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A new learning rule for classification of spatiotemporal spike patterns

In this paper, we present a new learning rule for classification of spatiotemporal spike patterns. This rule is derived from the common Widrow-Hoff rule, and it can be used for both the association and the classification. We mainly focus on investigating its classification ability in this paper. Through experimental simulations, it can be seen that this rule can successfully train the neuron to reproduce the desired spikes. In the classification task, the neuron is capable to classify different categories with the learning rule. We have proposed two decision-making schemes which are the absolute confidence and the relative confidence criteria. The classification performance is largely improved by the relative confidence criterion. The performance of this rule on classification of spatiotemporal spike patterns is also investigated and benchmarked by the tempotron rule.