Hierarchical Temporal Memory Implementation with Explicit States Extraction

摘要

Hierarchical temporal memory is an online machine learning model that simulates some of the structural and algorithmic properties of neocortex. The new implementation of hierarchical temporal memory is proposed in the paper. The main distinction of the implementation is chain extraction module that complements the spatial and temporal polling modules of HTM. The new module simplifies cross-level regions connection implementation (e.g. feedback). An experiment is also described to illustrate how hierarchical temporal memory with explicit states extraction works.