  For the gradient computation across the time domain in Spiking Neural
Networks (SNNs) training, two different approaches have been independently
studied. The first is to compute the gradients with respect to the change in
spike activation (activation-based methods), and the second is to compute the
gradients with respect to the change in spike timing (timing-based methods). In
this work, we present a comparative study of the two methods and propose a new
supervised learning method that combines them. The proposed method utilizes
each individual spike more effectively by shifting spike timings as in the
timing-based methods as well as generating and removing spikes as in the
activation-based methods. Experimental results showed that the proposed method
achieves higher performance in terms of both accuracy and efficiency than the
previous approaches.
