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Hierarchical Temporal Memory

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Julia implementation of core Numenta HTM algorithms. Read the docs.


Hierarchical Temporal Memory is an abstract algorithmic model of the human brain (specifically the neocortex). It's a tool for

  • neuroscience: understanding the human brain
  • machine learning: predicting time series and detecting anomalies

The main algorithms of this model, the Spatial Pooler and Temporal (Sequence) Memory, are described in:

This package implements Numenta's Hierarchical Temporal Memory in simple and concise language, relying on linear algebra and staying close to the mathematical description in the source material.

Experiments

Experiments and evaluation of this package lives in the HTMexperiments repo.

Roadmap