Hebbian learning accounts for the effects of experience and novel exposure on representational drift in CA1 of hippocampus

Abstract

Neuronal activity patterns slowly change over time even when sensory stimuli and animal behavior remain stable, a phenomenon known as representational drift. In area CA1 of the hippocampus, the amount of drift in the tuning of place cells on a familiar linear track is proportional to the time the animal spends exploring that track while the drift in cells mean rate depends on the absolute time elapsed between sessions. Recently it was shown that exploration of a novel, enriched environment between sessions on the familiar track actually decreases the drift in tuning on that track, i.e. it stabilizes place fields compared to a baseline condition without the novel learning. This finding challenges computational models of drift in which new learning leads to overwriting and hence one would expect more drift, not less. Here we show that such models are indeed compatible with the observed findings as long as spatially-tuned input populations to CA1 which are active on the track versus the enriched environment are largely non-overlapping. Furthermore, in order to reproduce the findings, we must assume that the total amount of learning between the baseline and novel-exposure conditions is the same. Namely, the synaptic resources available for encoding patterns, or episodes, is fixed over a given period of time, but can be preferentially allocated to episodes of particular salience, such as the exploration of novel environments.