Solving the Multi-class Classification Task in Spiking Neural Network by using Supervised Spiking Learning Rule with a Consistent Competitive Mechanism

Abstract

In recent years, spiking neural networks (SNNs), a computing model inspired by the brain's ability to code and process information in the time domain with great computational power, has drawn a lot of attention from researchers for learning applications. For training in SNNs, several supervised spiking learning rules have been proposed, however, applying these learning algorithms to real-world problems yet remains an open issue. For this reason, this paper presents a new spiking neural network for the handwritten digit dataset classification problem. Our proposed network is trained by using the spike-based NormAD algorithm with a consistent winner-take-all mechanism. The experiment has shown a promising performance just after one epoch passing over the test dataset.