Why and How Hippocampal Transition Cells Can Be Used in Reinforcement Learning

In this paper we present a model of reinforcement learning (RL) which can be used to solve goal-oriented navigation tasks. Our model supposes that transitions between places are learned in the hippocampus (CA pyramidal cells) and associated with information coming from path-integration. The RL neural network acts as a bias on these transitions to perform action selection. RL originates in the basal ganglia and matches observations of reward-based activity in dopaminergic neurons. Experiments were conducted in a simulated environment. We show that our model using transitions and inspired by Q-learning performs more efficiently than traditional actor-critic models of the basal ganglia based on temporal difference (TD) learning and using static states.