Evidence map›Paper›PMID 31569062›Full record

ReviewCurrent opinion in neurobiology2019

Causes and consequences of representational drift.

Michael E Rule, Timothy O'Leary, Christopher D Harvey

Abstract readReview
In one paragraph

Review in Current opinion in neurobiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 128 papers.

0numbers the graph read from it
0cells of the map it votes in
128citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

128 citing papers in PubMed.

  1. Article
  2. Stars2Cells: Astrometric Tracking of Neurons Across Imaging Sessions.bioRxiv : the preprint server for biology · 2026
    Article
  3. Changes in perceptual sampling contribute to representational drift.bioRxiv : the preprint server for biology · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Coordinated Representational Drift Across the Mouse Cortex.bioRxiv : the preprint server for biology · 2026
    Article
  11. Article
  12. Contamination of Engram Coactivity Networks During Forgetting.bioRxiv : the preprint server for biology · 2026
    Article
  13. Review
  14. Article
  15. Neural Representations of Reward-Related Memories Shift across Development.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026
    Article
  16. Stiefel Manifold Dynamical Systems for Tracking Representational Drift.bioRxiv : the preprint server for biology · 2026
    Article
  17. Order parameters and phase transitions of continual learning in deep neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  18. Review
  19. Article
  20. Article

68 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Michael E RuleDepartment of Engineering, University of Cambridge, Cambridge CB21PZ, United Kingdom.
Timothy O'LearyDepartment of Engineering, University of Cambridge, Cambridge CB21PZ, United Kingdom. Electronic address: timothy.oleary@eng.cam.ac.uk.
Christopher D HarveyDepartment of Neurobiology, Harvard Medical School, Boston, MA 02115 USA.

Funding

Studying perceptual decision-making across cortex by combining population imaging, connectomics, and computational modelingR01NS108410 · NINDS · HARVARD MEDICAL SCHOOL · PI HARVEY, CHRISTOPHER D, LEE, WEI-CHUNG ALLEN · 2018 to 2022
$5.7M
Parietal Cortex Networks for Sensorimotor Processing During NavigationR01NS089521 · NINDS · HARVARD MEDICAL SCHOOL · PI HARVEY, CHRISTOPHER D · 2015 to 2024
$4.5M
New approaches to understand neuronal microcircuit dynamics for working memoryR01MH107620 · NIMH · HARVARD MEDICAL SCHOOL · PI HARVEY, CHRISTOPHER D · 2015 to 2019
$2.8M
NIMH NIH HHS R01 MH107620NINDS NIH HHS R01 NS089521NINDS NIH HHS R01 NS108410
6 · The paper itself

Abstract

The nervous system learns new associations while maintaining memories over long periods, exhibiting a balance between flexibility and stability. Recent experiments reveal that neuronal representations of learned sensorimotor tasks continually change over days and weeks, even after animals have achieved expert behavioral performance. How is learned information stored to allow consistent behavior despite ongoing changes in neuronal activity? What functions could ongoing reconfiguration serve? We highlight recent experimental evidence for such representational drift in sensorimotor systems, and discuss how this fits into a framework of distributed population codes. We identify recent theoretical work that suggests computational roles for drift and argue that the recurrent and distributed nature of sensorimotor representations permits drift while limiting disruptive effects. We propose that representational drift may create error signals between interconnected brain regions that can be used to keep neural codes consistent in the presence of continual change. These concepts suggest experimental and theoretical approaches to studying both learning and maintenance of distributed and adaptive population codes.

Indexed as

BrainLearningMemoryNeurons

Identifiers

PMID31569062
PMCPMC7385530

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.