The hippocampus is a brain structure known for storing a map of episodic nature for each environment. In particular, neurons in the hippocampus selectively fire in their preferred locations (place fields) when an animal runs through the environment. However, where hippocampal place cells have their fields is famously hard to predict: if you know how a given subject encodes location of environment A, that doesn’t tell you much about how it encodes B.
We adapted a technique from human neuroimaging work (hyperalignment, inspired by Haxby Lab) enabling us to use how subject 1 encodes A and B (e.g. left and right arms of a maze), and how subject 2 encodes A, to predict how subject 2 encodes B.
Surprisingly, this between-subject prediction worked better than the within-subject controls we tried, and simulations suggest simple explanations such as correlated firing rates between A and B can be ruled out. Thus, we think between-subject prediction is a novel analysis approach that suggests an underlying regularity in how different places are mapped in the rodent hippocampus.