Hippocampal and striatal involvement in cognitive tasks: a computational model

TL;DR

This paper presents a neural model demonstrating how hippocampal and striatal systems support spatial and abstract decision-making by implementing model-based and model-free reinforcement learning, respectively.

Contribution

It introduces a unified neural model showing hippocampus and striatum support both spatial navigation and abstract decision tasks as Markov Decision Processes.

Findings

Hippocampus can represent non-spatial information for decision making

Striatum supports procedural learning in navigation tasks

Model demonstrates the neural basis of model-based reinforcement learning

Abstract

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.