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Spatiotemporal Backpropagation based on Channel Reward for Training High-Precision Spiking Neural Network

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Abstract

Spiking neural network (SNN) has attracted much attention due to its spatial–temporal information processing ability and high biological reliability. SNN is a hardware-friendly model based on event-driven and sparse triggering, which can achieve low power consumption and efficient information processing on neural morphological chips. However, SNN processes discrete spike trains, and its unique working mode makes it more difficult to train than traditional networks. Based on this, this paper proposes a spatiotemporal backpropagation (STBP) algorithm for directly training high-performance SNN. By narrowing the coding time window, we convert the leaky integrate-and-fire (LIF) into its explicit iterative version for direct SNN training. To solve the problem of non-differentiability of SNN discrete spikes, we introduce an approximate derivative of spike firing for gradient descent training. We propose a biologically reasonable channel reward (CR) mechanism integrated into the STBP algorithm. This method makes full use of the spatial domain (SD) and time domain (TD) information of input and does not require any additional complex techniques. We evaluate the proposed algorithm on traditional static MNIST, Fashion-MNIST datasets, and neuromorphological N-MNIST, DVS128 Gesture datasets. The experimental results show that the accuracy of this method in static and neuromorphological datasets exceeds the current advanced methods, and the time window used is small. This work provides a new perspective for directly training high-performance SNN.

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Funding

This work is supported by National Nature Science Foundation of China (grant No. 61871106), China (grant No. 2020JH2/10100029), and the Open Project Program Foundation of the Key Laboratory of Opto-Electronics Information Processing, Chinese Academy of Sciences (OEIPO-202002).

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Authors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, Shenyang, 110004, China

    Li-Ye Niu, Ying Wei, Yue Liu, Jun-Yu Long & Wen-Bo Liu

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

    Ying Wei

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Contributions

Li-Ye Niu wrote the main manuscript text and prepared figures. Ying Wei put forward some suggestions for revising the experimental design of the article. Yue Liu proofread the grammar and typesetting of the article. Jun-Yu Long and Wen-Bo Liu draw the table of the article. All authors reviewed the manuscript.

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Correspondence to Ying Wei.

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Niu, LY., Wei, Y., Liu, Y. et al. Spatiotemporal Backpropagation based on Channel Reward for Training High-Precision Spiking Neural Network. SIViP 17, 3467–3476 (2023). https://doi.org/10.1007/s11760-023-02569-0

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