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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Episodic Memory in Minicolumn Associative Knowledge Graphs
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Episodic Memory in Minicolumn Associative Knowledge Graphs

机译：迷你列关联知识图中的情节记忆

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摘要

A generalization of active neural associative knowledge graphs (ANAKGs) to their minicolumn form is presented in this paper. Each minicolumn represents a single symbol, and the activation of an individual neuron in a minicolumn depends on the context of the activation of the presynaptic neuron. The implemented memory model combines the ANAKG associative spiking neuron idea with the idea of the hierarchical temporal memory. This new associative memory organization preserves all properties of ANAKG memories, such as storage of knowledge based on the association of spatiotemporal input sequences, self-organization, quick learning, and recall of the sequential memories, while increasing the recall quality and the memory capacity. The recall quality advantage of the new approach over ANAKG increases with the length of the recalled episodes and the number of neurons used in each minicolumn. We introduced a new distance measure to compare the recalled sequences and defined a recall quality to determine the memory capacity. Performed tests confirmed our claims. Additional tests were performed to illustrate the computational complexity and the efficiency of the developed approach.
机译：本文介绍了主动神经联想知识图（ANAKG）到其迷你列形式的推广。每个微型柱代表一个符号，微型柱中单个神经元的激活取决于突触前神经元的激活情况。实施的内存模型将ANAKG关联的尖峰神经元思想与分层时间内存的思想结合在一起。这种新的关联性存储器组织保留了ANAKG存储器的所有属性，例如基于时空输入序列的关联，自组织，快速学习和顺序存储器的调用来存储知识，同时提高了调用质量和存储容量。与ANAKG相比，新方法的召回质量优势随召回事件的持续时间以及每个微型列中使用的神经元数量的增加而增加。我们引入了一种新的距离量度来比较召回的序列，并定义了召回质量来确定存储容量。进行的测试证实了我们的主张。进行了额外的测试，以说明所开发方法的计算复杂性和效率。

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