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.

Neural networks are applied to the problem of learning, using such techniques as Hebbian learning[115] and the relatively new field of Hierarchical Temporal Memory which simulates the architecture of the neocortex.

'Hierarchical Temporal Memory' (HTM) claims to explain how our brains discover, infer, and predict patterns in the phenomenal world. JP: Is the higher consciousness -- what philosophers sometimes call 'self-consciousness' -- a byproduct of HTM?

See also: Artificial intelligence, Neural network, Machine learning, Knowledge, AI