No abstract

Memories of past events can be recalled long after the event, indicating stability. But new experiences are also integrated into existing memories, indicating plasticity. In the hippocampus, spatial representations are known to remain stable, but have also been shown to drift over long periods of time. We hypothesized that experience, more than the passage of time, is the driving force behind memory plasticity. We compared the stability of place cells in the hippocampus of mice traversing two similar, familiar tracks for different durations. We found that the more time spent in an environment, the greater the representational drift, regardless of the total elapsed time. Our results suggest that spatial representation is a dynamic process, related to the ongoing experiences within a specific context, and is related to the accumulation of new memories rather than to passive forgetting.

Recent studies show that, even in constant environments, the tuning of single neurons changes over time in a variety of brain regions. This representational drift has been suggested to be a consequence of continuous learning under noise, but its properties are still not fully understood. To uncover the underlying mechanism, we trained an artificial network to perform a predictive coding task. After the loss converged, the activity slowly became sparser. We verified the generality of this phenomenon across modeling choices. This sparseness is a manifestation of drift in the solution space to a flatter area. It is consistent with recent experimental results demonstrating that CA1 spatial code becomes sparser after familiarity. We conclude that learning is divided into three overlapping phases: Fast familiarity with the environment, slow implicit regularization, and a steady state of null drift. These findings open the possibility of inferring learning algorithms from observations of drift statistics.