Think about how you choose a route to work, where to move house, or even which move to make in a game like Go. All of these scenarios require you to estimate the likely future reward of your decision. This is tricky because the number of possible scenarios explodes as one peers farther and farther into the future. Understanding how we do this is a major research question in neuroscience, while building systems that can effectively predict rewards is a major focus in AI research.

In our new paper, in Nature Neuroscience, we apply a neuroscience lens to a longstanding mathematical theory from machine learning to provide new insights into the nature of learning and memory. Specifically, we propose that the area of the brain known as the hippocampus offers a unique solution to this problem by compactly summarising future events using what we call a “predictive map.”

The hippocampus has traditionally been thought to only represent an animal’s current state, particularly in spatial tasks, such as navigating a maze. This view gained significant traction with the discovery of “place cells” in the rodent hippocampus, which fire selectively when the animal is in specific locations. While this theory accounts for many neurophysiological findings, it does not fully explain why the hippocampus is also involved in other functions, such as memory, relational reasoning, and decision making.