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For the above problem, this paper conducts a series of SNN robustness experiments around linear leakage. To the best of our knowledge, this paper is the first to propose the use of linear leakage on LIF neurons with directly trained SNNs (explained in Section 4.1).The key contributions can be summarized as follows:
We propose a method that uses linear leakage on LIF neurons of direct training of SNN.
We analyze the process of integrating and firing spikes based on linear leakage of LIF neurons, and demonstrates the inverse relationship between the leakage parameters ¥ó_m and spiking rate (1/T) in linear leakage.
In this paper, a linear leakage method is applied on three different network models based on MNIST and Cifar10 datasets. After experimental comparison and analysis, we found that the linear leakage method in this paper has stronger robustness compared to the no-leakage and exponential leakage approaches.
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IEEE/ICACT20230030 Slide.06
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