Hierarchical Temporal Memory With Time Axis Introduced Connection Segments

Abstract

In this paper, we propose an improved Hierarchical Temporal Memory (HTM) that can consider long-term dependence. Conventional HTM is a temporal sequence prediction model imitating the cerebral cortex structure and learning algorithm. In the conventional model, only the connection with the previous data is learned, but in the proposed HTM-TA the connection with several former data can be learned. Therefore, we add the time axis to the segment which is the collection of synapses in the structure of HTM and we name this HTM-TA. As a result of evaluation experiments, it was confirmed that the proposed model obtained higher accuracy than the conventional model on temporal sequence prediction.