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

<div>Comparison of the utilization degree of the minicolumn in larger input space. (a) After each round of TPL training, we calculate the mean and standard deviation of the activation frequency of each minicolumn to express the utilization degree of the minicolumn. (b) After each round of TPL_SN training, we also count such indicators. The dataset was trained for 10 rounds using different algorithms.</div>

Computational Intelligence and Neuroscience

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Figure 9

Figure 9: A Fast Spatial Pool Learning Algorithm of Hierarchical Temporal Memory Based on Minicolumn’s Self-Nomination 