Encoding Data for HTM Systems

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.

References in corpus (1)

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

Cited by in corpus (5)

• Continuous online sequence learning with an unsupervised neural network model
• A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations
• End-to-End Memristive HTM System for Pattern Recognition and Sequence Prediction
• Using Spatial Pooler of Hierarchical Temporal Memory to classify noisy videos with predefined complexity
• Heartbeat Classification in Wearables Using Multi-layer Perceptron and Time-Frequency Joint Distribution of ECG