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Episodic and associative memory from spatial scaffolds in the hippocampus

Nature volume 638, pages 739–751 (2025)Cite this article

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Abstract

Hippocampal circuits in the brain enable two distinct cognitive functions: the construction of spatial maps for navigation, and the storage of sequential episodic memories1,2,3,4,5. Although there have been advances in modelling spatial representations in the hippocampus6,7,8,9,10, we lack good models of its role in episodic memory. Here we present a neocortical–entorhinal–hippocampal network model that implements a high-capacity general associative memory, spatial memory and episodic memory. By factoring content storage from the dynamics of generating error-correcting stable states, the circuit (which we call vector hippocampal scaffolded heteroassociative memory (Vector-HaSH)) avoids the memory cliff of prior memory models11,12, and instead exhibits a graceful trade-off between number of stored items and recall detail. A pre-structured internal scaffold based on grid cell states is essential for constructing even non-spatial episodic memory: it enables high-capacity sequence memorization by abstracting the chaining problem into one of learning low-dimensional transitions. Vector-HaSH reproduces several hippocampal experiments on spatial mapping and context-based representations, and provides a circuit model of the ‘memory palaces’ used by memory athletes13. Thus, this work provides a unified understanding of the spatial mapping and associative and episodic memory roles of the hippocampus.

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Fig. 1: Biological memory: challenges and proposed architecture.
Fig. 2: The scaffold generates exponentially many attractors with equally large basins.
Fig. 3: High-capacity content-addressable item memory via heteroassociation with scaffold.
Fig. 4: Memory, inference and lifelong learning in the spatial domain.
Fig. 5: Sequence scaffold with low-dimensional grid cell shifts enables high-capacity episodic (sequence) memory.
Fig. 6: Vector-HaSH reproduces multiple aspects of entorhinal and hippocampal phenomenology.
Fig. 7: Accurate recall of arbitrary inputs by heteroassociation onto landmarks in a memory palace formed by Vector-HaSH.

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Data availability

Data were collecting by running the codes available at https://github.com/FieteLab.

Code availability

Codes used to run the model and analyse data are available at https://github.com/FieteLab.

References

  1. Scoville, WilliamBeecher & Milner, B. Loss of recent memory after bilateral hippocampal lesions. J. Neurol. Neurosurg. Psychiatry 20, 11 (1957).

    PubMed  PubMed Central  MATH  CAS  Google Scholar 

  2. O’Keefe, J. & Dostrovsky, J. The hippocampus as a spatial map. preliminary evidence from unit activity in the freely-moving rat. Brain Res. 34, 171–175 (1971).

    PubMed  MATH  Google Scholar 

  3. Hafting, T., Fyhn, M., Molden, S., Moser, M.-B. & Moser, E. I. Microstructure of a spatial map in the entorhinal cortex. Nature 436, 801–806 (2005).

    PubMed  MATH  ADS  CAS  Google Scholar 

  4. Solstad, T., Boccara, C. N., Kropff, E., Moser, May-Britt & Moser, E. I. Representation of geometric borders in the entorhinal cortex. Science 322, 1865–1868 (2008).

    PubMed  MATH  ADS  CAS  Google Scholar 

  5. Lever, C., Burton, S., Jeewajee, A., O’Keefe, J. & Burgess, N. Boundary vector cells in the subiculum of the hippocampal formation. J. Neurosci. 29, 9771–9777 (2009).

    PubMed  PubMed Central  MATH  CAS  Google Scholar 

  6. Samsonovich, A. & McNaughton, B. L. Path integration and cognitive mapping in a continuous attractor neural network model. J. Neurosci. 17, 5900–5920 (1997).

    PubMed  PubMed Central  MATH  CAS  Google Scholar 

  7. Hasselmo, M. E. A model of episodic memory: mental time travel along encoded trajectories using grid cells. Neurobiol. Learn. Mem. 92, 559–573 (2009).

    PubMed  PubMed Central  MATH  Google Scholar 

  8. Burak, Y. & Fiete, I. R. Accurate path integration in continuous attractor network models of grid cells. PLoS Comput. Biol. 5, e1000291 (2009).

    MathSciNet  PubMed  PubMed Central  MATH  ADS  Google Scholar 

  9. Stachenfeld, K. L., Botvinick, M. M. & Gershman, S. J. The hippocampus as a predictive map. Nat. Neurosci. 20, 1643–1653 (2017).

    PubMed  MATH  CAS  Google Scholar 

  10. Agmon, H. & Burak, Y. A theory of joint attractor dynamics in the hippocampus and the entorhinal cortex accounts for artificial remapping and grid cell field-to-field variability. eLife 9, e56894 (2020).

    PubMed  PubMed Central  MATH  CAS  Google Scholar 

  11. Abu-Mostafa, Y. S. & St Jacques, J. Information capacity of the hopfield model. IEEE Trans. Inf. Theory 31, 461–464 (1985).

    MATH  Google Scholar 

  12. Gardner, E. The space of interactions in neural network models. J. Phys. A 21, 257–270 (1988).

    MathSciNet  MATH  ADS  Google Scholar 

  13. Yates, F. A. The Art of Memory (Routledge & Kegan Paul, 1966).

  14. Proust, M. À la Recherche du Temps Perdu (Grasset, 1913).

  15. Reed, J. M. & Squire, L. R. Impaired recognition memory in patients with lesions limited to the hippocampal formation. Behav. Neurosci. 111, 667 (1997).

    PubMed  MATH  CAS  Google Scholar 

  16. Zola, S. M. et al. Impaired recognition memory in monkeys after damage limited to the hippocampal region. J. Neurosci. 20, 451–463 (2000).

    PubMed  PubMed Central  MATH  CAS  Google Scholar 

  17. Manns, J. R., Hopkins, R. O., Reed, J. M., Kitchener, E. G. & Squire, L. R. Recognition memory and the human hippocampus. Neuron 37, 171–180 (2003).

    PubMed  CAS  Google Scholar 

  18. Eichenbaum, H. On the integration of space, time, and memory. Neuron 95, 1007–1018 (2017).

    PubMed  PubMed Central  MATH  CAS  Google Scholar 

  19. Taube, J. S., Muller, R. U. & Ranck, J. B. Head-direction cells recorded from the postsubiculum in freely moving rats. i. description and quantitative analysis. J. Neurosci. 10, 420–435 (1990).

    PubMed  PubMed Central  MATH  CAS  Google Scholar 

  20. Lee, A. K. & Wilson, M. A. Memory of sequential experience in the hippocampus during slow wave sleep. Neuron 36, 1183–1194 (2002).

    PubMed  MATH  CAS  Google Scholar 

  21. Fenton, AndréA. et al. Unmasking the CA1 ensemble place code by exposures to small and large environments: more place cells and multiple, irregularly arranged, and expanded place fields in the larger space. J. Neurosci. 28, 11250–11262 (2008).

    PubMed  PubMed Central  MATH  CAS  Google Scholar 

  22. Colgin, LauraLee et al. Frequency of gamma oscillations routes flow of information in the hippocampus. Nature 462, 353–357 (2009).

    PubMed  MATH  ADS  CAS  Google Scholar 

  23. Stensola, H. et al. The entorhinal grid map is discretized. Nature 492, 72–78 (2012).

    PubMed  ADS  CAS  Google Scholar 

  24. Buzsáki, György & Moser, E. I. Memory, navigation and theta rhythm in the hippocampal–entorhinal system. Nat. Neurosci. 16, 130–138 (2013).

    PubMed  PubMed Central  MATH  Google Scholar 

  25. Hopfield, J. J. Neurons with graded response have collective computational properties like those of two-state neurons. Proc. Natl Acad. Sci. USA 81, 3088–3092 (1984).

    PubMed  PubMed Central  MATH  ADS  CAS  Google Scholar 

  26. Marr, D., Willshaw, D. & McNaughton, B. Simple Memory: A Theory for Archicortex (Springer, 1991).

  27. Skaggs, W., Knierim, J., Kudrimoti, H. & McNaughton, B. A model of the neural basis of the rat’s sense of direction. Adv. Neural Inf. Process. Syst. 7, 173–180 (1995).

    PubMed  MATH  CAS  Google Scholar 

  28. Burgess, N., Recce, M. & O’Keefe, J. A model of hippocampal function. Neural Netw. 7, 1065–1081 (1994).

    MATH  Google Scholar 

  29. McClelland, J. L., McNaughton, B. L. & O’Reilly, R. C. Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Psychol. Rev. 102, 419 (1995).

    PubMed  MATH  Google Scholar 

  30. Brun, V. H. et al. Place cells and place recognition maintained by direct entorhinal–hippocampal circuitry. Science 296, 2243–2246 (2002).

    PubMed  MATH  ADS  CAS  Google Scholar 

  31. Hartley, T., Burgess, N., Lever, C., Cacucci, F. & O’keefe, J. Modeling place fields in terms of the cortical inputs to the hippocampus. Hippocampus 10, 369–379 (2000).

    PubMed  MATH  CAS  Google Scholar 

  32. Sreenivasan, S. & Fiete, I. Grid cells generate an analog error-correcting code for singularly precise neural computation. Nat. Neurosci. 14, 1330–1337 (2011).

    PubMed  MATH  CAS  Google Scholar 

  33. Yoon, Ki. Jung et al. Specific evidence of low-dimensional continuous attractor dynamics in grid cells. Nat. Neurosci. 16, 1077–1084 (2013).

    PubMed  PubMed Central  MATH  CAS  Google Scholar 

  34. Yoon, K., Lewallen, S., Kinkhabwala, A. A., Tank, D. W. & Fiete, I. R. Grid cell responses in 1D environments assessed as slices through a 2D lattice. Neuron 89, 1086–1099 (2016).

    PubMed  PubMed Central  CAS  Google Scholar 

  35. Solstad, T., Moser, E. I. & Einevoll, G. T. From grid cells to place cells: a mathematical model. Hippocampus 16, 1026–1031 (2006).

    PubMed  MATH  Google Scholar 

  36. Trettel, S. G., Trimper, J. B., Hwaun, E., Fiete, I. R. & Colgin, L. L. Grid cell co-activity patterns during sleep reflect spatial overlap of grid fields during active behaviors. Nat. Neurosci. 22, 609–617 (2019).

    PubMed  PubMed Central  CAS  Google Scholar 

  37. Gardner, R. J. et al. Toroidal topology of population activity in grid cells. Nature 602, 123–128 (2022).

    PubMed  PubMed Central  MATH  ADS  CAS  Google Scholar 

  38. Gardner, R. J., Lu, L., Wernle, T., Moser, May-Britt & Moser, E. I. Correlation structure of grid cells is preserved during sleep. Nat. Neurosci. 22, 598–608 (2019).

    PubMed  MATH  CAS  Google Scholar 

  39. O’Reilly, R. C., Bhattacharyya, R., Howard, M. D. & Ketz, N. Complementary learning systems. Cogn. Sci. 38, 1229–1248 (2014).

    PubMed  Google Scholar 

  40. Hardcastle, K., Ganguli, S. & Giocomo, L. M. Environmental boundaries as an error correction mechanism for grid cells. Neuron 86, 827–839 (2015).

    PubMed  CAS  Google Scholar 

  41. Dordek, Y., Soudry, D., Meir, R. & Derdikman, D. Extracting grid cell characteristics from place cell inputs using non-negative principal component analysis. eLife 5, e10094 (2016).

    PubMed  PubMed Central  Google Scholar 

  42. Keinath, A. T., Epstein, R. A. & Balasubramanian, V. Environmental deformations dynamically shift the grid cell spatial metric. eLife 7, e38169 (2018).

    PubMed  PubMed Central  Google Scholar 

  43. Ocko, S. A., Hardcastle, K., Giocomo, L. M. & Ganguli, S. Emergent elasticity in the neural code for space. Proc. Natl Acad. Sci. USA 115, E11798–E11806 (2018).

    PubMed  PubMed Central  ADS  CAS  Google Scholar 

  44. Chaudhuri, R. & Fiete, I. Bipartite expander hopfield networks as self-decoding high-capacity error correcting codes. Adv. Neural Inf. Process. Syst. 32, 4175 (2019).

    MATH  Google Scholar 

  45. Whittington, James C. R. et al. The Tolman–Eichenbaum machine: unifying space and relational memory through generalization in the hippocampal formation. Cell 183, 1249–1263 (2020).

    PubMed  PubMed Central  MATH  CAS  Google Scholar 

  46. Buzsáki, György & Tingley, D. Space and time: the hippocampus as a sequence generator. Trends Cogn. Sci. 22, 853–869 (2018).

    PubMed  PubMed Central  MATH  Google Scholar 

  47. Aronov, D., Nevers, R. & Tank, D. W. Mapping of a non-spatial dimension by the hippocampal–entorhinal circuit. Nature 543, 719–722 (2017).

    PubMed  PubMed Central  ADS  CAS  Google Scholar 

  48. Killian, N. J., Potter, S. M. & Buffalo, E. A. Saccade direction encoding in the primate entorhinal cortex during visual exploration. Proc. Natl Acad. Sci. USA 112, 15743–15748 (2015).

    PubMed  PubMed Central  MATH  ADS  CAS  Google Scholar 

  49. Constantinescu, A. O., O’Reilly, J. X. & Behrens, T. E. J. Organizing conceptual knowledge in humans with a gridlike code. Science 352, 1464–1468 (2016).

    PubMed  PubMed Central  ADS  CAS  Google Scholar 

  50. Neupane, S., Fiete, L. & Jazayeri, M. Mental navigation in the primate entorhinal cortex. Nature 630, 704–711 (2024).

    PubMed  PubMed Central  ADS  CAS  Google Scholar 

  51. Krotov, D. and Hopfield, J. Large associative memory problem in neurobiology and machine learning. Preprint at https://doi.org/10.48550/arXiv.2008.06996 (2020).

  52. Witter, M. P., Doan, T. P., Jacobsen, B., Nilssen, E. S. & Ohara, S. Architecture of the entorhinal cortex a review of entorhinal anatomy in rodents with some comparative notes. Front. Syst. Neurosci. 11, 46 (2017).

    PubMed  PubMed Central  Google Scholar 

  53. Fiete, I. R., Burak, Y. & Brookings, T. What grid cells convey about rat location. J. Neurosci. 28, 6858–6871 (2008).

    PubMed  PubMed Central  CAS  Google Scholar 

  54. Sharma, S., Chandra, S. & Fiete, I. Content addressable memory without catastrophic forgetting by heteroassociation with a fixed scaffold. In 39th International Conference on Machine Learning 19658–19682 (PMLR, 2022).

  55. Radhakrishnan, A., Belkin, M. & Uhler, C. Overparameterized neural networks implement associative memory. Proc. Natl Acad. Sci. USA 117, 27162–27170 (2020).

    MathSciNet  PubMed  PubMed Central  MATH  ADS  CAS  Google Scholar 

  56. Kleinfeld, D. & Sompolinsky, H. Associative neural network model for the generation of temporal patterns. theory and application to central pattern generators. Biophys. J. 54, 1039–1051 (1988).

    PubMed  PubMed Central  MATH  CAS  Google Scholar 

  57. Fyhn, M., Hafting, T., Treves, A., Moser, May-Britt & Moser, E. I. Hippocampal remapping and grid realignment in entorhinal cortex. Nature 446, 190–194 (2007).

    PubMed  MATH  ADS  CAS  Google Scholar 

  58. Huszár, R., Zhang, Y., Blockus, H. & Buzsáki, György Preconfigured dynamics in the hippocampus are guided by embryonic birthdate and rate of neurogenesis. Nat. Neurosci. 25, 1201–1212 (2022).

    PubMed  PubMed Central  MATH  Google Scholar 

  59. Bonnevie, T. et al. Grid cells require excitatory drive from the hippocampus. Nat. Neurosci. 16, 309–317 (2013).

    PubMed  MATH  CAS  Google Scholar 

  60. Almog, N. et al. During hippocampal inactivation, grid cells maintain synchrony, even when the grid pattern is lost. eLife 8, e47147 (2019).

    PubMed  PubMed Central  MATH  Google Scholar 

  61. Hales, J. B. et al. Medial entorhinal cortex lesions only partially disrupt hippocampal place cells and hippocampus-dependent place memory. Cell Rep. 9, 893–901 (2014).

    PubMed  PubMed Central  MATH  CAS  Google Scholar 

  62. Wood, E. R., Dudchenko, P. A., Robitsek, R. J. & Eichenbaum, H. Hippocampal neurons encode information about different types of memory episodes occurring in the same location. Neuron 27, 623–633 (2000).

    PubMed  CAS  Google Scholar 

  63. Grieves, R. M., Wood, E. R. & Dudchenko, P. A. Place cells on a maze encode routes rather than destinations. eLife 5, e15986 (2016).

    PubMed  PubMed Central  Google Scholar 

  64. Dombeck, D. A., Harvey, C. D., Tian, L., Looger, L. L. & Tank, D. W. Functional imaging of hippocampal place cells at cellular resolution during virtual navigation. Nat. Neurosci. 13, 1433–1440 (2010).

    PubMed  PubMed Central  CAS  Google Scholar 

  65. Nadel, L. & Moscovitch, M. Memory consolidation, retrograde amnesia and the hippocampal complex. Curr. Opin. Neurobiol. 7, 217–227 (1997).

    PubMed  MATH  CAS  Google Scholar 

  66. Yadav, N., Toader, A. & Rajasethupathy, P. Thalamic and prefrontal contributions to an evolving memory. Neuron 112, 1045–1059 (2024).

    PubMed  CAS  Google Scholar 

  67. Tsoi, SauYee et al. Telencephalic outputs from the medial entorhinal cortex are copied directly to the hippocampus. eLife 11, e73162 (2022).

    PubMed  PubMed Central  CAS  Google Scholar 

  68. Donato, F., Jacobsen, R. I., Moser, May-Britt & Moser, E. I. Stellate cells drive maturation of the entorhinal–hippocampal circuit. Science 355, eaai8178 (2017).

    PubMed  MATH  Google Scholar 

  69. Benna, M. K. & Fusi, S. Place cells may simply be memory cells: Memory compression leads to spatial tuning and history dependence. Proc. Natl Acad. Sci. USA 118, e2018422118 (2021).

    PubMed  PubMed Central  MATH  CAS  Google Scholar 

  70. Teyler, T. J. & Rudy, J. W. The hippocampal indexing theory and episodic memory: updating the index. Hippocampus 17, 1158–1169 (2007).

    PubMed  MATH  Google Scholar 

  71. Treves, A. & Rolls, E. T. Computational analysis of the role of the hippocampus in memory. Hippocampus 4, 374–391 (1994).

    PubMed  MATH  CAS  Google Scholar 

  72. Alme, C. B. et al. Place cells in the hippocampus: eleven maps for eleven rooms. Proc. Natl Acad. Sci. USA 111, 18428–18435 (2014).

    PubMed  PubMed Central  MATH  ADS  CAS  Google Scholar 

  73. Yim, ManYi, Sadun, L. A., Fiete, I. R. & Taillefumier, T. Place-cell capacity and volatility with grid-like inputs. eLife 10, e62702 (2021).

    PubMed  PubMed Central  CAS  Google Scholar 

  74. Tapson, J. & van Schaik, A. Learning the pseudoinverse solution to network weights. Neural Netw. 45, 94–100 (2013).

    PubMed  MATH  CAS  Google Scholar 

  75. O’Reilly, R. C. Six principles for biologically based computational models of cortical cognition. Trends Cogn. Sci. 2, 455–462 (1998).

    PubMed  MATH  Google Scholar 

  76. Personnaz, L., Guyon, I. & Dreyfus, G. Information storage and retrieval in spin-glass like neural networks. J. Physique Lettres 46, 359–365 (1985).

    MATH  Google Scholar 

  77. Personnaz, L., Guyon, I. & Dreyfus, G. Collective computational properties of neural networks: new learning mechanisms. Phys. Rev. A 34, 4217 (1986).

    MathSciNet  MATH  ADS  CAS  Google Scholar 

  78. Parisi, G. A memory which forgets. J. Phys. A 19, L617 (1986).

    MathSciNet  MATH  ADS  Google Scholar 

  79. Fusi, S. & Abbott, L. F. Limits on the memory storage capacity of bounded synapses. Nat. Neurosci. 10, 485–493 (2007).

    PubMed  MATH  CAS  Google Scholar 

  80. Tsodyks, M. V. & Feigel’man, M. V. The enhanced storage capacity in neural networks with low activity level. Europhysics Lett. 6, 101 (1988).

    MATH  ADS  Google Scholar 

  81. Dominguez, D., Koroutchev, K., Serrano, E. & Rodríguez, F. B. Information and topology in attractor neural networks. Neural Comput. 19, 956–973 (2007).

    MathSciNet  PubMed  MATH  CAS  Google Scholar 

  82. Markus, E. J. et al. Interactions between location and task affect the spatial and directional firing of hippocampal neurons. J. Neurosci. 15, 7079–7094 (1995).

    PubMed  PubMed Central  MATH  CAS  Google Scholar 

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Acknowledgements

This work was supported by ONR award N00014-19-1-2584, by NSF-CISE award IIS-2151077 under the Robust Intelligence programme, by the ARO-MURI award W911NF-23-1-0277, by the Simons Foundation SCGB programme 1181110, and the K. Lisa Yang ICoN Center.

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Author notes

  1. These authors contributed equally: Sarthak Chandra, Sugandha Sharma

Authors and Affiliations

  1. Department of Brain and Cognitive Sciences and McGovern Institute, MIT, Cambridge, MA, USA

    Sarthak Chandra, Sugandha Sharma & Ila Fiete

  2. Center for Neuroscience, Department of Neurobiology, Physiology and Behavior, University of California Davis, Davis, CA, USA

    Rishidev Chaudhuri

  3. Department of Mathematics, University of California Davis, Davis, CA, USA

    Rishidev Chaudhuri

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  1. Sarthak Chandra
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Contributions

Conceptualization: S.C., S.S., R.C. and I.F. Funding acquisition: I.F. Writing: S.C., S.S. and I.F. Coding and analysis: S.C. and S.S.

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Correspondence to Ila Fiete.

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Extended data figures and tables

Extended Data Fig. 1 Critical number of hippocampal cells necessary to support all scaffold fixed points is asymptotically independent of the number of grid cells.

For a given number of modules, the critical number of hippocampal cells, \({N}_{h}^{* }\) increases slowly with the number of grid cells, but then asymptotically approaches a constant, as expected from the theoretical results in Sec. C.1.

Extended Data Fig. 2 Learning generalization approaches theoretical expectations with increasing Nh.

The number of generated fixed points approaches the maximal scaffold capacity for a very small number of learned patterns (see also Fig. 2f). As the number of hippocampal cells increases, the number of learning patterns necessary for complete generalization approaches the theoretical expectation of M × Kmax, as proved in SI Sec. C.4.

Extended Data Fig. 3 Hebbian learning between sensory layer and scaffold also produces memory continuum.

A memory continuum is obtained in Vector-HaSH even if the weights between the sensory and hippocampal layers are bi-directionally trained using Hebbian learning (instead of pseudoinverse learning, as in Fig. 3. This continuum is also asymptotically proportional to the theoretical bound on memory capacity (forest green dashed line indicative of slope of theoretical upper bound, vertical and horizontal position of dashed line is arbitrary). However, the proportionality constant is lower, with the gradual degradation of information recall occurring well before Nh. Vector-HaSH parameters identical to Fig. 3c with λ = {3, 4, 5}.

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Chandra, S., Sharma, S., Chaudhuri, R. et al. Episodic and associative memory from spatial scaffolds in the hippocampus. Nature 638, 739–751 (2025). https://doi.org/10.1038/s41586-024-08392-y

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