Applying Reinforcement Learning to Rodent Stress Research

Abstract

Rodent models are an invaluable tool for studying the pathophysiological mechanisms underlying stress and depressive disorders. However, the widely used behavioral assays to measure depressive-like states in rodents have serious limitations. In this commentary, we suggest that learning tasks, particularly those that can be analyzed with the framework of reinforcement learning, are ideal for assaying reward processing deficits relevant to depression. The key advantages of these tasks are their repeatable, quantifiable nature and the link to clinical studies. By optimizing the behavioral readout of stress-induced phenotypes in rodents, a reinforcement learning-based approach may help bridge the translational gap and advance antidepressant discovery.