HTM Theory

  • Yeldos Dauletkhanuly
  • Olga Krestinskaya
  • Alex Pappachen JamesEmail author
Part of the Modeling and Optimization in Science and Technologies book series (MOST, volume 14)


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.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Yeldos Dauletkhanuly
    • 1
  • Olga Krestinskaya
    • 1
  • Alex Pappachen James
    • 1
    Email author
  1. 1.Nazarbayev UniversityAstanaKazakhstan

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