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Poster
Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks
Jinseok Kim · Kyungsu Kim · Jae-Joon Kim

Wed Dec 09 09:00 PM -- 11:00 PM (PST) @ Poster Session 4 #1175

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.

Author Information

Jinseok Kim (Pohang University of Science and Technology (POSTECH))
Kyungsu Kim (POSTECH)
Jae-Joon Kim (POSTECH)