Semi-Supervised Learning for Spiking Neural Networks Based on Spike-Timing-Dependent Plasticity

Abstract

In this study, we propose a semi-supervised learning method for spiking neural networks based on spike-timing-dependent plasticity (STDP). The spiking neural network structure of the proposed method incorporates teacher neurons and synapses, which serve the same purpose as real-life teachers, who ensure that the actions of their students do not transcend social norms. In the first stage of the proposed learning method, STDP-based supervised learning is applied. In the second stage, STDP-based unsupervised learning is conducted in the absence of any input signal to the teacher neuron. The proposed learning method classified handwritten characters with higher accuracy than the existing method. On the MNIST dataset, the proposed method was approximately 5%, 1%, and 3% more accurate than the conventional algorithm on 100, 400, and 1600 excitatory neurons, respectively.

Keywords

• Spiking neural network
• semi-supervised learning
• spike-timing dependent plasticity
• image classification