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The low power consumption and high robustness of SNNs make them a potential replacement for ANNs in noisy input scenarios. In this paper, we propose a method for using linear leakage in neuronal models of directly trained SNNs based on in-depth research in related experiments and articles, and develops a series of experiments around this method. Our answers to the questions raised in the introductory section are given here as follows.
1) By changing the leakage of LIF neurons, the linear leakage has better robustness compared to the no-leakage (IF) and conventional exponential leakage (LIF) models.
2) We demonstrate that the spiking rate decreases and the sparsity of the network increases when the leakage increases through deriving the process of integrating and firing spikes of LIF neurons based on linear leakage. In addition, we observe that the effect of the linear leakage parameter on the pulse firing rate somewhat affects the classification accuracy of the network.
3) It is experimentally demonstrated that linear leakage enhances the robustness of SNN absolute accuracy by 25% compared to conventional exponential leakage under specific parameters. Also, this robustness is generalizable across different datasets and network models.
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IEEE/ICACT20230030 Slide.25
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