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,[1] anomaly detection,[2] classification, and ultimately sensorimotor applications.[3]
HTM has been tested and implemented in software through example applications from Numenta and a few commercial applications from Numenta's partners[clarification needed].
^Cui, Yuwei; Ahmad, Subutai; Hawkins, Jeff (2016). "Continuous Online Sequence Learning with an Unsupervised Neural Network Model". Neural Computation. 28 (11): 2474–2504. arXiv:1512.05463. doi:10.1162/NECO_a_00893. PMID 27626963. S2CID 3937908.
Hierarchicaltemporalmemory (HTM) is a biologically constrained machine intelligence technology developed by Numenta. Originally described in the 2004...
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