Computational Intelligence and Neuroscience / 2021 / Article / Fig 13

Research Article

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

Figure 13

Comparison of the utilization degree of the minicolumn in the New York taxi passenger flow. (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.
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