  It is now widely accepted that one of the roles of the hippocampus is to
maintain episodic spatial representations, while parallel striatal pathways
contribute to both declarative and procedural value computations by encoding
different input-specific outcome predictions. In this paper we investigate the
use of these brain mechanisms for action selection, linking them to model-based
and model-free controllers for decision making. To this aim we propose a
biologically inspired computational model that embodies these theories and
explains the functioning of the hippocampal-striatal circuit in a rat
navigation task. Its main characteristic is to allow the cooperation of
habitual and goal-directed behaviors, with the hippocampus primarily involved
in encoding spatial information and simulating possible navigation paths, and
the ventral and dorsal striatum involved in learning stimulus-response
behaviors and evaluating the reward expectancies associated to predicted
locations and sensed stimuli, respectively. The architecture we present employs
an unsupervised reinforcement learning rule for the hippocampal-striatal
network that is able to build a representation of the environment in which
rewarding sites and informative landmarks produce value gradients that are used
for planning and decision making. Additionally, it utilizes an arbitration
mechanism that balances between exploitation, i.e. stimulus-response behaviors,
and mental exploration, i.e. motor imagery processes, based on the intensity
and the variability of the responses of striatal neurons. We interpret these
results in light of recent experimental data that show anticipatory activations
in hippocampal and striatal areas.
