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arXiv · 1410.8580

An Online Algorithm for Learning Selectivity to Mixture Means

Matthew Lawlor·Steven Zucker
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Abstract

We develop a biologically-plausible learning rule called Triplet BCM that provably converges to the class means of general mixture models. This rule generalizes the classical BCM neural rule, and provides a novel interpretation of classical BCM as performing a kind of tensor decomposition. It achieves a substantial generalization over classical BCM by incorporating triplets of samples from the mixtures, which provides a novel information processing interpretation to spike-timing-dependent plasticity. We provide complete proofs of convergence of this learning rule, and an extended discussion of the connection between BCM and tensor learning.

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Matthew Lawlor, Steven Zucker. 2014-10-30. An Online Algorithm for Learning Selectivity to Mixture Means. https://arxiv.org/abs/1410.8580

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