That’s it, thanks for reading along. There’s a lot more details in the paper about the framework and its relationship to the multitude of experimental and computational studies of hippocampal remapping. Please let us know what you think!

Overall, all three remapping mechanisms are likely to be accurate depictions of hippocampal activity changes under different conditions. More generally, this perspective is not limited to the hippocampus and can be used to understand neural variability in other brain areas as well!

In the paper, we simulate several examples of each remapping type in RNN models and quantify the types of neural activity patterns underlying them.

Null-space (NS) remapping features changes outside of the latent space, i.e., in the null space, and relates to changes in which neurons participate in a redundant code. This relates to more subtle activity changes due to excitability, trial-to-trial variability, and even cell birth and death.

Mixed-selective (MS) remapping explains activity modulation through changes or shifts in non-spatial, cognitive axes. This relates to ideas about mixed or conjunctive representations of position with other variables such as context, behavior, or sensory info.

Using this framework, we identify three underlying coding mechanisms that explain remapping.

Encoder-decoder (ED) remapping reflects a true change in the latent space, visualized as a rotation of the latent axes. This relates to classic multi-chart and grid realignment models, among others.

This allows us to visualize hippocampal population activity along positional and cognitive latent axes, with localized “place fields” on top. From this perspective, neural trajectories are composed of a linear latent component, plus a nonlinear nullspace component.

Many theories exist for remapping, but what types of neural activity patterns are compatible with these different views? We study this question in a model with one key assumption: that hippocampal population activity reflects a linearly-decodable latent space.