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In this paper we present a biologically-inspired model of spatio-temporal learning in the hippocampus and prefrontal cortex which can be used in tasks requiring the behavior of the robot to be constrained by sensory and temporal information. In this model chains of sensory events are learned and associated with motor actions. The temporality of these sequences is also learned and can be used to predict the timing of upcoming events. The neural network acts as a novelty detector and can modulatedoi:10.1109/robio.2011.6181522 dblp:conf/robio/HirelGQ11 fatcat:lyqbt2iayzcxbow2yjvzcfxhue