Encoding Reality: Prediction-Assisted Cortical Learning Algorithm in Hierarchical Temporal Memory
Neural and Evolutionary Computing
2015-10-09 v2 Artificial Intelligence
Abstract
In the decade since Jeff Hawkins proposed Hierarchical Temporal Memory (HTM) as a model of neocortical computation, the theory and the algorithms have evolved dramatically. This paper presents a detailed description of HTM's Cortical Learning Algorithm (CLA), including for the first time a rigorous mathematical formulation of all aspects of the computations. Prediction Assisted CLA (paCLA), a refinement of the CLA is presented, which is both closer to the neuroscience and adds significantly to the computational power. Finally, we summarise the key functions of neocortex which are expressed in paCLA implementations.
Cite
@article{arxiv.1509.08255,
title = {Encoding Reality: Prediction-Assisted Cortical Learning Algorithm in Hierarchical Temporal Memory},
author = {Fergal Byrne},
journal= {arXiv preprint arXiv:1509.08255},
year = {2015}
}
Comments
Updated reference to unofficial revision of Hawkins and Ahmad, 2011