Sequence learning and planning on associative spiking neural network

We have been building an auto/heteroassociative spiking neural network combined with a working memory model. In this model, a state-driven forward sequence and a goal-driven backward sequence on the associative network are respectively represented by a sequence of synchronous firing in a particular gamma subcycle during a theta oscillation. These forward and backward sequence firings are transmitted to the working memory, temporarily maintained, and integrated based on a competition principle to make a plan. The paper shows that our system can learn forward and backward sequences simultaneously and a plan is incrementally synthesized by repeating their recall and integration