Computational Intelligence and Neuroscience / 2021 / Article / Fig 8

Research Article

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

Figure 8

Comparison of sparsity in larger input space. (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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