Feedforward Learning of Mixture Models
Abstract
We develop a biologically-plausible learning rule that provably converges to the class means of general mixture models. This rule generalizes the classical BCM neural rule within a tensor framework, substantially increasing the generality of the learning problem it solves. It achieves this by incorporating triplets of samples from the mixtures, which provides a novel information processing interpretation to spike-timing-dependent plasticity. We provide both proofs of convergence, and a close fit to experimental data on STDP.
Cite
Text
Lawlor and Zucker. "Feedforward Learning of Mixture Models." Neural Information Processing Systems, 2014.Markdown
[Lawlor and Zucker. "Feedforward Learning of Mixture Models." Neural Information Processing Systems, 2014.](https://mlanthology.org/neurips/2014/lawlor2014neurips-feedforward/)BibTeX
@inproceedings{lawlor2014neurips-feedforward,
title = {{Feedforward Learning of Mixture Models}},
author = {Lawlor, Matthew and Zucker, Steven W},
booktitle = {Neural Information Processing Systems},
year = {2014},
pages = {2564-2572},
url = {https://mlanthology.org/neurips/2014/lawlor2014neurips-feedforward/}
}