# Encoding Data for HTM Systems

arXiv: https://arxiv.org/abs/1602.05925 · HTML: https://mlsanity.com/paper/1602.05925/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2016.

## Built on by (most cited first, 7 of 7)

- [A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations](https://mlsanity.com/paper/2111.06077/) (2021)
- [Unpaired Image Translation via Vector Symbolic Architectures](https://mlsanity.com/paper/2209.02686/) (2022)
- [Neuronal Auditory Machine Intelligence (NEURO-AMI) In Perspective](https://mlsanity.com/paper/2401.02421/) (2023)
- [Cloud Failure Prediction with Hierarchical Temporal Memory: An Empirical Assessment](https://mlsanity.com/paper/2110.03431/) (2021)
- [C3S Micro-architectural Enhancement: Spike Encoder Block and Relaxing Gamma Clock (Asynchronous)](https://mlsanity.com/paper/2306.15093/) (2023)
- [Implementing Online Reinforcement Learning with Clustering Neural Networks](https://mlsanity.com/paper/2402.18472/) (2024)
- [Enhancing Biologically Inspired Hierarchical Temporal Memory with Hardware-Accelerated Reflex Memory](https://mlsanity.com/paper/2504.03746/) (2025)

## Abstract (arXiv)

Hierarchical Temporal Memory (HTM) is a biologically inspired machine intelligence technology that mimics the architecture and processes of the neocortex. In this white paper we describe how to encode data as Sparse Distributed Representations (SDRs) for use in HTM systems. We explain several existing encoders, which are available through the open source project called NuPIC, and we discuss requirements for creating encoders for new types of data.