Hierarchical Temporal Memory is a brain inspired memory prediction framework modeled after the uniform structure and connectivity of pyramidal neurons found in the human neocortex. Similar to the neocortex, Hierarchical Temporal Memory processes spatiotemporal information for anomaly detection and prediction. A critical component in the Hierarchical Temporal Memory algorithm is the Spatial Pooler, which is responsible for processing feedforward data into sparse distributed representations.
This study addresses three fundamental research questions for Hierarchical Temporal Memory algorithms. What are the metrics for understanding the semantic content of sparse distributed representations? The semantic content and relationships between representations was visualized with uniqueness matrices and dimensionality reduction techniques. How can spatial semantic information in images be encoded into binary representations for the Hierarchical Temporal Memory's Spatial Pooler? A Contractive Autoencoder was exploited to create binary representations containing spatial information from image data. The uniqueness matrix shows that the Contractive Autoencoder encodes spatial information with strong spatial semantic relationships. The final question is how can vector operations of sparse distributed representations be enhanced to produce separable representations? A binding operation that results in a novel vector was implemented as a circular bit shift between two binary vectors. Binding of labeled sparse distributed representations was shown to create separable representations, but more robust representations are limited by vector density.
Computer Engineering (MS)
Department, Program, or Center
Computer Engineering (KGCOE)
Boudreau, Luke G., "Contractive Autoencoding for Hierarchical Temporal Memory and Sparse Distributed Representation Binding" (2018). Thesis. Rochester Institute of Technology. Accessed from
RIT – Main Campus
Available for download on Wednesday, May 08, 2019