Representational drift reflects ongoing balancing of stochastic changes by Hebbian learning

Recent evidence indicates that even under stable environmental and behavioral conditions, responses to sensory stimuli undergo continuous reformatting over the course of days, a condition described as representational drift. However, the processes underlying this phenomenon remain poorly understood. Examining the dynamics of signal and noise correlations among neuron pairs in chronic calcium imaging experiments in the mouse auditory cortex, we investigate how activity-dependent, Hebbian-like plasticity and activity-independent, stochastic synaptic processes contribute to representational drift. We found that signal correlations predict future noise correlations, suggesting that stimulus-induced coactivation leads to increased effective connectivity between neuron pairs. Moreover, simple linear network models were able to account for the observed temporal dependencies between signal and noise correlations, but only if both Hebbian-like plasticity and stochastic changes of either inputs or recurrent synapses contribute to representational drift. In conclusion, our findings suggest that continuous sensory input–driven Hebbian-like plasticity can balance ongoing stochastic synaptic changes, thereby preventing the network’s functional degradation.