Abstract
The prelimbic cortex (PrL) is involved in the organization of operant behaviors, but the relationship between longitudinal PrL neural activity and operant learning and performance is unknown. Here, we developed deep behavior mapping (DBM) to identify behavioral microstates in video recordings. We combined DBM with longitudinal calcium imaging to quantify behavioral tuning in PrL neurons as mice learned an operant task. We found that a subset of PrL neurons were strongly tuned to highly specific behavioral microstates, both task and non-task related. Overlapping neural ensembles were tiled across consecutive microstates in the response-reinforcer sequence, forming a continuous map. As mice learned the operant task, weakly tuned neurons were recruited into new ensembles, with a bias toward behaviors similar to their initial tuning. In summary, our data suggest that the PrL contains neural ensembles that jointly encode a map of behavioral states that is fine grained, is continuous, and grows during operant learning.