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Perineuronal nets stabilize the grid cell network
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Abstract
Grid cells are part of a widespread network which supports navigation and spatial memory. Stable grid patterns appear late in development, in concert with extracellular matrix aggregates termed perineuronal nets (PNNs) that condense around inhibitory neurons. It has been suggested that PNNs stabilize synaptic connections and long-term memories, but their role in the grid cell network remains elusive. We show that removal of PNNs leads to lower inhibitory spiking activity, and reduces grid cells' ability to create stable representations of a novel environment. Furthermore, in animals with disrupted PNNs, exposure to a novel arena corrupted the spatiotemporal relationships within grid cell modules, and the stored representations of a familiar arena. Finally, we show that PNN removal in entorhinal cortex distorted spatial representations in downstream hippocampal neurons. Together this work suggests that PNNs provide a key stabilizing element for the grid cell network.
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“and in V1 visual cortex, ChABC decreases both excitatory and inhibitory inputs to PV interneurons [46, 47].”
From The extracellular matrix and perineuronal nets in memory (2022) · cites this paper as [46]
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“Both hippocampus and perirhinal cortex are rich in PV interneurons enwrapped with PNNs, and PNNs are also present on some hippocampal pyramidal neurons [46, 101, 102].”
From The extracellular matrix and perineuronal nets in memory (2022) · cites this paper as [46]
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“All the social memory associated regions are highly populated with PNN-surrounded PV neurons [46, 66, 130, 131].”
From The extracellular matrix and perineuronal nets in memory (2022) · cites this paper as [46]
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“while in the entorhinal cortex, ChABC reduces inhibitory inputs”
From The extracellular matrix and perineuronal nets in memory (2022) · cites this paper as [46]
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“Following PNN removal in entorhinal cortex there are distorted spatial representations in downstream hippocampal neurons (Fig. 2C) [46].”
From The extracellular matrix and perineuronal nets in memory (2022) · cites this paper as [46]
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“This suggests that PNNs contribute to ensure a rigid grid cell network, which is essential for new representations to form, and that the heightened network plasticity caused by PNN removal interferes with stored spatial representations and perhaps memories.”
From The extracellular matrix and perineuronal nets in memory (2022) · cites this paper as [46]
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“However, degradation of PNNs in medial entorhinal cortex, where PV positive neurons are enwrapped in particularly dense PNNs, destabilizes the grid cell networks leading to impaired representations of new environments [46].”
From The extracellular matrix and perineuronal nets in memory (2022) · cites this paper as [46]
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“This suggests that PNNs contribute to ensure a rigid grid cell network, which is essential for new representations to form, and that the heightened network plasticity caused by PNN removal interferes with stored spatial representations and perhaps memories.”
From The extracellular matrix and perineuronal nets in memory (2022) · cites this paper as [46]
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“The higher performance in prokaryotes is consistent with previous observations regarding the availability of evolutionary information in prokaryotes compared to Eukarya27 (Supplementary Fig. 1).”
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“Another recently published method obtains AUC 0.76 on this set27.”
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“We used 1661 protein complexes with known interfaces from a recent study27 to test the developed pipeline.”
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“The study of AF2s ability to separate interacting and non-interacting proteins here contains more extensive data than recent studies27.”
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“Pairing the correct sequences should result in MSAs containing inter-chain co-evolutionary information27.”
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“PLCG2 polymorphisms are associated with both reduced and increased risk of Alzheimer’s disease (AD) and with longevity.”
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Cites
Microstructure of a spatial map in the entorhinal cortex
Hafting, T., Fyhn, M., Molden, S., Moser, M.-B., & Moser, E. I. (2005) · Nature · doi:10.1038/nature03721 · Cited by 4,068
Self-citationMechanisms of Gamma Oscillations
Buzsáki, G., & Wang, X.-J. (2012) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-062111-150444 · Cited by 2,796
Critical period plasticity in local cortical circuits
Hensch, T. K. (2005) · Nature Reviews Neuroscience · doi:10.1038/nrn1787 · Cited by 2,323
Path integration and the neural basis of the 'cognitive map'
McNaughton, B. L., Battaglia, F. P., Jensen, O., Moser, E. I., & Moser, M.-B. (2006) · Nature Reviews Neuroscience · doi:10.1038/nrn1932 · Cited by 2,024
Reactivation of Ocular Dominance Plasticity in the Adult Visual Cortex
Pizzorusso, T., Medini, P., Berardi, N., Chierzi, S., Fawcett, J. W., & Maffei, L. (2002) · Science · doi:10.1126/science.1072699 · Cited by 1,679
The effects of changes in the environment on the spatial firing of hippocampal complex-spike cells
Muller, R., & Kubie, J. (1987) · The Journal of Neuroscience · doi:10.1523/jneurosci.07-07-01951.1987 · Cited by 1,581
Conjunctive Representation of Position, Direction, and Velocity in Entorhinal Cortex
Sargolini, F., Fyhn, M., Hafting, T., McNaughton, B. L., Witter, M. P., Moser, M.-B., & Moser, E. I. (2006) · Science · doi:10.1126/science.1125572 · Cited by 1,485
Self-citationThree groups of interneurons account for nearly 100% of neocortical GABAergic neurons
Rudy, B., Fishell, G., Lee, S., & Hjerling‐Leffler, J. (2011) · Developmental Neurobiology · doi:10.1002/dneu.20853 · Cited by 1,420
Spatial representation in the entorhinal cortex
Fyhn, M., Molden, S., Witter, M. P., Moser, E. I., & Moser, M.-B. (2004) · Science · doi:10.1126/science.1099901 · Cited by 1,339
Self-citationIntracellular Features Predicted by Extracellular Recordings in the Hippocampus In Vivo
Henze, D. A., Borhegyi, Z., Csicsvari, J., Mamiya, A., Harris, K. D., & Buzsáki, G. (2000) · Journal of Neurophysiology · doi:10.1152/jn.2000.84.1.390 · Cited by 942
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Cormerais, Y., Lapp, S. C., Kalafut, K. C., Cissé, M. Y., Shin, J., Stefadu, B., Personnaz, J., Schrötter, S., Freed, J., D’Amore, A., et al. (2024) · Developmental Cell · doi:10.1016/j.devcel.2025.05.008 · Cited by 24
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Mohanty, S. K., Chiaromonte, F., & Makova, K. D. (2025) · Genome biology · doi:10.1186/s13059-025-03635-1 · Cited by 21
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Curci, P. L., Zhang, J., Mähler, N., Seyfferth, C., Mannapperuma, C., Diels, T., Hautegem, T. V., Jonsen, D., Street, N. R., Hvidsten, T. R., et al. (2022) · PLANT PHYSIOLOGY · doi:10.1093/plphys/kiac374 · Cited by 20
Sabatier, P., Beusch, C. M., Meng, Z., & Zubarev, R. A. (2022) · Analytical Chemistry · doi:10.1021/acs.analchem.2c03506 · Cited by 22
Misino, S., Busch, A., Wagner, C. B., Bento, F., & Luke, B. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac1125 · Cited by 23
Castellan, M., Zamperin, G., Franzoni, G., Foiani, G., Zorzan, M., Drzewnioková, P., Mancin, M., Brian, I., Bortolami, A., Pagliari, M., et al. (2023) · Viruses · doi:10.3390/v15020428 · Cited by 21
Bhati, M., Mapel, X. M., Lloret‐Villas, A., & Pausch, H. (2023) · Genetics · doi:10.1093/genetics/iyad161 · Cited by 21
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Yadav, P., Quattrone, A. C., Yang, Y., Owens, J., Kiat, R., Kuppusamy, T., Russo, S. E., & Weber, K. A. (2023) · ISME Communications · doi:10.1038/s43705-023-00335-4 · Cited by 20
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Jo, H., Hong, H., Hwang, H. J., Chang, W. S., & Kim, J. K. (2023) · Patterns · doi:10.1016/j.patter.2023.100899 · Cited by 24
A sensitive period for the development of episodic-like memory in mice
Ramsaran, A. I., Ventura, S., Gallucci, J., de Snoo, M. L., Josselyn, S. A., & Frankland, P. W. (2024) · Current Biology · doi:10.1016/j.cub.2025.03.032 · Cited by 24
Grisard, E., Névo, N., Lescure, A., Doll, S. G., Corbé, M., Jouve, M. J., Lavieu, G., Joliot, A., Nery, E. D., Jaular, L. M., et al. (2022) · Journal of Extracellular Vesicles · doi:10.1002/jev2.12242 · Cited by 18
Armbruster, E. G., Rani, P., Lee, J., Klusch, N., Hutchings, J., Hoffman, L. Y., Buschkaemper, H., Enüstün, E., Adler, B. A., Inlow, K., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.20.558163 · Cited by 20
Zhovmer, A. S., Manning, A., Smith, C., Hayes, J. B., Burnette, D. T., Wang, J., Cartagena‐Rivera, A. X., Dokholyan, N. V., Singh, R. K., & Tabdanov, E. D. (2021) · ACS Nano · doi:10.1021/acsnano.1c04435 · Cited by 19
Martin, P. C. N., Kim, H., Lövkvist, C., Hong, B., & Won, K. (2022) · Molecular Systems Biology · doi:10.15252/msb.202211080 · Cited by 19
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Astorkia, M., Lachman, H. M., & Zheng, D. (2022) · Journal of Neurodevelopmental Disorders · doi:10.1186/s11689-022-09441-1 · Cited by 19
Riquelme‐Guzmán, C., Beck, T., Edwards‐Jorquera, S., Schlüßler, R., Müller, P. F., Guck, J. R., Möllmert, S., & Sandoval‐Guzmán, T. (2022) · Open Biology · doi:10.1098/rsob.220078 · Cited by 19
Robust genome editing via modRNA-based Cas9 or base editor in human pluripotent stem cells
Haideri, T., Howells, A. R., Jiang, Y., Yang, J., Bao, X., & Lian, X. L. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100290 · Cited by 18
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Qin, R., Mahal, L. K., & Bojar, D. (2022) · iScience · doi:10.1016/j.isci.2022.105163 · Cited by 19
Post-phagocytosis activation of NLRP3 inflammasome by two novel T6SS effectors
Cohen, H., Baram, N., Fridman, C. M., Edry‐Botzer, L., Salomon, D., & Gerlic, M. (2022) · eLife · doi:10.7554/elife.82766 · Cited by 19
Kane, H., LaMarche, N. M., Scannail, A. N., Garza, A., Koay, H., Azad, A. I., Kunkemoeller, B. H., Stevens, B., Brenner, M. B., & Lynch, L. (2022) · eLife · doi:10.7554/elife.76586 · Cited by 18
Zhu, Y., & Champer, J. (2023) · ACS Synthetic Biology · doi:10.1021/acssynbio.2c00611 · Cited by 19
Kraus, F., Tune, S., Obleser, J., & Herrmann, B. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.2181-22.2023 · Cited by 19
Roswell, M., Harrison, T., & Genung, M. A. (2023) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2022.0186 · Cited by 18
Wynn, E. A., Dide-Agossou, C., Reichlen, M. J., Rossmassler, K., Mubarak, R. A., Reid, J. J., Tabor, S. T., Born, S. E. M., Ransom, M., Davidson, R. M., et al. (2023) · mBio · doi:10.1128/mbio.02363-23 · Cited by 19
Conserved role of hnRNPL in alternative splicing of epigenetic modifiers enables B cell activation
Subramani, P. G., Fraszczak, J., Helness, A., Estall, J. L., Möröy, T., & Noia, J. M. D. (2024) · EMBO Reports · doi:10.1038/s44319-024-00152-3 · Cited by 19
Short-Term Effects of Vagus Nerve Stimulation on Learning and Evoked Activity in Auditory Cortex
Lai, J., & David, S. V. (2021) · eNeuro · doi:10.1523/eneuro.0522-20.2021 · Cited by 18
Rodrigues, N. C., Silva‐Cruz, A., Caulino‐Rocha, A., Bento‐Oliveira, A., Ribeiro, J. A., & Cunha‐Reis, D. (2021) · European Journal of Neuroscience · doi:10.1111/ejn.15390 · Cited by 19
Nanopore sequencing reveals endogenous NMD-targeted isoforms in human cellsPreprint
Karousis, E. D., Gypas, F., Zavolan, M., & Mühlemann, O. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.30.442116 · Cited by 18
Absolute copy number fitting from shallow whole genome sequencing dataPreprint
Sauer, C. M., Eldridge, M., Vias, M., Hall, J. A. M., Boyle, S. E., Macintyre, G., Bradley, T. P., Markowetz, F. M., & Brenton, J. D. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.19.452658 · Cited by 17
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Leduc, A., Xu, Y., Shipkovenska, G. G., Dou, Z., & Slavov, N. (2025) · Nature Communications · doi:10.1038/s41467-025-56736-7 · Cited by 18
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Hitch, T. C. A., Bosch, J., Bolsega, S., Deschamps, C., Etienne‐Mesmin, L., Treichel, N. S., Blanquet‐Diot, S., Ocvirk, S., Basic, M., & Clavel, T. (2025) · The ISME Journal · doi:10.1093/ismejo/wraf209 · Cited by 20
Metadata
- Authors
- Christensen, A. C., Lensjø, K. K., Lepperød, M. E., Dragly, S.-A., Sutterud, H., Blackstad, J. S., Fyhn, M., & Hafting, T.
- Affiliations
- University of Oslo (NO)
- Corresponding author
- Hafting, Torkel
- ORCID
- Venue
- Nature Communications
- Published
- 2021-01-11
- Volume
- 12
- Issue
- 1
- Pages
- 253-253
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC7801665
- OpenAlex
- W3120392574
- Cited by
- 1,865
- References
- 76
- Citations by year
- 2026: 132 · 2025: 277 · 2024: 340 · 2023: 431 · 2022: 499 · 2021: 170
- FWCI
- 189.42
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by)
- Funders
- Norges Forskningsråd (217920, 231248)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Spatial Cognition and Navigation
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- perineuronal nets, grid cells, extracellular matrix, inhibitory interneurons, spatial memory, navigation, entorhinal cortex, hippocampus, synaptic stability
- MeSH terms
- Grid Cells, Action Potentials, Animals, Computer Simulation, Hippocampus, Male, Models, Neurological, Neurons, Theta Rhythm, Time Factors, Entorhinal Cortex, Rats, Long-Evans, Rats
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