ArticleeLife2024
Representational drift as a result of implicit regularization.
Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 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
- Coordinated Representational Drift Across the Mouse Cortex.Research square · 2026Article
- Coordinated Representational Drift Across the Mouse Cortex.bioRxiv : the preprint server for biology · 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
- Single-Field Evolution Rule Governs the Dynamics of Representational Drift in Mouse Hippocampal Dorsal CA1 Region.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Sensory reformatting for a working visual memory.Trends in cognitive sciences · 2025Review
- Stability through plasticity: Finding robust memories through representational drift.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Distributed and drifting signals for working memory load in human cortex.bioRxiv : the preprint server for biology · 2025Article
- Representational drift as the consequence of ongoing memory storage.Scientific reports · 2025Article
- Representational drift without synaptic plasticity.bioRxiv : the preprint server for biology · 2025Article
- Representational drift and learning-induced stabilization in the piriform cortex.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Time Makes Space: Emergence of Place Fields in Networks Encoding Temporally Continuous Sensory Experiences.bioRxiv : the preprint server for biology · 2025Article
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Abstract
Recent studies show that, even in constant environments, the tuning of single neurons changes over time in a variety of brain regions. This representational drift has been suggested to be a consequence of continuous learning under noise, but its properties are still not fully understood. To investigate the underlying mechanism, we trained an artificial network on a simplified navigational task. The network quickly reached a state of high performance, and many units exhibited spatial tuning. We then continued training the network and noticed that the activity became sparser with time. Initial learning was orders of magnitude faster than ensuing sparsification. This sparsification is consistent with recent results in machine learning, in which networks slowly move within their solution space until they reach a flat area of the loss function. We analyzed four datasets from different labs, all demonstrating that CA1 neurons become sparser and more spatially informative with exposure to the same environment. We conclude that learning is divided into three overlapping phases: (i) Fast familiarity with the environment; (ii) slow implicit regularization; and (iii) a steady state of null drift. The variability in drift dynamics opens the possibility of inferring learning algorithms from observations of drift statistics.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.