Hierarchical temporal memory (HTM) is a biologically constrained machine intelligence technology developed by Numenta. Initially described in the 2004 e-book On Intelligence by Jeff Hawkins with Sandra Blakeslee, Memory Wave Experience HTM is primarily used today for anomaly detection in streaming data. The technology relies on neuroscience and the physiology and interaction of pyramidal neurons within the neocortex of the mammalian (specifically, human) brain. On the core of HTM are learning algorithms that may retailer, learn, infer, and Memory Wave recall excessive-order sequences. Not like most different machine learning methods, HTM consistently learns (in an unsupervised course of) time-primarily based patterns in unlabeled data. HTM is robust to noise, and has high capability (it could actually be taught multiple patterns simultaneously). A typical HTM network is a tree-formed hierarchy of ranges (not to be confused with the "layers" of the neocortex, as described below). These ranges are composed of smaller components called regions (or nodes). A single stage in the hierarchy probably incorporates several areas. Larger hierarchy levels often have fewer areas.

Increased hierarchy levels can reuse patterns learned on the lower ranges by combining them to memorize extra complicated patterns. Every HTM area has the same primary perform. In learning and inference modes, sensory knowledge (e.g. data from the eyes) comes into bottom-degree regions. In generation mode, the bottom stage regions output the generated sample of a given category. When set in inference mode, a area (in each degree) interprets information developing from its "child" areas as probabilities of the categories it has in Memory Wave Experience. Each HTM region learns by figuring out and memorizing spatial patterns-mixtures of input bits that often occur at the identical time. It then identifies temporal sequences of spatial patterns which can be prone to happen one after another. HTM is the algorithmic part to Jeff Hawkins’ Thousand Brains Theory of Intelligence. So new findings on the neocortex are progressively integrated into the HTM mannequin, which modifications over time in response. The new findings do not essentially invalidate the previous elements of the mannequin, so ideas from one era aren't essentially excluded in its successive one.

During training, a node (or area) receives a temporal sequence of spatial patterns as its enter. 1. The spatial pooling identifies (in the enter) steadily noticed patterns and memorise them as "coincidences". Patterns which can be considerably related to one another are handled as the identical coincidence. Numerous doable input patterns are reduced to a manageable number of known coincidences. 2. The temporal pooling partitions coincidences which are prone to follow each other within the coaching sequence into temporal groups. Every group of patterns represents a "cause" of the enter sample (or "identify" in On Intelligence). The ideas of spatial pooling and temporal pooling are still fairly important in the present HTM algorithms. Temporal pooling is not yet nicely understood, and its which means has modified over time (as the HTM algorithms advanced). During inference, the node calculates the set of probabilities that a pattern belongs to each known coincidence. Then it calculates the probabilities that the input represents every temporal group.

The set of probabilities assigned to the groups is named a node's "perception" concerning the enter sample. This perception is the result of the inference that's handed to one or more "dad or mum" nodes in the following greater level of the hierarchy. If sequences of patterns are much like the coaching sequences, then the assigned probabilities to the groups won't change as often as patterns are received. In a extra basic scheme, the node's perception can be sent to the input of any node(s) at any stage(s), but the connections between the nodes are still fixed. The upper-level node combines this output with the output from other little one nodes thus forming its own input pattern. Since decision in house and time is misplaced in every node as described above, beliefs formed by increased-level nodes symbolize an excellent bigger range of area and time. This is supposed to replicate the organisation of the bodily world as it's perceived by the human brain.
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