A robust implementation of the spatial pooler within the theory of Hierarchical Temporal Memory (HTM)

In this study learning reinforcement and noise rejection of a spatial pooler was examined, the first learning stage in a Hierarchical Temporal Memory (HTM) network. Hierarchical Temporal Memory (HTM) is a proposed model within the field of neuromorphic engineering. It describes a top down approach to understanding how the human brain performs higher reasoning and has application as a machine-learning algorithm. Final results displayed an increase in permanence values associated with the learning of the input pseudo-sensory signal and the system was able to accurately recognize the input signal with up to twenty percent of the binary data randomly modified. These results demonstrated conclusive evidence that HTM is a possible choice when machine intelligence is a system requirement.