Computational Intelligence and Neuroscience / 2021 / Article / Fig 12

Research Article

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

Figure 12

Comparison of sparsity in the New York taxi passenger flow. (a) After each round of TPL training, we calculate the mean and standard deviation of the number of activated minicolumns in each input to express the sparsity of the spatial pool. (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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