Spiking Sequence Label-Based Spatio-Temporal Back-Propagation Algorithm for Training Deep Spiking Neural Networks

WANG Zihua; YE Ying; LIU Hongyun; XU Yan; FAN Yubo; WANG Weidong

doi:10.11999/JEIT230705

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WANG Zihua, YE Ying, LIU Hongyun, XU Yan, FAN Yubo, WANG Weidong. Spiking Sequence Label-Based Spatio-Temporal Back-Propagation Algorithm for Training Deep Spiking Neural Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230705

Citation:

WANG Zihua, YE Ying, LIU Hongyun, XU Yan, FAN Yubo, WANG Weidong. Spiking Sequence Label-Based Spatio-Temporal Back-Propagation Algorithm for Training Deep Spiking Neural Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230705

Citation:

WANG Zihua, YE Ying, LIU Hongyun, XU Yan, FAN Yubo, WANG Weidong. Spiking Sequence Label-Based Spatio-Temporal Back-Propagation Algorithm for Training Deep Spiking Neural Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230705

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Spiking Sequence Label-Based Spatio-Temporal Back-Propagation Algorithm for Training Deep Spiking Neural Networks

doi: 10.11999/JEIT230705

WANG Zihua¹,
YE Ying^{1, 4},
LIU Hongyun^{2, 3},
XU Yan¹,
FAN Yubo¹,
WANG Weidong^{1, 2, 3
,
,}

1.
School of Biological and Medical Engineering, Beihang University, Beijing 100191, China
2.
Research Center for Biomedical Engineering, Medical Innovation & Research Division, Chinese PLA General Hospital, Beijing 100853, China
3.
Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Beijing 100853, China
4.
North China Institute of Computing Technology, Beijing 100083, China

Funds: The Scientific and Technological Innovation 2030 - “New Generation Artificial Intelligence” Major Project (2020AAA0105800)

Received Date: 2023-07-15
Rev Recd Date: 2024-03-12

Available Online: 2024-04-09

Abstract

Abstract

Spiking Neural Networks (SNN) have a signal processing mode close to the cerebral cortex, which is considered to be an important approach to realize brain-inspired computing. However, the lack of effective supervised learning algorithms for deep spiking neural networks has been a great challenge for spiking sequence label-based brain-inspired computing tasks. A supervised learning algorithm for training deep spiking neural network is proposed in this paper. It is an error backpropagation algorithm that uses surrogate gradient to solve the problem of non-differentiable spike generation function, and define the postsynaptic potential and membrane potential reversal iteration factors represent the spatial and temporal dependencies of pulsed neurons, respectively. It differs from existing learning algorithms based on firing rate encoding, fully reflects analytically the temporal dynamic properties of the spiking neuron. Therefore, the algorithm proposed in this paper is well-suited for application to tasks that require longer time sequences rather than spiking firing rates, such as behavior control. Proposed algorithm is validated on the static image datasets CIFAR10, and the neuromorphic dataset NMNIST. It shows good performance on all these datasets, which helps to further investigate spike-based brain-inspired computation.
- Spiking Neural Networks (SNN),
- Supervised learning,
- Error backpropagation,
- Temporal spike sequence labeling,
- Surrogate gradient

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References(24)

References

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