An efficient coding account of representational drift

Neural responses to stimuli change continually over days even as perception and behavior remain stable, a phenomenon called representational drift. Drift has been modeled with unsupervised learning objectives that admit a degenerate solution space of representations that preserve input similarity structure. But such objectives are ill-suited for early sensory areas, where the code must allocate sensitivity according to input statistics rather than reproduce similarity structure. Here, we test the hypothesis that drift in these areas instead arises from mechanisms supporting efficient coding, formalized as maximizing Fisher information about the stimulus in the neural population. We show that a biologically plausible uncertainty-gated differential Hebbian plasticity rule, in which synaptic change depends on the time derivatives of pre- and post-synaptic activity, implements gradient ascent on Fisher information, and that adding synaptic noise produces substantial drift in individual neurons while population Fisher information remains stable. We confirm this prediction in longitudinal calcium imaging from the primary visual and auditory cortex in mice, where single-neuron tuning drifts over days while population Fisher information does not. These findings offer a novel, efficient coding account of drift in sensory cortex.