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.
Structure and algorithms 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. When set in inference mode, a region (in each level) interprets information coming up from its "child" regions as probabilities of the categories it has in memory. Each HTM region learns by identifying and memorizing spatial patterns—combinations of input bits that often occur at the same time. It then identifies temporal sequences of spatial patterns that are likely to occur one after another.
As an evolving model HTM is the algorithmic component to Jeff Hawkins' Thousand Brains Theory of Intelligence. So new findings on the neocortex are progressively incorporated into the HTM model, which changes over time in response. The new findings do not necessarily invalidate the previous parts of the model, so ideas from one generation are not necessarily excluded in its successive one. Because of the evolving nature of the theory, there have been several generations of HTM algorithms, which are briefly described below.
First generation: zeta 1 The first generation of HTM algorithms is sometimes referred to as zeta 1.
Training During training, a node (or region) receives a temporal sequence of spatial patterns as its input. The learning process consists of two stages:
The spatial pooling identifies (in the input) frequently observed patterns and memorise them as "coincidences". Patterns that are significantly similar to each other are treated as the same coincidence. A large number of possible input patterns are reduced to a manageable number of known coincidences. The temporal pooling partitions coincidences that are likely to follow each other in the training sequence into temporal groups. Each group of patterns represents a "cause" of the input pattern (or "name" in On Intelligence). The concepts of spatial pooling and temporal pooling are still quite important in the current HTM algorithms. Temporal pooling is not yet well understood, and its meaning has changed over time (as the HTM algorithms evolved).
Inference 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 each temporal group. The set of probabilities assigned to the groups is called a node's "belief" about the input pattern. (In a simplified implementation, node's belief consists of only one winning group). This belief is the result of the inference that is passed to one or more "parent" nodes in the next higher level of the hierarchy. "Unexpected" patterns to the node do not have a dominant probability of belonging to any one temporal group but have nearly equal probabilities of belonging to several of the groups. If sequences of patterns are similar to the training sequences, then the assigned probabilities to the groups will not change as often as patterns are received. The output of the node will not change as much, and a resolution in time is lost. In a more general scheme, the node's belief can be sent to the input of any node(s) at any level(s), but the connections between the nodes are still fixed. The higher-level node combines this output with the output from other child nodes thus forming its own input pattern. Since resolution in space and time is lost in each node as described above, beliefs formed by higher-level nodes represent an even larger range of space and time. This is meant to reflect the organisation of the physical world as it is perceived by the human brain. Larger concepts (e.g. causes, actions, and objects) are perceived to change more slowly and consist of smaller concepts that change more quickly. Jeff Hawkins postulates that brains evolved this type of hierarchy to match, predict, and affect the organisation of the external world. More details about the functioning of Zeta 1 HTM can be found in Numenta's old documentation.
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