A Deep Spike Learning through Critical Time Points

In addition to biological plausibility, spiking neural networks (SNNs) are drawing significant attention recently due to their promising advantages in computational efficiency, which could potentially help to overcome the consumption obstacle in deep learning. Training deep SNNs is of great importance for solving practical tasks. In this paper, we propose a new deep spike learning rule to train deep SNNs to associate input spike patterns with desired output spike numbers. Our proposed rule is able to construct error signals based on a critical time point that is likely close to change the neuron's response toward its desired. We evaluate the performance of our method with both static and dynamic vision datasets. Experimental results show that the proposed rule can effectively learn spike patterns encoded with both rate and temporal codes, and more importantly, achieves impressive accuracies on all benchmark datasets. We further provide a comprehensive analysis of both codes with respect to efficiency and robustness. Our study thus provides an effective rule that is generalized to process information under a broad range of coding schemes, which would be of great merit for spike-based learning and processing.