HTM theory

Yeldos Dauletkhanuly, Olga Krestinskaya, Alex James Pappachen

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This chapter presents the general background information about the Hierarchical Temporal Memory (HTM). HTM is a recently proposed cognitive learning algorithm that is intended to emulate the overall structural and functionality of the human neocortex responsible for the high-order functions such as cognition, learning and making predictions. The main properties of HTM is hierarchical structure, sparsity and modularity. HTM consists of two main parts: HTM Spatial Pooler (SP) and HTM Temporal Memory (TM). The HTM SP performs the encoding of the input data and produces sparse distributed representation (SDR) of the input pattern useful for visual data processing and classification tasks. The HTM TM detects the temporal changes in the input data and performs prediction making.

Original languageEnglish
Title of host publicationModeling and Optimization in Science and Technologies
PublisherSpringer Verlag
Pages169-180
Number of pages12
DOIs
Publication statusPublished - Jan 1 2020

Publication series

NameModeling and Optimization in Science and Technologies
Volume14
ISSN (Print)2196-7326
ISSN (Electronic)2196-7334

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ASJC Scopus subject areas

  • Modelling and Simulation
  • Medical Assisting and Transcription
  • Applied Mathematics

Cite this

Dauletkhanuly, Y., Krestinskaya, O., & James Pappachen, A. (2020). HTM theory. In Modeling and Optimization in Science and Technologies (pp. 169-180). (Modeling and Optimization in Science and Technologies; Vol. 14). Springer Verlag. https://doi.org/10.1007/978-3-030-14524-8_13