The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Impermanence of dendritic spines in live adult CA1 hippocampus
Open accessAbstract
The mammalian hippocampus is crucial for episodic memory formation and transiently retains information for about 3-4 weeks in adult mice and longer in humans. Although neuroscientists widely believe that neural synapses are elemental sites of information storage, there has been no direct evidence that hippocampal synapses persist for time intervals commensurate with the duration of hippocampal-dependent memory. Here we tested the prediction that the lifetimes of hippocampal synapses match the longevity of hippocampal memory. By using time-lapse two-photon microendoscopy in the CA1 hippocampal area of live mice, we monitored the turnover dynamics of the pyramidal neurons' basal dendritic spines, postsynaptic structures whose turnover dynamics are thought to reflect those of excitatory synaptic connections. Strikingly, CA1 spine turnover dynamics differed sharply from those seen previously in the neocortex. Mathematical modelling revealed that the data best matched kinetic models with a single population of spines with a mean lifetime of approximately 1-2 weeks. This implies ∼100% turnover in ∼2-3 times this interval, a near full erasure of the synaptic connectivity pattern. Although N-methyl-d-aspartate (NMDA) receptor blockade stabilizes spines in the neocortex, in CA1 it transiently increased the rate of spine loss and thus lowered spine density. These results reveal that adult neocortical and hippocampal pyramidal neurons have divergent patterns of spine regulation and quantitatively support the idea that the transience of hippocampal-dependent memory directly reflects the turnover dynamics of hippocampal synapses.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Perhaps even more problematic is the report that a substantial portion of dendritic spines on CA1 pyramidal cells turn over within a few weeks (416, 417).”
From Synaptic memory and CaMKII (2023) · cites this paper as [416]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Medial Temporal Lobe Memory System
Squire, L. R., & Zola-Morgan, S. (1991) · Science · doi:10.1126/science.1896849 · Cited by 3,282
Imaging Neuronal Subsets in Transgenic Mice Expressing Multiple Spectral Variants of GFP
Feng, G., Mellor, R. H., Bernstein, M., Keller-Peck, C., Nguyen, Q. T., Wallace, M., Nerbonne, J. M., Lichtman, J. W., & Sanes, J. R. (2000) · Neuron · doi:10.1016/s0896-6273(00)00084-2 · Cited by 3,042
The organization of recent and remote memories
Frankland, P. W., & Bontempi, B. (2005) · Nature Reviews Neuroscience · doi:10.1038/nrn1607 · Cited by 2,138
Synaptic tagging and long-term potentiation
Frey, U., & Morris, R. G. M. (1997) · Nature · doi:10.1038/385533a0 · Cited by 1,736
Dendritic spine changes associated with hippocampal long-term synaptic plasticity
Engert, F., & Bonhoeffer, T. (1999) · Nature · doi:10.1038/19978 · Cited by 1,412
Rapid formation and selective stabilization of synapses for enduring motor memories
Xu, T., Yu, X., Perlik, A. J., Tobin, W. F., Zweig, J. A., Tennant, K., Jones, T., & Zuo, Y. (2009) · Nature · doi:10.1038/nature08389 · Cited by 1,193
Stably maintained dendritic spines are associated with lifelong memories
Yang, G., Pan, F., & Gan, W.-B. (2009) · Nature · doi:10.1038/nature08577 · Cited by 1,182
Long-term dynamics of CA1 hippocampal place codes
Ziv, Y., Burns, L. D., Cocker, E. D., Hamel, E. O., Ghosh, K. K., Kitch, L. J., Gamal, A. E., & Schnitzer, M. J. (2013) · Nature Neuroscience · doi:10.1038/nn.3329 · Cited by 1,155
Self-citationTransient and persistent dendritic spines in the neocortex in vivo
Holtmaat, A. J. G. D., Trachtenberg, J. T., Wilbrecht, L., Shepherd, G. M., Zhang, X., Knott, G. W., & Svoboda, K. (2005) · Neuron · doi:10.1016/j.neuron.2005.01.003 · Cited by 1,131
Harris, K., & Stevens, J. (1989) · The Journal of Neuroscience · doi:10.1523/jneurosci.09-08-02982.1989 · Cited by 1,073
Cited by
Sorted by citations
Items 301–396 of 396
Synaptic Cell Adhesion: A Functional Perspective
Honkanen, K., & Sando, R. C. (2026) · Advances in neurobiology · doi:10.1007/978-3-032-12594-1_5 · Cited by 1
Cognitive impairment in neuromyelitis optica spectrum disorders
Алексеевна, В. Е., Kasatkin, D. S., Korchevnyi, S. E., & Bereznyak, K. I. (2024) · Neurology neuropsychiatry Psychosomatics · doi:10.14412/2074-2711-2024-3-117-122 · Cited by 1
O’Leary, T. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-15754-7.00086-9 · Cited by 1
Taste cells depend on axon proximity to generate presynaptic sites
Landon, S. M., Holder, E., Ng, A., Wood, R. M., Kuri, E. G., Pinto, L., Humayun, S., & Macpherson, L. J. (2025) · PLoS ONE · doi:10.1371/journal.pone.0325312 · Cited by 1
Stable excitatory-inhibitory synapse balance despite dynamic turnover
Garbett, K. A., Allen, J. P., Lopez, J. M., Smith, C. M., & Sando, R. C. (2026) · eLife · doi:10.1101/2025.06.02.657384 · Cited by 1
Klimmt, H., Kuhn, F. E., Kappel, D., Murthy, B., Ulivi, A. F., Argunşah, A. Ö., Vieweg, S., Huettl, R.-E., Remy, S., & Attardo, A. (2026) · Nature Communications · doi:10.1038/s41467-026-77800-w · Cited by 1
Self-citationRapidly reconfigurable dynamic computing in neural networks with fixed synaptic connectivity
Mason, K., Sennik, S., Clopath, C., Gruber, A., & Nicola, W. (2026) · Communications Biology · doi:10.1038/s42003-026-10877-7 · Cited by 1
Vadakkan, K. I. (2026) · Journal of Integrative Neuroscience · doi:10.31083/jin49226 · Cited by 1
In vivo optical imaging of brain activity
Mo, C., Chen, S., Zhai, M., Wu, R., Wang, Z., Yu, J. X., Wang, A., Chen, L., & Cheng, H. (2018) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/n972018-01142 · Cited by 1
Research on the role of hippocampus in memory consolidation
Yan, Z. (2023) · Theoretical and Natural Science · doi:10.54254/2753-8818/6/20230191 · Cited by 1
Dendritic Spine Plasticity and Memory Formation
Ammassari‐Teule, M., & Segal, M. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-809324-5.21113-9 · Cited by 1
Mikhaylova, M., & Kreutz, M. R. (2018) · e-Neuroforum · doi:10.1515/nf-2018-a006 · Cited by 1
Long-Term In Vivo Imaging of Structural Plasticity in Rodents
Poll, S., & Fuhrmann, M. (2018) · Handbook of behavioral neuroscience · doi:10.1016/b978-0-12-812028-6.00014-8 · Cited by 1
Imaging Synapse Formation and Remodeling In Vitro and In Vivo
Okabe, S. (2019) · Springer eBooks · doi:10.1007/978-981-13-7908-6_19 · Cited by 2
Adaptive optics two-photon microendoscopy for high-resolution and deep-brain imaging in vivo
Chen, C., Qin, Z., He, S., Wu, W., Wang, Y., Tam, K. F., Ip, N. Y., & Qu, J. Y. (2020) · Neural Imaging and Sensing · doi:10.1117/12.2546054 · Cited by 1
Dual sampling neural network: Learning without explicit optimization
Teramae, J.-N., & Tsubo, Y. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.043051 · Cited by 1
Local learning rules to attenuate forgetting in neural networksPreprint
Deistler, M., Sorbaro, M., Rule, M. E., & Hennig, M. H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.05097 · Cited by 1
Network resonance during slow-wave sleep facilitates memory consolidation through phase-codingPreprint
Skilling, Q. M., Clawson, B. C., Eniwaye, B., Shaver, J. J., Ognjanovski, N., Aton, S. J., & Żochowski, M. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/565242 · Cited by 1
Dual stochasticity in the cortex as a biologically plausible learning with the most efficient codingPreprint
Teramae, J.-N. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/811646 · Cited by 1
Souza, A. C., Golbert, D. C. F., Cassoli, J. S., Sánchez-Gendriz, I., Lima, V. V. F., da Silva, F. A. C., Martins‐de‐Souza, D., & Ribeiro, S. T. G. (2022) · Research Square · doi:10.21203/rs.3.rs-1842393/v2 · Cited by 1
Koyama, Y., Yamamoto, T., Hirayama, J.-I., Jimura, K., Sadato, N., & Chikazoe, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.12.543130 · Cited by 1
Redman, W. T., Acosta–Mendoza, S., Wei, X.-X., & Goard, M. J. (2024) · eLife · doi:10.7554/elife.100652.1 · Cited by 1
Golf, S. R., Trotter, J. H., Wang, J., Nakahara, G., Han, X., Wernig, M., & Südhof, T. C. (2025) · eLife · doi:10.7554/elife.87589.2 · Cited by 1
Neurons Rho to Get in Shape for the Day
Mann, K. J., & Clandinin, T. R. (2015) · Cell · doi:10.1016/j.cell.2015.07.044
Molekulare Dynamik der neuronalen Informationsübertragung
Heine, M., & Bikbaev, A. F. (2018) · e-Neuroforum · doi:10.1515/nf-2017-0042
Molecular dynamics of neuronal information transfer
Heine, M., & Bikbaev, A. F. (2018) · e-Neuroforum · doi:10.1515/nf-2017-a042
Mikhaylova, M., & Kreutz, M. R. (2018) · e-Neuroforum · doi:10.1515/nf-2018-0006
A Thermodynamic Model of Mesoscale Neural Field Dynamics: Derivation and Linear AnalysisPreprint
Qin, Y. F., Maurer, A. P., & Sheremet, A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.25.172288 · Cited by 1
Exciting inhibition: Local regulation of synapse formation and plasticity
Kruijssen, D. L. H. (2020) · Utrecht University Repository (Utrecht University) · doi:10.33540/296
Hippocampal and Neocortical Dynamics During Long-term Memory in Macaques
Hussin, A. T. (2020) · YorkSpace (York University)
Chopra, R. (2020) · Deep Blue (University of Michigan)
Research on the Role of Hippocampus in Remote Memory
Sun, Y. (2021) · International Conference on Biomedical Engineering and Technology (ICBET) · doi:10.1145/3460238.3460264
Drift and stabilization of cortical response selectivity
Schmidt, A. (2021) · eDiss (Georg-August-Universität Göttingen) · doi:10.53846/goediss-8733
(2019) · Elsevier eBooks · doi:10.1016/b978-3-437-21882-8.00017-7
Kalaska, J. F. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.733934910.793560134
Souza, A. C., Golbert, D. C. F., Cassoli, J. S., Sánchez-Gendriz, I., Lima, V. V. F., da Silva, F. A. C., Martins‐de‐Souza, D., & Ribeiro, S. T. G. (2022) · Research Square · doi:10.21203/rs.3.rs-1842393/v1
Bartos, M., Kaufhold, D., Ocaña-Fernández, M. D. Á., Cazala, A., Yuan, M., Kulik, Á., Cholvin, T., Steup, S., Sauer, J., Elgueta, C., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-2787096/v1
eLife Assessment: Drift of neural ensembles driven by slow fluctuations of intrinsic excitability
Giocomo, L. M. (2023) · eLife · doi:10.7554/elife.88053.1.sa3
Drift of neural ensembles driven by slow fluctuations of intrinsic excitabilityPreprint
Delamare, G., Zaki, Y., Cai, D. J., & Clopath, C. (2023) · eLife · doi:10.7554/elife.88053.1
Golf, S. R., Trotter, J. H., Wang, J., Nakahara, G., Han, X., Wernig, M., & Südhof, T. C. (2023) · eLife · doi:10.7554/elife.87589
Chikazoe, J., Koyama, Y., Yamamoto, T., Hirayama, J.-I., Jimura, K., & Sadato, N. (2023) · Research Square · doi:10.21203/rs.3.rs-3146460/v1
Author Response: Drift of neural ensembles driven by slow fluctuations of intrinsic excitability
Delamare, G., Zaki, Y., Cai, D. J., & Clopath, C. (2023) · eLife · doi:10.7554/elife.88053.2.sa2
eLife Assessment: Drift of neural ensembles driven by slow fluctuations of intrinsic excitability
Giocomo, L. M. (2023) · eLife · doi:10.7554/elife.88053.2.sa1
Author response: Drift of neural ensembles driven by slow fluctuations of intrinsic excitability
Delamare, G., Zaki, Y., Cai, D. J., & Clopath, C. (2024) · eLife · doi:10.7554/elife.88053.3.sa2
Synaptic plasticity and learning and memory
Cooke, S. F., & Chaloner, F. A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15754-7.00044-4
Repetition Suppression and Related Effects
Kryukov, V. I. (2025) · Mathematical Biology and Bioinformatics · doi:10.17537/2024.19.609
Redman, W. T., Acosta–Mendoza, S., Wei, X.-X., & Goard, M. J. (2025) · eLife · doi:10.7554/elife.100652.2
Redman, W. T., Acosta–Mendoza, S., Wei, X.-X., & Goard, M. J. (2025) · eLife · doi:10.7554/elife.100652.3.sa4
Field-conjugate adaptive optics enables extended field-of-view microendoscopy for deep brain imaging
Wang, Q., Liu, R., & Lim, B. K. (2025) · Optica · doi:10.1364/optica.565483
Advances in the detection and applications of spatial cognitive neural information
Xu, Z., Mingchuan, W. A. N. G., Xu, W., Wang, Y., Lv, S., Song, Y., Wang, M., Liu, J., Wang, Y., Wu, Y., et al. (2025) · Scientia Sinica Informationis · doi:10.1360/ssi-2024-0398
(2022) · Elsevier eBooks · doi:10.1016/b978-3-437-21884-2.00033-7
(2022) · Elsevier eBooks · doi:10.1016/b978-3-437-21884-2.00017-9
Coordinated dynamics of excitatory and inhibitory synapse assemblyPreprint
Garbett, K. A., Allen, J. P., & Sando, R. C. (2025) · eLife · doi:10.7554/elife.107635.1
Stable excitatory-inhibitory synapse balance despite dynamic turnoverPreprint
Garbett, K. A., Allen, J. P., Lopez, J. M., Smith, C. M., & Sando, R. C. (2025) · eLife · doi:10.7554/elife.107635
Kashiwagi, Y., Liu, Q., Go, Y., Saito, R., Aiba, A., Nakazawa, T., & Okabe, S. (2026) · eLife · doi:10.7554/elife.109083.4
Nakamura, K., Endo, K., & Kazama, H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.30.679426
Representational drift shows same-class acceleration in visual cortex and artificial neural networksPreprint
Liu, J., Farahat, A., & Vinck, M. A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.05.686897
Timing-dependent effects of maternal separation stress in female rats: Insights into sex differences
Kajita, Y., Takabayashi, K., & Mushiake, H. (2025) · Physiology & Behavior · doi:10.1016/j.physbeh.2025.115175
Gaitanidis, A., Pampanin, V., Thiem, J., Kalaras, G., Huthmacher, L., Hürkey, S., Kadas, D., Dimitriadou, A., Ntogka, A., Sanyal, S., et al. (2025) · PLoS Biology · doi:10.1371/journal.pbio.3003553 · Cited by 1
Kashiwagi, Y., Liu, Q., Go, Y., Saito, R., Aiba, A., Nakazawa, T., & Okabe, S. (2025) · eLife · doi:10.7554/elife.109083.1.sa0
Kashiwagi, Y., Liu, Q., Go, Y., Saito, R., Aiba, A., Nakazawa, T., & Okabe, S. (2025) · eLife · doi:10.7554/elife.109083
Decoding Spine Nanostructure in Mental Disorders Reveals a Schizophrenia-Linked Role for Ecrg4Preprint
Kashiwagi, Y., Liu, Q., Go, Y., Saito, R., Aiba, A., Nakazawa, T., & Okabe, S. (2025) · eLife · doi:10.7554/elife.109083.1
MDMA enhances prefrontal plasticity and representational drift during fear extinctionPreprint
Geva, N., Jefferson, S. J., Krishnamurthy, E., Anderson, T. L., Rondeau, J. A., Wehrle, P. H., Rosado, A. F., Pittenger, C., Krystal, J. H., & Kaye, A. P. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.06.710094
Norimoto, H. (2016) · Institutional Repositories DataBase (IRDB) · doi:10.15083/00073872
Hippocampal plasticity underlying learning and memory processes in healthy and diseased conditions
Petsophonsakul, P. (2017) · theses.fr (ABES) · doi:10.70675/5fdda64ez871dz42c8za517zf44c3618705e
The role of intrinsic neuronal excitability for Prelimbic network function
Sa, R. D. (2019) · theses.fr (ABES) · doi:10.70675/8b11b402z0ae7z4587z8084z65b59ce7ba62
Development of advanced optical systems for spatially and temporally precise brain interrogation
Molinier, C. (2021) · theses.fr (ABES) · doi:10.70675/128c1cd5z1210z4f3ez858dzd0acd752045b
Identifying distinct ensembles recruited during memory acquisition to form the engram
Pouget, C. (2024) · theses.fr (ABES) · doi:10.70675/339fe468z82fbz4f38zbe9azb44f1082d215
Molecular mechanisms underlying the surface organization of the NMDA receptors during development
Bénac, N. (2024) · theses.fr (ABES) · doi:10.70675/e3508a2ezb62ez4f99zb27bz156201271974
Qian, X., Liu, C., Zhang, Y., Subramaniam, S., Fang, Q., Deen, J., & Jiang, W. (2026) · Progress in Biomedical Engineering · doi:10.1088/2516-1091/ae67c5
Kashiwagi, Y., Liu, Q., Go, Y., Saito, R., Aiba, A., Nakazawa, T., & Okabe, S. (2026) · eLife · doi:10.7554/elife.109083.2
Axial Imaging Technologies in Multiphoton Microscopy
He, H., Shi, M., Xie, Y., Dong, X., & Wong, K. K. Y. (2026) · Advanced Devices & Instrumentation · doi:10.34133/adi.0199
Stable excitatory-inhibitory synapse balance despite dynamic turnoverPreprint
Garbett, K. A., Allen, J. P., Lopez, J. M., Smith, C. M., & Sando, R. C. (2026) · eLife · doi:10.7554/elife.107635.2
Kashiwagi, Y., Liu, Q., Go, Y., Saito, R., Aiba, A., Nakazawa, T., & Okabe, S. (2026) · eLife · doi:10.7554/elife.109083.3
Functional relevance of mobile and clustered Ca V 2.1 channels in central synapsesPreprint
Khallouqi, A. E., Amaral, C., Lassen, A. S., Weißbach, S., Werkmann, C., Bikbaev, A. F., Mark, M. D., Herlitze, S., Heck, J., Walter, A. M., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.13.737722
Deep learning-assisted mapping of dendritic spines using sequential 2D two-photon calcium imaging
Cupolillo, D., Regio, V., Galleano, V., & Barberis, A. (2026) · iScience · doi:10.1016/j.isci.2026.117010
Challenges in Studying Synapses in Alzheimer's Disease
Südhof, T. C. (2026) · Annals of Neurology · doi:10.1002/ana.78331
Manifold-constrained plasticity enables stable learning in recurrent neural circuits
Godin, C., & Thivierge, J. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014719
Clustering of the Adhesion-GPCR Latrophilin-3 Drives Excitatory Synapse AssemblyPreprint
Huyan, C., Bhat, A. A., Landa, G., Südhof, T. C., & Dunn, A. R. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.12.757884
Local processors: Active dendrites and artificial intelligence
Hu, Y., Glatz, N. S., & Palmer, L. M. (2026) · Current Research in Neurobiology · doi:10.1016/j.crneur.2026.100169
Metadata
- Authors
- Attardo, A., Fitzgerald, J. E., & Schnitzer, M. J.
- Affiliations
- Howard Hughes Medical Institute (US)
- Stanford University (US)
- Corresponding author
- Schnitzer, Mark J.
- Venue
- Nature
- Published
- 2015-06-22
- Volume
- 523
- Issue
- 7562
- Pages
- 592-596
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC4648621
- OpenAlex
- W1655792606
- Cited by
- 399
- References
- 34
- Citations by year
- 2026: 23 · 2025: 43 · 2024: 29 · 2023: 43 · 2022: 39 · 2021: 45 · 2020: 38 · 2019: 34 · 2018: 31 · 2017: 32 · 2016: 35 · 2015: 7
- FWCI
- 16.33
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Institute on Aging (R21AG038771)
- National Institute of Mental Health (R21MH092809)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Developmental Neuroscience › Neurogenesis and neuroplasticity mechanisms
- Keywords
- hippocampal CA1, synaptic plasticity, episodic memory, NMDA receptor, pyramidal neurons, synaptic connectivity, microendoscopy, spine density, adult mice, NMDA receptor blockade
- MeSH terms
- Animals, Endoscopy, Kinetics, Male, Neuronal Plasticity, Synapses, Time Factors, Receptors, N-Methyl-D-Aspartate, Photons, Pyramidal Cells, Neocortex, Dendritic Spines, Mice, CA1 Region, Hippocampal, Memory, Episodic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
