Error-driven changes in hippocampal representations accompany flexible re-learning.

Flexible behavior requires both the learning of new associations, and the suppression of previous ones, but how neural circuits achieve this balance remains unclear. Here we show that continuous changes in hippocampal representations, known as drift, may facilitate this process. We used voluntary head-fixation and calcium imaging to record from CA1 in rats during an odor-guided navigation task that required frequent re-learning. We found systematic representational changes over the course of the multi-hour sessions that were increased following errors. A simple neural network model revealed that such error-driven drift can enable flexible re-learning by allowing new associations to form from new neural patterns. A consequence of this is that previous associations are maintained in latent synaptic weights. These findings reconcile the apparent tension between representational drift and stable memory storage, demonstrating how dynamic neural codes could support both flexible behavior and lasting memories.