A structured scaffold underlies activity in the hippocampus
Open accessAbstract
Abstract Place cells are believed to organize memory across space and time, inspiring the idea of the cognitive map. Yet unlike the structured activity in the associated grid and head-direction cells, they remain an enigma: their responses have been difficult to predict and are complex enough to be statistically well-described by a random process. Here we report one step toward the ultimate goal of understanding place cells well enough to predict their fields. Within a theoretical framework in which place fields are derived as a conjunction of external cues with internal grid cell inputs, we predict that even apparently random place cell responses should reflect the structure of their grid inputs and that this structure can be unmasked if probed in sufficiently large neural populations and large environments. To test the theory, we design experiments in long, locally featureless spaces to demonstrate that structured scaffolds undergird place cell responses. Our findings, together with other theoretical and experimental results, suggest that place cells build memories of external inputs by attaching them to a largely prespecified grid scaffold.
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
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?
If grid cell outputs are available, CSCG can use those as additional sensations, which would speed up learning in the middle portions of empty arenas where unique sensations are not available, and help stabilize the place fields away from the boundaries or other landmark cues.
“If grid cell outputs are available, CSCG can use those as additional sensations. This would speed up learning in the middle portions of empty arenas where unique sensations are not available, and it will also help stabilize the place fields away from the boundaries or other landmark cues (56, 57), consistent with the idea of grid cells providing an optional scaffolding for place cells (58).”
From Space is a latent sequence: A theory of the hippocampus (2024) · cites this paper as [58]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat
O'Keefe, J., & Dostrovsky, J. (1971) · Brain Research · doi:10.1016/0006-8993(71)90358-1 · Cited by 6,151 · see citing works
The preregistration revolution
Nosek, B. A., Ebersole, C. R., DeHaven, A. C., & Mellor, D. T. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1708274114 · Cited by 1,998 · see citing works
Taube, J., Muller, R., & Ranck, J. (1990) · The Journal of Neuroscience · doi:10.1523/jneurosci.10-02-00420.1990 · Cited by 1,809 · see citing works
Internally Generated Cell Assembly Sequences in the Rat Hippocampus
Pastalkova, E., Itskov, V., Amarasingham, A., & Buzsáki, G. (2008) · Science · doi:10.1126/science.1159775 · Cited by 1,301 · see citing works
Zhang, K. (1996) · The Journal of Neuroscience · doi:10.1523/jneurosci.16-06-02112.1996 · Cited by 1,001 · see citing works
The hippocampus as a predictive map
Stachenfeld, K. L., Botvinick, M. M., & Gershman, S. J. (2017) · Nature Neuroscience · doi:10.1038/nn.4650 · Cited by 1,000 · see citing works
The log-dynamic brain: how skewed distributions affect network operations
Buzsáki, G., & Mizuseki, K. (2014) · Nature reviews. Neuroscience · doi:10.1038/nrn3687 · Cited by 964 · see citing works
Megı́as, M., Emri, Z., Freund, T. F., & Gulyás, A. I. (2001) · Neuroscience · doi:10.1016/s0306-4522(00)00496-6 · Cited by 950 · see citing works
Functional imaging of hippocampal place cells at cellular resolution during virtual navigation
Dombeck, D. A., Harvey, C. D., Tian, L., Looger, L. L., & Tank, D. W. (2010) · Nature Neuroscience · doi:10.1038/nn.2648 · Cited by 873 · see citing works
Intracellular dynamics of hippocampal place cells during virtual navigation
Harvey, C. D., Collman, F., Dombeck, D. A., & Tank, D. W. (2009) · Nature · doi:10.1038/nature08499 · Cited by 854 · see citing works
Accurate Path Integration in Continuous Attractor Network Models of Grid Cells
Burak, Y., & Fiete, I. R. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000291 · Cited by 779 · see citing works
Self-citationPreplay of future place cell sequences by hippocampal cellular assemblies
Dragoi, G., & Tonegawa, S. (2011) · Nature · doi:10.1038/nature09633 · Cited by 716 · see citing works
Behavioral time scale synaptic plasticity underlies CA1 place fields
Bittner, K. C., Milstein, A. D., Grienberger, C., Romani, S., & Magee, J. C. (2017) · Science · doi:10.1126/science.aan3846 · Cited by 707 · see citing works
The entorhinal grid map is discretized
Stensola, H., Stensola, T., Solstad, T., Frøland, K., Moser, M., & Moser, E. I. (2012) · Nature · doi:10.1038/nature11649 · Cited by 702 · see citing works
Whittington, J. C. R., Muller, T. H., Mark, S., Chen, G., Barry, C., Burgess, N., & Behrens, T. E. J. (2020) · Cell · doi:10.1016/j.cell.2020.10.024 · Cited by 660 · see citing works
Hippocampal “Time Cells”: Time versus Path Integration
Kraus, B. J., Robinson, R. J., White, J. A., Eichenbaum, H., & Hasselmo, M. E. (2013) · Neuron · doi:10.1016/j.neuron.2013.04.015 · Cited by 526 · see citing works
From grid cells to place cells: A mathematical model
Solstad, T., Moser, E. I., & Einevoll, G. T. (2006) · Hippocampus · doi:10.1002/hipo.20244 · Cited by 477 · see citing works
Memory and Brain Systems: 1969–2009
Squire, L. R. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.3575-09.2009 · Cited by 477 · see citing works
Spatial and behavioral correlates of hippocampal neuronal activity
Wiener, S. I., Paul, C., & Eichenbaum, H. (1989) · Journal of Neuroscience · doi:10.1523/jneurosci.09-08-02737.1989 · Cited by 397 · see citing works
A Dedicated Population for Reward Coding in the Hippocampus
Gauthier, J. L., & Tank, D. W. (2018) · Neuron · doi:10.1016/j.neuron.2018.06.008 · Cited by 394 · see citing works
Chaudhuri, R., Gerçek, B., Pandey, B., Peyrache, A., & Fiete, I. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0460-x · Cited by 367 · see citing works
Self-citationDiversity in neural firing dynamics supports both rigid and learned hippocampal sequences
Grosmark, A. D., & Buzsáki, G. (2016) · Science · doi:10.1126/science.aad1935 · Cited by 367 · see citing works
Intracellular Determinants of Hippocampal CA1 Place and Silent Cell Activity in a Novel Environment
Epsztein, J., Brecht, M., & Lee, A. K. (2011) · Neuron · doi:10.1016/j.neuron.2011.03.006 · Cited by 306 · see citing works
Self-citationSpecific evidence of low-dimensional continuous attractor dynamics in grid cells
Yoon, K., Buice, M. A., Barry, C., Hayman, R., Burgess, N., & Fiete, I. R. (2013) · Nature Neuroscience · doi:10.1038/nn.3450 · Cited by 296 · cited 1× · see citing works
“models [15, 16, 17, 18, 19, 20] and thus a good explanation of the circuit mechanisms underlying their function as velocity integrators and analog memories – it is unclear how the hippocampus and place cells encode the cognitive maps that link what to where in memory.”
BackgroundSelf-citationThe emergence of grid cells: Intelligent design or just adaptation?
Kropff, E., & Treves, A. (2008) · Hippocampus · doi:10.1002/hipo.20520 · Cited by 254 · see citing works
Grid cells generate an analog error-correcting code for singularly precise neural computation
Sreenivasan, S., & Fiete, I. (2011) · Nature Neuroscience · doi:10.1038/nn.2901 · Cited by 221 · see citing works
Self-citationWhat is the best index of detectability?
Simpson, A. J., & Fitter, M. J. (1973) · Psychological Bulletin · doi:10.1037/h0035203 · Cited by 212 · see citing works
Fenton, A. A., Kao, H.-Y., Neymotin, S. A., Olypher, A. V., Vayntrub, Y., Lytton, W. W., & Ludvig, N. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.2862-08.2008 · Cited by 208 · see citing works
Diehl, G. W., Hon, O. J., Leutgeb, S., & Leutgeb, J. K. (2017) · Neuron · doi:10.1016/j.neuron.2017.03.004 · Cited by 204 · see citing works
Large environments reveal the statistical structure governing hippocampal representations
Rich, P. D., Liaw, H.-P., & Lee, A. K. (2014) · Science · doi:10.1126/science.1255635 · Cited by 177 · see citing works
Self-citationDordek, Y., Soudry, D., Meir, R., & Derdikman, D. (2016) · eLife · doi:10.7554/elife.10094 · Cited by 175 · see citing works
Place cells are more strongly tied to landmarks in deep than in superficial CA1
Geiller, T., Fattahi, M., Choi, J.-S., & Royer, S. (2017) · Nature Communications · doi:10.1038/ncomms14531 · Cited by 163 · see citing works
Awake hippocampal reactivations project onto orthogonal neuronal assemblies
Malvache, A., Reichinnek, S., Villette, V., Haimerl, C., & Cossart, R. (2016) · Science · doi:10.1126/science.aaf3319 · Cited by 163 · see citing works
Preexisting hippocampal network dynamics constrain optogenetically induced place fields
McKenzie, S., Huszár, R., English, D. F., Kim, K., Christensen, F., Yoon, E., & Buzsáki, G. (2021) · Neuron · doi:10.1016/j.neuron.2021.01.011 · Cited by 150 · see citing works
Cohen, J. D., Bolstad, M., & Lee, A. K. (2017) · eLife · doi:10.7554/elife.23040 · Cited by 144 · see citing works
Self-citationCorrelation structure of grid cells is preserved during sleep
Gardner, R. J., Lu, L., Wernle, T., Moser, M., & Moser, E. I. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0360-0 · Cited by 134 · see citing works
Lee, J. S., Briguglio, J. J., Cohen, J. D., Romani, S., & Lee, A. K. (2020) · Cell · doi:10.1016/j.cell.2020.09.024 · Cited by 132 · see citing works
Self-citationEmergence of preconfigured and plastic time-compressed sequences in early postnatal development
Farooq, U., & Dragoi, G. (2019) · Science · doi:10.1126/science.aav0502 · Cited by 131 · see citing works
Mechanisms Underlying the Neural Computation of Head Direction
Hulse, B. K., & Jayaraman, V. (2020) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-072116-031516 · Cited by 130 · see citing works
Double-ring network model of the head-direction system
Xie, X., Hahnloser, R. H. R., & Seung, H. S. (2002) · Physical Review E · doi:10.1103/physreve.66.041902 · Cited by 124 · see citing works
Trettel, S. G., Trimper, J. B., Hwaun, E., Fiete, I. R., & Colgin, L. L. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0359-6 · Cited by 108 · see citing works
Self-citationSelection of preconfigured cell assemblies for representation of novel spatial experiences
Dragoi, G., & Tonegawa, S. (2013) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2012.0522 · Cited by 94 · see citing works
Grid Cell Responses in 1D Environments Assessed as Slices through a 2D Lattice
Yoon, K., Lewallen, S., Kinkhabwala, A. A., Tank, D. W., & Fiete, I. (2016) · Neuron · doi:10.1016/j.neuron.2016.01.039 · Cited by 81 · see citing works
Self-citationShort-term reverberant memory model of hippocampal field CA3
Wiebe, S. P., St�ubli, U. V., & Ambros-Ingerson, J. (1997) · Hippocampus · doi:10.1002/(sici)1098-1063(1997)7:6 · Cited by 32 · see citing works
What Versus Where: Non-spatial Aspects of Memory Representation by the Hippocampus
Eichenbaum, H. (2016) · Current topics in behavioral neurosciences · doi:10.1007/7854_2016_450 · Cited by 30 · see citing works
Preconfigured patterns are the primary driver of offline multi‐neuronal sequence replay
Liu, K., Sibille, J., & Dragoi, G. (2018) · Hippocampus · doi:10.1002/hipo.23034 · Cited by 30 · see citing works
From grid cells to place cells with realistic field sizes
Neher, T., Azizi, A. H., & Cheng, S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0181618 · Cited by 25 · see citing works
Place-cell capacity and volatility with grid-like inputs
Yim, M. Y., Sadun, L. A., Fiete, I., & Taillefumier, T. O. (2021) · eLife · doi:10.7554/elife.62702 · Cited by 12 · see citing works
Self-citationEpisodic memory, semantic memory, and amnesia
Squire, L. R., & Zola, S. M. (1998) · Hippocampus · doi:10.1002/(sici)1098-1063(1998)8:3
Flagged first, then by citations
Research works printed but not linked: 2
- O'Keefe, J. & Nadel, L. The hippocampus as a cognitive map (Oxford: Clarendon Press, 1978).
- Harland, B., Contreras, M., Souder, M. & Fellous, J.-M. Dorsal CA1 hippocampal place cells form a multi-scale representation of megaspace 31, 2178-2190.e6 (2021). URL https://doi.org/10.1016%2Fj.cub.2021.03.003. · DOI
Cited by
Sorted by citations
Samborska, V., Butler, J. L., Walton, M. E., Behrens, T. E. J., & Akam, T. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01149-8 · Cited by 133 · see citing works
Safron, A., Çatal, O., & Verbelen, T. (2022) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2022.787659 · Cited by 33 · see citing works
Space is a latent sequence: A theory of the hippocampus
Raju, R. V., Guntupalli, J. S., Zhou, G., Wendelken, C., Lázaro-Gredilla, M., & George, D. (2024) · Science Advances · doi:10.1126/sciadv.adm8470 · Cited by 25 · see citing works
The chicken and egg problem of grid cells and place cells
Morris, G., & Derdikman, D. (2022) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2022.11.003 · Cited by 30 · see citing works
Samborska, V., Butler, J. L., Walton, M. E., Behrens, T. E. J., & Akam, T. E. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.05.433967 · Cited by 25 · see citing works
Self-Supervised Grid Cells Without Path IntegrationPreprint
Pettersen, M. B., Schøyen, V. S., Østby, M. D., Malthe‐Sørenssen, A., & Lepperød, M. E. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.30.596577 · Cited by 16 · see citing works
Grid Cell Firing Patterns Maintain their Hexagonal Firing Patterns on a Circular TrackPreprint
Yim, M. Y., Walton, S., & Hedrick, K. R. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.14.557783
Self-citationGrid cell populations maintain their hexagonal firing patterns on a circular trackPreprint
Yim, M. Y., Walton, S., & Hedrick, K. R. (2024) · Research Square · doi:10.21203/rs.3.rs-4468831/v1
Self-citationNymoen, I., Rogge, F. S., Aasen, A. H., Grødem, S., Malthe‐Sørenssen, A., Lepperød, M. E., Hafting, T., Fyhn, M., & Lensjø, K. K. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.20.629630
Nymoen, I., Rogge, F. S., Aasen, A. H., Grødem, S., Malthe‐Sørenssen, A., Lepperød, M. E., Hafting, T., Fyhn, M., & Lensjø, K. K. (2025) · eLife · doi:10.7554/elife.106095
Nymoen, I., Rogge, F. S., Aasen, A. H., Grødem, S., Malthe‐Sørenssen, A., Lepperød, M. E., Hafting, T., Fyhn, M., & Lensjø, K. K. (2025) · eLife · doi:10.7554/elife.106095.1 · Cited by 1 · see citing works
- Authors
- Mulders, D., Yim, M. Y., Lee, J. S., Lee, A. K., Taillefumier, T., & Fiete, I. R.
- Affiliations
- McGovern Institute for Brain Research (US)
- Howard Hughes Medical Institute (US)
- The University of Texas at Austin (US)
- Corresponding author
- Lee, Albert K.
- Taillefumier, Thibaud
- Fiete, Ila R.
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2021-11-22
- OpenAlex
- W3215776587
- Cited by
- 11
- References
- 49
- Citations by year
- 2025: 2 · 2024: 4 · 2023: 1 · 2022: 3 · 2021: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Funders
- Howard Hughes Medical Institute
- Simons Foundation
- Office of Naval Research
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Physical Sciences › Engineering › Automotive Engineering › Spatial Cognition and Navigation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- place cells, place fields, grid cells, hippocampus, cognitive maps, spatial memory, external cues, neural population activity, featureless environments
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
