Hierarchical temporal memory

Hierarchical Temporal Memory (HTM) is a machine learning model developed by Jeff Hawkins and Dileep George of Numenta, Inc. that models some of the structural and algorithmic properties of the neocortex as Bayesian networks.

Whilst criticized by the AI community as rehashing existing material (for example, in the December 2005 issue of the Artificial Intelligence journal), the model is quite novel in proposing functions for cortical layers. As such it is related to similar work by Thomas Poggio and David Mumford amongst others.

See also

• On Intelligence
• Memory-prediction framework
• List of artificial intelligence projects

References

• Template:PDFlink by Jeff Hawkins and Dileep George, Numenta Inc., 2006-05-17
• On Intelligence; Jeff Hawking, Sandra Blakeslee; Henry Holt, 2004; ISBN 0312712340

External links

Official

• Numenta, Inc.
• OnIntelligence.org Forum, an Internet forum for the discussion of relevant topics, especially relevant being the Models and Simulation Topics forum.
• Hierarchical Temporal Memory (Microsoft PowerPoint presentation)
• Hierarchical Temporal Memory: Theory and Implementation (Google Video)

Other

• The Gartner Fellows: Jeff Hawkins Interview by Tom Austin, Gartner, March 2, 2006
• Emerging Tech: Jeff Hawkins reinvents artificial intelligence by Debra D'Agostino and Edward H. Baker, CIO Insight, May 1 2006
• "Putting your brain on a microchip" by Stefanie Olsen, CNET News.com, May 12 2006
• "The Thinking Machine" by Evan Ratliff, Wired, March 2007
• Think like a human by Jeff Hawkins , IEEE Spectrum, April 2007
• Neocortex - Memory-Prediction Framework — Open Source Implementation with GNU General Public License