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

TL;DR

This review discusses how engineering synaptic plasticity improves the precision, learning, and scalability of neuromorphic computing systems.

Contribution

The paper introduces novel strategies for dynamic plasticity design in neuromorphic systems, enhancing computational accuracy and adaptability.

Findings

Diversified plasticity behaviors support stable learning and temporal processing in neuromorphic models.

Multifunctional device integration enables compact and energy-efficient neuromorphic architectures.

Array-level developments demonstrate scalability and system-level applicability of plasticity engineering.

Abstract

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.

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.

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.

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