Archive note. I assembled this bibliography in 2011 while preparing a report on modelling children's second-language learning with HTM. It represents the literature I could locate then, not a current survey. The old blog mirrored some files for continuity; this restoration lists the works but does not republish third-party PDFs.

The later note on accessing legacy Numenta content explains the gaps in the surviving source material.

At the time there were relatively few publications directly concerned with Hierarchical Temporal Memory. The list below became a working map of the field: the ideas that preceded NuPIC, Numenta’s own technical material, adjacent memory models and the first application studies.

Foundations

  • Jeff Hawkins and Sandra Blakeslee, On Intelligence (2004).
  • Dileep George and Jeff Hawkins, “A Hierarchical Bayesian Model of Invariant Pattern Recognition in the Visual Cortex” (2004/2005).
  • Dileep George and Jeff Hawkins, “Invariant Pattern Recognition Using Bayesian Inference on Hierarchical Sequences” (2004).
  • Dileep George, How the Brain Might Work: A Hierarchical and Temporal Model for Learning and Recognition (2008).

Numenta and NuPIC publications

  • Jeff Hawkins and Dileep George, Hierarchical Temporal Memory: Concepts, Theory and Terminology (2006).
  • Numenta, Node Algorithms Guide (2007).
  • Numenta, HTM Comparison with Existing Models (2007).
  • Numenta, Problems That Fit HTMs (2007).
  • Numenta, Getting Started with NuPIC (2008).
  • Numenta, Node Plugin Developer’s Guide (2008).
  • Numenta, Advanced NuPIC Programming (2008).
  • Numenta, Vision Toolkit Tutorial (2009).
  • Numenta, Hierarchical Temporal Memory Including HTM Cortical Learning Algorithms (2010).
  • S. Garalevicius, “Analysis and Implementation of the Memory-Prediction Framework” (2005).
  • L. Lee Majure, “Unsupervised Phoneme Acquisition Using Hierarchical Temporal Models” (2006).
  • S. Garalevicius, “Memory-Prediction Framework for Pattern Recognition” (2007).
  • N. Farahmand, M. H. Dezfoulian, H. GhiasiRad, A. Mokhtari and A. Nouri, “Online Temporal Pattern Learning” (2009).
  • J. A. Starzyk, “Spatio-Temporal Memories for Machine Learning: A Long-Term Memory Organization” (2009).
  • A. Nouri and H. Nikmehr, “Hierarchical Bayesian Reservoir Memory” (2009).

Early HTM applications

For a broader view of the language-recognition work represented below, see the contemporary language-identification reading list.

  • J. van Doremalen, “Hierarchical Temporal Memory Networks for Spoken Digit Recognition” (2007).
  • B. Bobier, “Handwritten Digit Recognition Using Hierarchical Temporal Memory” (2007).
  • J. M. Casarella, “The Application of Hierarchical Temporal Memory to the Evaluation of EEG Signals” (2007).
  • N. C. Schey, “Song Identification Using the Numenta Platform for Intelligent Computing” (2008).
  • D. Robinson, K. Leung and X. Falco, “Spoken Language Identification with Hierarchical Temporal Memories” (2009).
  • J. Hartung, J. McCormack and F. Jacobus, “Support for the Use of Hierarchical Temporal Memory Systems in Automated Design Evaluation: A First Experiment” (2009).
  • S. Štolc and I. Bajla, “On the Optimum Architecture of the Biologically Inspired HTM Model Applied to Hand-Written Digit Recognition” (2010).

The value of this old list is historical as much as technical. It captures a period when biologically inspired sequence models were still a small, unusually cross-disciplinary research programme—before the later deep-learning wave reorganised the vocabulary and priorities of the field.

Published at Internet Archive ↗