VERSION 0.2, DECEMBER 10, 2010

HIERARCHICAL TEMPORAL MEMORY

 

Including

 

HTM Cortical Learning Algorithms

 

 

VERSION 0.2, DECEMBER 10, 2010

 

©Numenta, Inc. 2010

 

 

Use of Numenta’s software and intellectual property, including the ideas contained in this document, are free for non-commercial research purposes. For details, see http://www.numenta.com/about-numenta/licensing.php.

Read This First!

This is a draft version of this document. There are several things missing that you should be aware of.

 

What IS in this document:

This document describes in detail new algorithms for learning and prediction developed by Numenta in 2010. The new algorithms are described in sufficient

detail that a programmer can understand and implement them if desired. It starts

with an introductory chapter. If you have been following Numenta and have read some of our past white papers, the material in the introductory chapter will be

familiar. The other material is new.

 

What is NOT in this document:

There are several topics related to the implementation of these new algorithms that did not make it into this early draft.

 

- Although most aspects of the algorithms have been implemented and tested in software, none of the test results are currently included.

 

- There is no description of how the algorithms can be applied to practical problems. Missing is a description of how you would convert data from a sensor or database into a distributed representation suitable for the algorithms.

 

- The algorithms are capable of on-line learning. A few details needed to fully implement on-line learning in some rarer cases are not described.

 

- Other planned additions include a discussion of the properties of sparse distributed representations, a description of applications and examples, and citations for the appendixes.

 

We are making this document available in its current form because we think the algorithms will be of interest to others. The missing components of the document should not impede understanding and experimenting with the algorithms by motivated researchers. We will revise this document regularly to reflect our progress.