Numenta brings brain theory to machine learning - Scienmag

REDWOOD CITY, CA – November 14, 2016– Numerous proposals have been offered for how intelligent machines might learn sequences of patterns, which is believed to be an essential component of any intelligent system. Researchers at Numenta Inc. have published a new study, "Continuous Online Sequence Learning with an Unsupervised Neural Network Model," which compares their biologically-derived HTM sequence memory to traditional machine learning algorithms. The paper has been published in MIT Press Journal's Neural Computation 28, 2474-2504 (2016). You can read and download the paper here. Authored by Numenta researchers Yuwei Cui, Subutai Ahmad, and Jeff Hawkins, the new paper serves as a companion piece to Numenta's breakthrough research offered in "Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex," which appeared in Frontiers in Neural Circuits, in March 2016.