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First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Summary: The paper extends the classical BCM learning rule to utilize information from spike triplets. It is shown that the update rule can learn selectivity for mixture distributions (by converging to the class means). Quality: By employing tensor notation, the paper shows that the BCM rule can be generalized to use information from more than a pair of spikes (spike triplets are used for the examples). While the model has fewer parameters than previous learning algorithms based on spike triplets or quadruplets and the method can be shown to have stable points as class means of mixture distributions, the lack of experimental comparisons with other models makes it hard to gauge the incremental contribution of the model.