Quantifying stimulus-relevant representational drift using cross-modality contrastive learning

Abstract: Previous works investigating representational drift from sensory to central nervous systems converged to show that neural coding, especially at the population level, readily overcomes these session-to-session fluctuations. However, representational drift in the primary visual cortex is more prominent when presenting naturalistic stimuli than artificial stimuli. Animals continuously navigate natural environments during the evolutionary timescale. Why did evolution not get rid of representational drift if it was just an inconvenience? Here, we investigate how representational drift simultaneously influences the encoding of multiple behaviorally relevant features in a natural movie stimulus. Because natural environments contain multiple interacting spatio-temporal features, previous works only provided incomplete understanding of representational drift because of such simplification. Here, we use cross modality contrastive learning to learn an embedding of neural activity that retains only those relevant components of the natural movie stimulus. We also observe that our learned embedding is near-optimal in decoding a whole suite of natural features (scene, optic flow, complex spatio-temporal features, and time) and generalizable to decode those features from single-trial or novel hold-out data. Using this embedding as a surrogate model, we observe that representational drift perturbs the local geometry of the embedding, and this results in various changes in performance when we decode from a different session (90 min later) even at the population level. Our work further suggests that a separate compensation mechanism may be necessary for the optic flow features, as their autocorrelation scale is shorter than the minimum time needed to discriminate scene texture features. Thus, representational drift may encourage neural processing flexibility rather than be a mere nuisance.