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Research Progress of spiking neural network in image classification: a review

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Abstract

Spiking neural network (SNN) is a new generation of artificial neural networks (ANNs), which is more analogous with the brain. It has been widely considered with neural computing and brain-like intelligence. SNN is a sparse trigger event-driven model, and it has the characteristics of hardware friendliness and energy saving. SNN is more suitable for hardware implementation and rapid information processing. SNN is also a powerful method for deep learning (DL) to study brain-like computing. In this paper, the common SNN learning and training methods in the field of image classification are reviewed. In detail, we examine the SNN algorithms based on synaptic plasticity, approximate backpropagation (BP), and ANN to SNN. This paper comprehensively introduces and tracks the latest progress of SNN. On this basis, we also analyze and discuss the challenges and opportunities it faces. Finally, this paper prospects for the future development of SNN in the aspects of the biological mechanism, network training and design, computing platform, and interdisciplinary communication. This review can provide a reference for the research of SNN to promote its application in complex tasks.

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Acknowledgements

This work is supported by National Nature Science Foundation of China (grant No.61871106), and the Open Project Program Foundation of the Key Laboratory of Opto-Electronics Information Processing, Chinese Academy of Sciences (OEIP-O-202002).

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  1. College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China

    Li-Ye Niu, Ying Wei, Wen-Bo Liu, Jun-Yu Long & Tian-hao Xue

  2. Information Technology R&D Innovation Center of Peking University, Shaoxing, China

    Ying Wei

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Niu, LY., Wei, Y., Liu, WB. et al. Research Progress of spiking neural network in image classification: a review. Appl Intell 53, 19466–19490 (2023). https://doi.org/10.1007/s10489-023-04553-0

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