A Robotic Model of Hippocampal Reverse Replay for Reinforcement Learning

Understand

Hippocampal reverse replay is thought to contribute to learning, and particularly reinforcement learning, in animals.

• We present a computational model of learning in the hippocampus that builds on a previous model of the hippocampal-striatal network viewed as implementing a three-factor reinforcement learning rule.
• To augment this model with hippocampal reverse replay, a novel policy gradient learning rule is derived that associates place cell activity with responses in cells representing actions.
• This new model is evaluated using a simulated robot spatial navigation task inspired by the Morris water maze.