ABSTRACT Neural representations of space in the hippocampus and related brain areas change over timescales of days-weeks, even in familiar contexts and when behavior appears stable. It is unclear whether this ‘representational drift’ is primarily driven by the passage of time or by behavioral experience. Here we present a novel deep-learning approach for measuring network-level representational drift, quantifying drift as the rate of change in decoder error of deep neural networks as a function of train-test lag. Using this method, we analyse a longitudinal dataset of 0.5–475 Hz broadband local field potential (LFP) data recorded from dorsal hippocampal CA1, medial prefrontal cortex and parietal cortex of six rats over ∼ 30 days, during learning of a spatial navigation task in an unfamiliar environment. All three brain regions contained clear spatial representations which improve and drift over training sessions. We find that the rate of drift slows for later training sessions. Finally, we find that drift is statistically better explained by task-relevant rewarded experiences within the maze, rather than the passage of time or number of sessions the animal spent on the maze. Our use of deep neural networks to quantify drift in broadband neural time series unlocks new possibilities for testing which aspects of behavior drive representational drift.

Deep learning-based location decoding reveals that across-day representational drift is better predicted by rewarded experience than time