When and How to Plan: Behavioral Quantification and Hippocampal Encoding of Multi-Step Planning

Abstract

What does it mean to plan? Reinforcement learning theory provides us with a formal definition that identifies the foundational building blocks of planning: the use of an internal action-outcome model to evaluate and guide behavior. The ability to plan comes effortlessly to both animals and humans alike, and it is necessary for successfully navigating the world around us. However, building algorithms that mimic the flexibility of biological planning is exceedingly difficult, and how the brain supports this function is still poorly understood. Identifying the neural mechanisms that underlie efficient planning will provide important insights not only towards computational models of planning, but also for understanding a fundamental function of the brain and why it might fail in the presence of neural disorders. Understanding how the brain plans is the primary goal of my thesis, where I break the problem down into three parts: the “what”, “when”, and “how”. The “what” establishes the problem at hand: what do we know about planning in the brain, and what regions have been implicated? Next, the “when” addresses the way in which we can measure planning and when an animal is most likely to be engaged in planning behavior. Lastly, the “how” begins to answer what neural mechanisms, specifically in dorsal hippocampus, underlie planning behavior. Taken together, the results of this thesis provide new insights into the “when” and “how” the brain plans. We find that we can detect discernible shifts in planning strategy that significantly predict changes in behavioral vigor and neural encoding. We additionally find that the hippocampus represents and utilizes sophisticated state information to bias choice encoding towards upcoming actions dependent on both reward and state transitions.