Plasticity Engineering for Neural Precision, Temporal Learning, and Scalable Neuromorphic Systems”. Nano-Micro Letters 18 (January):196. https://doi.org/10.1007/s40820-025-02028-0.
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