Encoding Data for HTM Systems

2016/02/18 by Scott Purdy, Purdy, Scott
Computer Science · Engineering · #Advanced Memory and Neural Computing #FOS: Biological sciences #FOS: Computer and information sciences #Ferroelectric and Negative Capacitance Devices #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE) #Neurons and Cognition (q-bio.NC)

paper · pdf · doi:10.48550/arxiv.1602.05925

openalex publication_date 2016/02/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex

Related

• Properties of Sparse Distributed Representations and their Application to Hierarchical Temporal Memory
• Non-volatile Hierarchical Temporal Memory: Hardware for Spatial Pooling
• Porting HTM Models to the Heidelberg Neuromorphic Computing Platform
• Feature extraction without learning in an analog Spatial Pooler memristive-CMOS circuit design of Hierarchical Temporal Memory
• A Mathematical Formalization of Hierarchical Temporal Memory's Spatial Pooler