Three stages of model’s evaluation.

Firstly, the SNN is trained with STDP on the training set without supervisory labels. Then the fixed network is run on the training set, and the output of the pooling layer and the corresponding labels in the training set, i.e. training labels, which are the corresponding labels of currently processed input samples, are used to train the classifier. Finally, the classifier is run to predict the labels of the test data, which are called as predicted labels. The classification accuracy of the model is evaluated by comparing the predicted labels with the corresponding ground truth labels.