Incremental learning algorithm for spatio-temporal spike pattern classification

In a previous work (Mohemmed et al. [11]), the authors proposed a supervised learning algorithm to train a spiking neuron to associate input/output spike patterns. In this paper, the association learning rule is applied in training a single layer of spiking neurons to classify multiclass spike patterns whereby the neurons are trained to recognize an input spike pattern by emitting a predetermined spike train. The training is performed in incremental fashion, i.e. the synaptic weights are adjusted after each presentation of a training pattern. The individual neurons are trained independently from other neurons and on patterns from a single class. A spike train comparison criterion is used to decode the output spike trains into class labels. The results of the simulation experiments on a synthetic dataset of spike patterns show a high efficiency in solving the considered classification task.