Abstract
Hierarchical Temporal Memory is a new type of artificial neural network model, which imitates the structure and information processing flow of the human brain. Hierarchical Temporal Memory has strong adaptability and fast learning ability and becomes a hot spot in current research. Hierarchical Temporal Memory obtains and saves the temporal characteristics of input sequences by the temporal pool learning algorithm. However, the current algorithm has some problems such as low learning efficiency and poor learning effect when learning time series data. In this paper, a temporal pool learning algorithm based on location awareness is proposed. The cell selection rules based on location awareness and the dendritic updating rules based on adjacent inputs are designed to improve the learning efficiency and effect of the algorithm. Through the algorithm prototype, three different datasets are used to test and analyze the algorithm performance. The experimental results verify that the algorithm can quickly obtain the complete characteristics of the input sequence. No matter whether there are similar segments in the sequence, the proposed algorithm has higher prediction recall and precision than the existing algorithms.
Highlights
Hierarchical Temporal Memory (HTM) is a new neural network model that simulates the organization and structure of cortical cells and the way the human brain processes information
A temporal pool learning algorithm based on location awareness (TPL_LA) is proposed. e generation rules of learning cells and active cells based on location awareness and the adjustment rules of dendrite branches associated with adjacent inputs are designed. ree datasets are used to test the performance of TPL_LA and TPL. e main innovations of this paper are as follows
Ese three datasets are used to train the two algorithms, respectively, and the corresponding datasets are used for prediction tests. e test results are used to evaluate the learning effect of whether the two algorithms can obtain all features of the sequence. ree evaluation indicators, i.e., mean recall (MR), mean precision (MP), and F1, are used to evaluate the two algorithms. e recallt describes whether the input is within the prediction range at time t. e precisiont describes the proportion of the input in the prediction range at time t. e evaluation indicators are calculated using the following equations: (1) Get Wt of current input from spatial pool: (2) Get A t−1 ⊓ t−1 Cellt− 1 from last time
Summary
Hierarchical Temporal Memory (HTM) is a new neural network model that simulates the organization and structure of cortical cells and the way the human brain processes information. E generation rules of learning cells and active cells based on location awareness and the adjustment rules of dendrite branches associated with adjacent inputs are designed. (1) Using the multiple cells contained in the minicolumn, the generated learning cells and active cells can accurately express the position information of the input In this way, the algorithm can give priority to learning the current sequence features, reduce the possibility of self-cycle prediction when learning continuous same inputs, and improve the learning effect of TPL. (2) According to the generated learning cell set and active cell set, the algorithm adjusts the updating rules of dendritic branches associated with adjacent inputs, and a new setting strategy of synaptic value is designed to improve the learning efficiency of TPL.
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