ArticleScientific reports2025
Representational drift as the consequence of ongoing memory storage.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Manifold-constrained plasticity enables stable learning in recurrent neural circuits.PLoS computational biology · 2026Article
- Changes in perceptual sampling contribute to representational drift.bioRxiv : the preprint server for biology · 2026Article
- Locally balanced inhibition allows for robust learning of input-output associations in feedforward networks with Hebbian plasticity.Journal of computational neuroscience · 2026Article
- Representational drift reflects ongoing balancing of stochastic changes by Hebbian learning.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
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4 authors.
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Abstract
Memory systems with biologically constrained synapses have been the topic of intense theoretical study for over thirty years. Perhaps the most fundamental and far-reaching finding from this work is that the storage of new memories implies the partial erasure of already-stored ones. This overwriting leads to a decorrelation of sensory-driven activity patterns over time, even if the input patterns remain similar. Representational drift (RD) should therefore be an expected and inevitable consequence of ongoing memory storage. We tested this hypothesis by fitting a network model to data from long-term chronic calcium imaging experiments in mouse hippocampus. Synaptic turnover in the model inputs, consistent with the ongoing encoding of new activity patterns, accounted for the observed statistics of RD. This mechanism also provides a parsimonious explanation for the diverse effects of experience on drift found in experiment. Our results suggest that RD should be observed wherever neuronal circuits are involved in a process of ongoing learning or memory storage.
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