  The hippocampus and the striatum support episodic and procedural memory,
respectively, and "place" and "response" learning within spatial navigation.
Recently this dichotomy has been linked to "model-based" and "model-free"
reinforcement learning. Here we present a well-constrained neural model of how
both systems support spatial navigation, and apply the same model to more
abstract problems such as sequential decision making. In particular, we show
that if a task can be transformed into a Markov Decision Process, the machinery
provided by the hippocampus and striatum can be utilized to solve it. These
results show how the hippocampal complex can represent non-spatial problems,
including context, probabilities and action-dependent information, in support
of "model-based" reinforcement learning to complement learning within the
striatum.
