# Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks Source: [Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:41:59.280Z Authors: Jinseok Kim, Kyungsu Kim, Jae-Joon Kim Published: 2020-06-04T04:47:01.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on MNIST (test) (ANN vs SNN Accuracy Comparison)](): Image Classification; MNIST (test); SNN Accuracy: 97.6. ## Original sources and implementation [Original paper]() [PDF]()