Hierarchical temporal memory

Hierarchical temporal memory (HTM) is an unsupervised to semi-supervised online 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. HTM is a biomimetic model based on the memory-prediction theory of brain function described by Jeff Hawkins in his book On Intelligence. HTM is a method for discovering and inferring the high-level causes of observed input patterns and sequences, thus building an increasingly complex model of the world.

Jeff Hawkins states that HTM does not present any new idea or theory, but combines existing ideas to mimic the neocortex with a simple design that provides a large range of capabilities. HTM combines and extends approaches used in Sparse distributed memory, Bayesian networks, spatial and temporal clustering algorithms, while using a tree-shaped hierarchy of nodes that is common in neural networks.

A typical HTM network is a tree-shaped hierarchy of levels that are composed of smaller elements called nodes or columns. A single level in the hierarchy is also called a region. Higher hierarchy levels often have fewer nodes and therefore less spatial resolvability. Higher hierarchy levels can reuse patterns learned at the lower levels by combining them to memorize more complex patterns.

Each HTM node has the same basic functionality. In learning and inference modes, sensory data comes into the bottom level nodes. In generation mode, the bottom level nodes output the generated pattern of a given category. The top level usually has a single node that stores the most general categories (concepts) which determine, or are determined by, smaller concepts in the lower levels which are more restricted in time and space. When in inference mode, a node in each level interprets information coming in from its child nodes in the lower level as probabilities of the categories it has in memory.

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