Representational drift reflects ongoing balancing of stochastic changes by Hebbian learning

Abstract

Recent studies have shown that neural responses to familiar sounds continuously change in stable environments, a phenomenon termed representational drift. By analyzing long-term recordings from the mouse auditory cortex, we suggest that this drift results from an interplay between two processes: Hebbian-like plasticity, where neural connections are strengthened through coactivation, and stochastic synaptic changes that randomly alter neural connectivity. Using correlation analyses and computational modeling, we show that both processes are necessary to explain the observed patterns of neural activity. These findings provide mechanistic insights into how neural circuits maintain stable function despite continuous reorganization, with implications for understanding learning, memory, and neural adaptation. 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.

Keywords: representational drift; Hebbian learning; synaptic plasticity; stochastic processes; auditory cortex; neural dynamics; population coding

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Recommended citation: J-B Eppler, T Lai, DF Aschauer, S Rumpel, & M Kaschube. (2026). "Representational drift reflects ongoing balancing of stochastic changes by Hebbian learning." Proceedings of the National Academy of Sciences, 123(5), e2503046123. https://doi.org/10.1073/pnas.2503046123