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

arXiv: https://arxiv.org/abs/1503.07469 · HTML: https://mlsanity.com/paper/1503.07469/

## Key facts

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

## Built on by (most cited first, 14 of 14)

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- [Architecture of a Cortex Inspired Hierarchical Event Recaller](https://mlsanity.com/paper/2405.02371/) (2024)
- [Exploring a Cognitive Architecture for Learning Arithmetic Equations](https://mlsanity.com/paper/2405.04550/) (2024)
- [The Cooperative Network Architecture: Learning Structured Networks as Representation of Sensory Patterns](https://mlsanity.com/paper/2407.05650/) (2024)
- [Predictive Attractor Models](https://mlsanity.com/paper/2410.02430/) (2024)
- [Homeostasis and Sparsity in Transformer](https://mlsanity.com/paper/2412.00503/) (2024)
- [Creating Intelligence: A Computational Foundation for AGI](https://mlsanity.com/paper/2606.31819/) (2026)

## 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.