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Physical neural networks with self-learning capabilities

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Physical neural networks are artificial neural networks that mimic synapses and neurons using physical systems or materials.These networks harness the distinctive characteristics of physical systems to carry out computations effectively,potentially sur-passing the constraints of conventional digital neural networks.A recent advancement known as"physical self-learning"aims to achieve learning through intrinsic physical processes rather than relying on external computations.This article offers a compre-hensive review of the progress made in implementing physical self-learning across various physical systems.Prevailing learning strategies that contribute to the realization of physical self-learning are discussed.Despite challenges in understanding the funda-mental mechanism of learning,this work highlights the progress towards constructing intelligent hardware from the ground up,incorporating embedded self-organizing and self-adaptive dynamics in physical systems.

self-learningphysical neural networksneuromorphic computingphysical learning

Weichao Yu、Hangwen Guo、Jiang Xiao、Jian Shen

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State Key Laboratory of Surface Physics and Institute for Nanoelectronic Devices and Quantum Computing,Fudan University,Shanghai 200433,China

Zhangjiang Fudan International Innovation Center,Fudan University,Shanghai 201210,China

Shanghai Research Center for Quantum Sciences,Shanghai 201315,China

Zhangjiang Fudan International Innovation Center Fudan University,Shanghai 201210,China

Department of Physics,Fudan University,Shanghai 200433,China

Collaborative Innovation Center of Advanced Microstructures,Nanjing 210093,China

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National Key Research and Development Program of ChinaNational Key Research and Development Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaShanghai Municipal Science and Technology Major ProjectShanghai Pujiang ProgramShanghai Science and Technology CommitteeShanghai Science and Technology Committee

2022YFA14033002020YFA030910012204107120740732019SHZDZX0121PJ140150021JC140620020JC1415900

2024

10.1007/s11433-024-2403-x

中国科学:物理学 力学 天文学(英文版)
中国科学院

中国科学:物理学 力学 天文学(英文版)

CSTPCD
影响因子：0.91
ISSN：1674-7348
年,卷(期)：2024.67(8)