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There are two common approaches to train SNN:
Pre-training ANN Model and Converting to SNN: this approach[10-12] usually requires a relatively larger time step, which reduces information loss while also introducing higher latency and energy consumption, while this method ignores the SNN time domain information, which is contrary to the original purpose of SNN.
Direct Training of SNN: this approach can be divided into unsupervised training and supervised training. The former uses the Spike Time Dependent Plasticity (STDP) rule [7], this method is often limited to shallow structures and cannot be applied to complex datasets [3]. The latter uses the gradient substitution method to solve the problem of unsteady SNN gradients.Wu [1] et al. proposed the STBP method and applied it to deep SNNs [2,9], smaller time steps are used while taking into account temporal and spatial information, thus achieving higher accuracy rates in large data sets. In this experiment, we use the STBP framework as baseline.
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IEEE/ICACT20230030 Slide.16
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