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arXiv:1602.05925cs.NE

Encoding Data for HTM Systems

Scott Purdy

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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.

arXiv comments
Journal reference
DOI
Subjects
cs.NENeural and Evolutionary Computing q-bio.NCNeurons and Cognition

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  1. Study urges caution when comparing neural networks to the brain 1 comment

    • We've been doing significant research using HTM[1][2][3][4] w/ SDR[5] similar to the proprietary implementation of Semantic Folding[6] and were able to classify literally gigabytes of documents per second on FPGAs. HTM aims to resemble the architecture of the Neocortex. I simply…

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