Hierarchical temporal memory

Hierarchical temporal memory (HTM) is a biologically constrained machine intelligence technology developed by Numenta. Originally described in the 2004 book On Intelligence by Jeff Hawkins with Sandra Blakeslee, HTM is primarily used today for anomaly detection in streaming data. The technology is based on neuroscience and the physiology and interaction of pyramidal neurons in the neocortex of the mammalian (in particular, human) brain. At the core of HTM are learning algorithms that can store, learn, infer, and recall high-order sequences. Unlike most other machine learning methods, HTM constantly learns (in an unsupervised process) time-based patterns in unlabeled data. HTM is robust to noise, and has high capacity (it can learn multiple patterns simultaneously). When applied to computers, HTM is well suited for prediction, anomaly detection, classification, and ultimately sensorimotor applications. HTM has been tested and implemented in software through example applications from Numenta and a few commercial applications from Numenta's partners. A typical HTM network is a tree-shaped hierarchy of levels (not to be confused with the "layers" of the neocortex, as described below). These levels are composed of smaller elements called regions (or nodes). A single level in the hierarchy possibly contains several regions. Higher hierarchy levels often have fewer regions. Higher hierarchy levels can reuse patterns learned at the lower levels by combining them to memorize more complex patterns. Each HTM region has the same basic function. In learning and inference modes, sensory data (e.g. data from the eyes) comes into bottom-level regions. In generation mode, the bottom level regions output the generated pattern of a given category. The top level usually has a single region that stores the most general and most permanent categories (concepts); these determine, or are determined by, smaller concepts at lower levels—concepts that are more restricted in time and space.