Planning at decision time and in the background during spatial navigation

Planning is the model-based approach to solving control problems. The hallmark of planning is the endogenous generation of dynamical representations of future states, like goal locations, or state sequences, like trajectories to the goal location, using an internal model of the task. We review recent evidence of model-based planning processes and the representation of future goal states in the brain of rodents and humans engaged in spatial navigation tasks. We highlight two distinct but complementary usages of planning as identified in artificial intelligence: 'at decision time', to support goal-directed choices and sequential memory encoding, and 'in the background', to learn behavioral policies and to optimize internal models. We discuss how two kinds of internally generated sequences in the hippocampus - theta and SWR sequences - might participate in the neuronal implementation of these two planning modes, thus supporting a flexible model-based system for adaptive cognition and action.