Episodic Memory in Minicolumn Associative Knowledge Graphs

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.