Hierarchical Temporal Memory
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Hierarchical temporal memory 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 using an approach somewhat similar to Bayesian networks. HTM model is based on the memory-prediction theory of brain function described by Jeff Hawkins in his book On Intelligence. HTMs are claimed to be biomimetic models of cause inference in intelligence. Jeff Hawkins states that HTM does not present any new idea or theory, but combines existing ideas to mimic the neocortex with the simplest design…mehr

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Produktbeschreibung
Hierarchical temporal memory 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 using an approach somewhat similar to Bayesian networks. HTM model is based on the memory-prediction theory of brain function described by Jeff Hawkins in his book On Intelligence. HTMs are claimed to be biomimetic models of cause inference in intelligence. Jeff Hawkins states that HTM does not present any new idea or theory, but combines existing ideas to mimic the neocortex with the simplest design that provides the greatest range of capabilities. He stated this is similar to the Palm Pilot, a device he designed that became popular because of its particular blend of old features. Similarities to existing AI ideas are described in the December 2005 issue of the Artificial Intelligence journal. It is similar to work by Tomaso Poggio and David Mumford.