Unsupervised Pattern Recognition with Spiking Neuron Groups

Pattern recognition problems can be solved with spiking neuron models. In this study, the pattern recognition performance of an architecture built with groups that have excitatory and inhibitory neural dynamics was investigated. Examples from the MNIST dataset were converted into spike signals and used as stimuli. The goal of the research is to create a clustering model based on spiking neurons and train it using unsupervised learning. In the study, connections between neuron groups were designed to create competition, and constraint schemes based on the winner-take-all mechanism were used. Because the labels are unknown, accuracy values cannot be calculated like in classification problems. As a result, intra-group accuracy percentages were calculated, and the accuracy of the architecture was evaluated. As a result, the results of this research show that models based on spiking neuron groups can successfully solve unsupervised pattern recognition problems.