A Fast Spatial Pool Learning Algorithm of Hierarchical Temporal Memory Based on Minicolumn's Self-Nomination.

As a new type of artificial neural network model, HTM has become the focus of current research and application. The sparse distributed representation is the basis of the HTM model, but the existing spatial pool learning algorithms have high training time overhead and may cause the spatial pool to become unstable. To overcome these disadvantages, we propose a fast spatial pool learning algorithm of HTM based on minicolumn's nomination, where the minicolumns are selected according to the load-carrying capacity and the synapses are adjusted using compressed encoding. We have implemented the prototype of the algorithm and carried out experiments on three datasets. It is verified that the training time overhead of the proposed algorithm is almost unaffected by the encoding length, and the spatial pool becomes stable after fewer iterations of training. Moreover, the training of the new input does not affect the already trained results.