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

<div>Comparison of training time overhead in the New York taxi passenger flow. Each column in the graph represents the training time overhead of the dataset after a round of training. The dataset was trained for 10 rounds using different algorithms.</div>

Computational Intelligence and Neuroscience

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Figure 10

Figure 10: A Fast Spatial Pool Learning Algorithm of Hierarchical Temporal Memory Based on Minicolumn’s Self-Nomination 