mlsanityData to 14 Oct 2026

Properties of Sparse Distributed Representations and their Application to Hierarchical Temporal Memory

arXiv 1503.07469 ↗First posted 25 Mar 2015q-bio.NC cs.AIcited by 14

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Abstract (arXiv)

Empirical evidence demonstrates that every region of the neocortex represents information using sparse activity patterns. This paper examines Sparse Distributed Representations (SDRs), the primary information representation strategy in Hierarchical Temporal Memory (HTM) systems and the neocortex. We derive a number of properties that are core to scaling, robustness, and generalization. We use the theory to provide practical guidelines and illustrate the power of SDRs as the basis of HTM. Our goal is to help create a unified mathematical and practical framework for SDRs as it relates to cortical function.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Mar 2015.

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Citing papers per year: 2021: 1; 2022: 4; 2023: 3; 2024: 5; 2025: 0; 2026: 1.

14 papers.

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  1. 2022Unpaired Image Translation via Vector Symbolic Architecturescited by 13
  2. 2021Ultra-High Dimensional Sparse Representations with Binarization for Efficient Text Retrievalcited by 7
  3. 2023A Sparse Quantized Hopfield Network for Online-Continual Memorycited by 5
  4. 2022Can a Hebbian-like learning rule be avoiding the curse of dimensionality in sparse distributed data?cited by 1
  5. 2022SPARTAN: Sparse Hierarchical Memory for Parameter-Efficient Transformerscited by 1
  6. 2022A Hierarchical Approach to Conditional Random Fields for System Anomaly Detectioncited by 0
  7. 2023Ensoul: A framework for the creation of self organizing intelligent ultra low power systems (SOULS) through evolutionary enerstatic networkscited by 0
  8. 2023Spatially-resolved hyperlocal weather prediction and anomaly detection using IoT sensor networks and machine learning techniquescited by 0
  9. 2024Architecture of a Cortex Inspired Hierarchical Event Recallercited by 0
  10. 2024Exploring a Cognitive Architecture for Learning Arithmetic Equationscited by 0
  11. 2024The Cooperative Network Architecture: Learning Structured Networks as Representation of Sensory Patternscited by 0
  12. 2024Predictive Attractor Modelscited by 0
2 more
  1. 2024Homeostasis and Sparsity in Transformercited by 0
  2. 2026Creating Intelligence: A Computational Foundation for AGIcited by 0