Learning weights with STDP to build prototype images for classification

Abstract

The combination of Spike Timing Dependent Plasticity (STDP) and latency coding used in a spiking neural network has been shown to learn hierarchical features. In this paper we propose a new way to classify images using an SVM. Prototype images are built from the weights learned in an unsupervised manner using STDP. The prototype images are cross correlated with the input image and the peak of the cross correlation with each prototype image is used as additional features for an SVM. The network, demonstrated on the MNIST data set, achieves 99.15% testing accuracy which is the best reported accuracy for a SNN with unsupervised training.