Sequence Memories and their Integration for Planning: A Spiking Neural Network Model
نویسنده
چکیده
We propose a biologically-inspired auto/heteroassociative spiking neural network combined with a working memory model, in which a state-driven forward sequence and a goal-driven backward sequence on the associative network are integrated on the working memory to make a plan. By discrete pulse-driven neural network simulations, we show that several characteristics of planning process such as goal-directed attention control at a branch point of a plan, incremental planning, and planning by combining episodes can be realized on our system.
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Sequence Learning and Planning on Associative Spiking Neural Network
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