Abstract
Learning and interpreting the structure of the environment is an innate feature of biological systems, and is integral to guiding flexible behaviors for evolutionary viability. The concept of a cognitive map has emerged as one of the leading metaphors for these capacities, and unraveling the learning and neural representation of such a map has become a central focus of neuroscience. In recent years, many models have been developed to explain cellular responses in the hippocampus and other brain areas. Because it can be difficult to see how these models differ, how they relate and what each model can contribute, this Review aims to organize these models into a clear ontology. This ontology reveals parallels between existing empirical results, and implies new approaches to understand hippocampal–cortical interactions and beyond.
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Data availability
No data were generated in this Review.
Code availability
Python and TensorFlow code are available at https://github.com/djcrw/generalising-structural-knowledge.
References
Scoville, W. B. & Milner, B. Loss of recent memory after bilateral hippocampal lesions. J. Neurol. Neurosurg. Psychiatry 20, 11–21 (1957).
Cohen, N. J. & Squire, L. R. Preserved learning and retention of pattern-analyzing skill in amnesia: dissociation of knowing how and knowing that. Science 210, 207–210 (1980).
O’Keefe, J. & Nadel, L. The Hippocampus as a Cognitive Map (Oxford Univ. Press, 1978).
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).
Hassabis, D., Kumaran, D., Vann, S. D. & Maguire, E. A. Patients with hippocampal amnesia cannot imagine new experiences. Proc. Natl Acad. Sci. USA 104, 1726–1731 (2007).
Tolman, E. C. Cognitive maps in rats and men. Psychol. Rev. 55, 189–208 (1948).
Turner, C. H. The homing of ants: an experimental study of ant behavior. J. Comp. Neurol. Psychol. 17, 367–434 (1907).
Zanforlin, M. & Poli, G. The burrowing rat: a new technique to study place learning and orientation. Acti. Memorie 82, 653–670 (1970).
Behrens, T. E. J. et al. What is a cognitive map? Organizing knowledge for flexible behavior. Neuron 100, 490–509 (2018).
Tenenbaum, J. B., Kemp, C., Griffiths, T. L. & Goodman, N. D. How to grow a mind: statistics, structure and abstraction. Science 331, 1279–1285 (2011).
Bartlett, F. C. & Burt, C. Remembering: a study in experimental and social psychology. Br. J. Educ. Psychol. 3, 187–192 (1932).
Harlow, H. F. The formation of learning sets. Psychological Rev. 56, 51–65 (1949).
Moser, E. I., Moser, M.-B. & McNaughton, B. L. Spatial representation in the hippocampal formation: a history. Nat. Neurosci. 20, 1448–1464 (2017).
Aronov, D., Nevers, R. & Tank, D. W. Mapping of a non-spatial dimension by the hippocampal–entorhinal circuit. Nature 543, 719–722 (2017).
Knudsen, E. B. & Wallis, J. D. Hippocampal neurons construct a map of an abstract value space. Cell 184, 4640–4650 (2021).
Nieh, E. H. et al. Geometry of abstract learned knowledge in the hippocampus. Nature https://doi.org/10.1038/s41586-021-03652-7 (2021).
Doeller, C. F., Barry, C. & Burgess, N. Evidence for grid cells in a human memory network. Nature 463, 657–661 (2010).
Constantinescu, A. O. et al. Organizing conceptual knowledge in humans with a gridlike code. Science 352, 1464–1468 (2016).
Bao, X. et al. Grid-like neural representations support olfactory navigation of a two-dimensional odor space. Neuron 102, 1066–1075 (2019).
Park, S. A., Miller, D. S., Nili, H., Ranganath, C. & Boorman, E. D. Map making: constructing, combining and inferring on abstract cognitive maps. Neuron 107, 1226–1238 (2020).
Bongioanni, A. et al. Activation and disruption of a neural mechanism for novel choice in monkeys. Nature 591, 270–274 (2021).
Rueckemann, J. W., Sosa, M., Giocomo, L. M. & Buffalo, E. A. The grid code for ordered experience. Nat. Rev. Neurosci. 22, 637–649 (2021).
Radulescu, A., Shin, Y. S. & Niv, Y. Human representation learning. Annu. Rev. Neurosci. 44, 253–273 (2021).
Sanders, H., Wilson, M. A. & Gershman, S. J. Hippocampal remapping as hidden state inference. eLife 9, e51140 (2020).
Stoianov, I., Maisto, D. & Pezzulo, G. The hippocampal formation as a hierarchical generative model supporting generative replay and continual learning. Prog. Neurobiol. 217, 102329 (2022).
Niv, Y. Learning task-state representations. Nat. Neurosci. 22, 1544–1553 (2019).
Sutton, R. S. & Barto, A. G. Reinforcement learning: an introduction. in IEEE Transactions on Neural Networks https://doi.org/10.1109/TNN.1998.712192 (2017).
Bellman, R. A Markovian decision process. J. Math. Mech. 6, 679–684 (1957).
Gershman, S. J. & Niv, Y. Learning latent structure: carving nature at its joints. Curr. Opin. Neurobiol. 20, 251–256 (2010).
Wilson, R. C., Takahashi, Y. K., Schoenbaum, G. & Niv, Y. Orbitofrontal cortex as a cognitive map of task space. Neuron 81, 267–278 (2014).
Watkins, C. J. C. H. & Dayan, P. Technical note: Q-learning. Mach. Learn. 8, 279–292 (1992).
Tolman, E. C., Ritchie, B. F. & Kalish, D. Studies in spatial learning. I. Orientation and the short-cut. J. Exp. Psychol. 36, 13–24 (1946).
Bush, D., Barry, C., Manson, D. & Burgess, N. Using grid cells for navigation. Neuron 87, 507–520 (2015).
Stemmler, M., Mathis, A. & Herz, A. V. M. Connecting multiple spatial scales to decode the population activity of grid cells. Sci. Adv. 1, e1500816 (2015).
Foster, D. J., Morris, R. G. M. & Dayan, P. A model of hippocampally dependent navigation, using the temporal difference learning rule. Hippocampus 10, 1–16 (2000).
Gustafson, N. J. & Daw, N. D. Grid cells, place cells and geodesic generalization for spatial reinforcement learning. PLoS Comput. Biol. 7, e1002235 (2011).
Stachenfeld, K. L., Botvinick, M. M. & Gershman, S. J. The hippocampus as a predictive map. Nat. Neurosci. 20, 1643–1653 (2017).
Piray, P. & Daw, N. D. A model for learning based on the joint estimation of stochasticity and volatility. Nat. Commun. 12, 6587 (2021).
Dusek, J. A. & Eichenbaum, H. The hippocampus and memory for orderly stimulus relations. Proc. Natl Acad. Sci. USA 94, 7109–7114 (1997).
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).
Frank, L. M., Brown, E. N. & Wilson, M. Trajectory encoding in the hippocampus and entorhinal cortex. Neuron 27, 169–178 (2000).
Komorowski, R. W., Manns, J. R. & Eichenbaum, H. Robust conjunctive item-place coding by hippocampal neurons parallels learning what happens where. J. Neurosci. 29, 9918–9929 (2009).
Carpenter, F., Manson, D., Jeffery, K., Burgess, N. & Barry, C. Grid cells form a global representation of connected environments. Curr. Biol. 25, 1176–1182 (2015).
Grieves, R. M., Wood, E. R. & Dudchenko, P. A. Place cells on a maze encode routes rather than destinations. eLife 5, 1–24 (2016).
Sun, C., Yang, W., Martin, J. & Tonegawa, S. Hippocampal neurons represent events as transferable units of experience. Nat. Neurosci. 23, 651–663 (2020).
Taube, J., Muller, R. & Ranck, J. Head-direction cells recorded from the postsubiculum in freely moving rats. I. Description and quantitative analysis. J. Neurosci. 10, 420–435 (1990).
Darwin, C. Origin of certain instincts. Nature 7, 417–418 (1873).
Mittelstaedt, M. L. & Mittelstaedt, H. Homing by path integration in a mammal. Naturwissenschaften 67, 566–567 (1980).
Etienne, A. S. & Jeffery, K. J. Path integration in mammals. Hippocampus 14, 180–192 (2004).
Loomis, J. M. et al. Nonvisual navigation by blind and sighted: assessment of path integration ability. J. Exp. Psychol. 122, 73–91 (1993).
Maaswinkel, H., Jarrard, L. E. & Whishaw, I. Q. Hippocampectomized rats are impaired in homing by path integration. Hippocampus 9, 553–561 (1999).
Sreenivasan, S. & Fiete, I. Grid cells generate an analog error-correcting code for singularly precise neural computation. Nat. Neurosci. 14, 1330–1337 (2011).
Mathis, A., Herz, A. V. M. & Stemmler, M. Optimal population codes for space: grid cells outperform place cells. Neural Comput. 24, 2280–2317 (2012).
Chen, G., Lu, Y., King, J. A., Cacucci, F. & Burgess, N. Differential influences of environment and self-motion on place and grid cell firing. Nat. Commun. 10, 630 (2019).
Anderson, M. I. & Jeffery, K. J. Heterogeneous modulation of place cell firing by changes in context. J. Neurosci. 23, 8827–8835 (2003).
Bostock, E., Muller, R. U. & Kubie, J. L. Experience-dependent modifications of hippocampal place cell firing. Hippocampus 1, 193–205 (1991).
Muller, R. U. & Kubie, J. L. The effects of changes in the environment on the spatial firing of hippocampal complex-spike cells. J. Neurosci. 7, 1951–1968 (1987).
Fyhn, M., Hafting, T., Treves, A., Moser, M. B. & Moser, E. I. Hippocampal remapping and grid realignment in entorhinal cortex. Nature 446, 190–194 (2007).
Yoon, K. et al. Specific evidence of low-dimensional continuous attractor dynamics in grid cells. Nat. Neurosci. 16, 1077–1084 (2013).
Manns, J. R. & Eichenbaum, H. Evolution of declarative memory. Hippocampus 16, 795–808 (2006).
Whittington, J. C. R. et al. The Tolman–Eichenbaum machine: unifying space and relational memory through generalization in the hippocampal formation. Cell 183, 1249–1263 (2020).
Mark, S., Moran, R., Parr, T., Kennerley, S. W. & Behrens, T. E. J. Transferring structural knowledge across cognitive maps in humans and models. Nat. Commun. 11, 4783 (2020).
Kemp, C. & Tenenbaum, J. B. The discovery of structural form. Proc. Natl Acad. Sci. USA 105, 10687–10692 (2008).
Høydal, Ø. A., Skytøen, E. R., Andersson, S. O., Moser, M. -B. & Moser, E. I. Object-vector coding in the medial entorhinal cortex. Nature 568, 400–404 (2019).
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).
Becker, S. & Burgess, N. Modelling spatial recall, mental imagery and neglect. Adv. Neural Inf. Process. Syst. 13, 96–102 (2001).
Barry, C. et al. The boundary vector cell model of place cell firing and spatial memory. Rev. Neurosci. 17, 71–97 (2006).
Solstad, T., Boccara, C. N., Kropff, E., Moser, M.-B. & Moser, E. I. Representation of geometric borders in the entorhinal cortex. Science 322, 1865–1868 (2008).
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).
Gauthier, J. L. & Tank, D. W. A dedicated population for reward coding in the hippocampus. Neuron 99, 179–193 (2018).
Sarel, A., Finkelstein, A., Las, L. & Ulanovsky, N. Vectorial representation of spatial goals in the hippocampus of bats. Science 355, 176–180 (2017).
Grieves, R. M. & Jeffery, K. J. The representation of space in the brain. Behav. Processes 135, 113–131 (2017).
Eichenbaum, H. Time cells in the hippocampus: a new dimension for mapping memories. Nat. Rev. Neurosci. 15, 732–744 (2014).
George, D. et al. Clone-structured graph representations enable flexible learning and vicarious evaluation of cognitive maps. Nat. Commun. 12, 2392 (2021).
Uria, B. et al. The spatial memory pipeline: a model of egocentric to allocentric understanding in mammalian brains. Preprint at bioRxiv https://doi.org/10.1101/2020.11.11.378141 (2020).
Botvinick, M. & Toussaint, M. Planning as inference. Trends Cogn. Sci. 16, 485–488 (2012).
Friston, K. The free-energy principle: a rough guide to the brain? Trends Cogn. Sci. 13, 293–301 (2009).
Banino, A. et al. Vector-based navigation using grid-like representations in artificial agents. Nature 557, 429–433 (2018).
Dordek, Y., Soudry, D., Meir, R. & Derdikman, D. Extracting grid cell characteristics from place cell inputs using non-negative principal component analysis. eLife 5, 1–36 (2016).
McNamee, D. C., Stachenfeld, K. L., Botvinick, M. M. & Gershman, S. J. Flexible modulation of sequence generation in the entorhinal–hippocampal system. Nat. Neuro. https://doi.org/10.1038/s41593-021-00831-7 (2021).
Pfeiffer, B. E. & Foster, D. J. Autoassociative dynamics in the generation of sequences of hippocampal place cells. Science 349, 180–183 (2015).
Baram, A. B., Muller, T. H., Whittington, J. C. R. & Behrens, T. E. J. Intuitive planning: global navigation through cognitive maps based on grid-like codes. Preprint at bioRxiv https://doi.org/10.1101/421461 (2018).
Yu, C., Behrens, T. E. J. & Burgess, N. Prediction and generalisation over directed actions by grid cells. International Conference on Learning Representations (2021).
O’Keefe, J. & Recce, M. L. Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus 3, 317–330 (1993).
Burgess, N., Barry, C. & O’Keefe, J. An oscillatory interference model of grid cell firing. Hippocampus 17, 801–812 (2009).
Burak, Y. & Fiete, I. Do we understand the emergent dynamics of grid cell activity? J. Neurosci. 26, 9352–9354 (2006).
Wang, J. X. et al. Prefrontal cortex as a meta-reinforcement learning system. Nat. Neurosci. 21, 860–868 (2018).
Chen, L. et al. Decision transformer: reinforcement learning via sequence modeling. Preprint at https://arxiv.org/abs/2106.01345 (2021).
Janner, M., Li, Q. & Levine, S. Offline reinforcement learning as one big sequence modeling problem. Preprint at https://arxiv.org/abs/2106.02039 (2021).
Foster, D. J. & Wilson, M. A. Reverse replay of behavioural sequences in hippocampal place cells during the awake state. Nature 440, 680–683 (2006).
Deshmukh, S. S. & Knierim, J. J. Influence of local objects on hippocampal representations: landmark vectors and memory. Hippocampus 23, 253–267 (2013).
Evans, T. & Burgess, N. Coordinated hippocampal-entorhinal replay as structural inference. Adv. Neural Inf. Process. Syst. 32, 1731–1743 (2019).
Mattar, M. G. & Daw, N. D. Prioritized memory access explains planning and hippocampal replay. Nat. Neurosci. 21, 1609–1617 (2018).
Momennejad, I. et al. The successor representation in human reinforcement learning. Nat. Hum. Behav. 1, 680–692 (2017).
Ólafsdóttir, H. F., Carpenter, F. & Barry, C. Coordinated grid and place cell replay during rest. Nat. Neurosci. 19, 792–794 (2016).
Kaefer, K., Nardin, M., Blahna, K. & Csicsvari, J. Replay of behavioral sequences in the medial prefrontal cortex during rule switching. Neuron 106, 154–165 (2020).
Boccara, C. N., Nardin, M., Stella, F., O’Neill, J. & Csicsvari, J. The entorhinal cognitive map is attracted to goals. Science 363, 1443–1447 (2019).
Butler, W. N., Hardcastle, K. & Giocomo, L. M. Remembered reward locations restructure entorhinal spatial maps. Science 363, 1447–1452 (2019).
Ziv, Y. et al. Long-term dynamics of CA1 hippocampal place codes. Nat. Neurosci. 16, 264–266 (2013).
Driscoll, L. N., Pettit, N. L., Minderer, M., Chettih, S. N. & Harvey, C. D. Dynamic reorganization of neuronal activity patterns in parietal cortex. Cell 170, 986–999 (2017).
Rule, M. E., O’Leary, T. & Harvey, C. D. Causes and consequences of representational drift. Curr. Opin. Neurobiol. 58, 141–147 (2019).
Rubin, A., Geva, N., Sheintuch, L. & Ziv, Y. Hippocampal ensemble dynamics timestamp events in long-term memory. eLife 4, e12247 (2015).
Pastalkova, E., Itskov, V., Amarasingham, A. & Buzsaki, G. Internally generated cell assembly sequences in the rat hippocampus. Science 321, 1322–1327 (2008).
MacDonald, C. J., Lepage, K. Q., Eden, U. T. & Eichenbaum, H. Hippocampal ‘time cells’ bridge the gap in memory for discontiguous events. Neuron 71, 737–749 (2011).
Zhou, J. et al. Evolving schema representations in orbitofrontal ensembles during learning. Nature 590, 606–611 (2021).
Zhou, J. et al. Complementary task structure representations in hippocampus and orbitofrontal cortex during an odor sequence task. Curr. Biol. 29, 3402–3409 (2019).
Miller, E. K. & Cohen, J. D. An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24, 167–202 (2001).
Bernardi, S. et al. The geometry of abstraction in the hippocampus and prefrontal cortex. Cell 183, 954–967 (2020).
Morton, N. W., Schlichting, M. L. & Preston, A. R. Representations of common event structure in medial temporal lobe and frontoparietal cortex support efficient inference. Proc. Natl Acad. Sci. USA 117, 29338–29345 (2020).
Samborska, V., Butler, J. L., Walton, M. E., Behrens, T. E. & Akam, T. Complementary task representations in hippocampus and prefrontal cortex for generalizing the structure of problems. Nat. Neurosci. (in the press).
Schuck, N. W., Cai, M. B., Wilson, R. C. & Niv, Y. Human orbitofrontal cortex represents a cognitive map of state space. Neuron 91, 1402–1412 (2016).
Yu, J. Y., Liu, D. F., Loback, A., Grossrubatscher, I. & Frank, L. M. Specific hippocampal representations are linked to generalized cortical representations in memory. Nat. Commun. 9, 2209 (2018).
Hawkins, J., Lewis, M., Klukas, M., Purdy, S. & Ahmad, S. A framework for intelligence and cortical function based on grid cells in the neocortex. Front. Neural Circuits 12, 121 (2019).
Lewis, M. Hippocampal spatial mapping as fast graph learning. Preprint at https://arxiv.org/abs/2107.00567 (2021).
Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 17351780 (1997).
Vaswani, A. et al. Attention is all you need. Adv. Neural Inf. Process. Syst. 20, 5999–6009 (2017).
Brown, T. B. et al. Language models are few-shot learners. Preprint at https://arxiv.org/abs/2005.14165 (2020).
Dosovitskiy, A. et al. An image is worth 16x16 words: transformers for image recognition at scale. Preprint at https://arxiv.org/abs/2010.11929 (2020).
Amalric, M. & Dehaene, S. Origins of the brain networks for advanced mathematics in expert mathematicians. Proc. Natl Acad. Sci. USA 113, 4909–4917 (2016).
Whittington, J. C. R., Warren, J. & Behrens, T. E. J. Relating transformers to models and neural representations of the hippocampal formation. In International Conference on Learning Representations (2022).
Higgins, I. et al. β-VAE: learning basic visual concepts with a constrained variational framework. In International Conference on Learning Representations (2017).
Higgins, I. et al. Towards a definition of disentangled representations. Preprint at https://arxiv.org/abs/1812.02230 (2018).
Killian, N. J. & Buffalo, E. A. Grid cells map the visual world. Nat. Neurosci. 21, 161–162 (2018).
Nau, M., Navarro Schröder, T., Bellmund, J. L. S. & Doeller, C. F. Hexadirectional coding of visual space in human entorhinal cortex. Nat. Neurosci. 21, 188–190 (2018).
Julian, J. B., Keinath, A. T., Frazzetta, G. & Epstein, R. A. Human entorhinal cortex represents visual space using a boundary-anchored grid. Nat. Neurosci. 21, 191–194 (2018).
Schwartenbeck, P. et al. Generative replay for compositional visual understanding in the prefrontal-hippocampal circuit. Preprint at bioRxiv https://doi.org/10.1101/2021.06.06.447249 (2021).
Bellmund, J. L. S., Gärdenfors, P., Moser, E. I. & Doeller, C. F. Navigating cognition: spatial codes for human thinking. Science 362, eaat6766 (2018).
Salz, D. M. et al. Time cells in hippocampal area CA3. J. Neurosci. 36, 7476–7484 (2016).
Dayan, P. Improving generalization for temporal difference learning: the successor representation. Neural Comput. 5, 613–624 (1993).
Mehta, M. R., Quirk, M. C. & Wilson, M. A. Experience-dependent asymmetric shape of hippocampal receptive fields. Neuron 25, 707–715 (2000).
Derdikman, D. et al. Fragmentation of grid cell maps in a multicompartment environment. Nat. Neurosci. 12, 1325–1332 (2009).
Krupic, J., Burgess, N. & O’Keefe, J. Neural representations of location composed of spatially periodic bands. Science 337, 853–857 (2012).
Garvert, M. M., Dolan, R. J. & Behrens, T. E. A map of abstract relational knowledge in the human hippocampal–entorhinal cortex. eLife 6, e17086 (2017).
Schapiro, A. C., Turk-browne, N. B., Botvinick, M. M., Norman, K. A. & Schapiro, A. C. Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning. Philos. Trans. R. Soc. Lond. B Biol. Sci. 372, 20160049 (2017).
Momennejad, I. Learning structures: predictive representations, replay, and generalization. Curr. Opin. Behav. Sci. 32, 155–166 (2020).
Todorov, E. Linearly solvable Markov decision problems. In Advances in Neural Information Processing Systems 1369–1376 https://doi.org/10.7551/mitpress/7503.003.0176 (2007).
Cormack, G. V. & Horspool, R. N. S. Data compression using dynamic Markov modelling. Comput. J. 30, 541–550 (1987).
Dempster, A. P., Laird, N. M. & Rubin, D. B. Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. 39, 1–22 (1977).
Zhang, K. Representation of spatial orientation by the intrinsic dynamics of the head-direction cell ensemble: a theory. J. Neurosci. 16, 2112–2126 (1996).
Skaggs, W. E., Knierim, J. J., Kudrimoti, H. S. & McNaughton, B. L. A model of the neural basis of the rat’s sense of direction. Adv. neural Inf. Process. Syst. 7, 173–180 (1995).
Samsonovich, A. & McNaughton, B. L. Path integration and cognitive mapping in a continuous attractor neural network model. J. Neurosci. 17, 5900–5920 (1997).
Tsodyks, M. Attractor neural network models of spatial maps in hippocampus. Hippocampus 9, 481–489 (1999).
Burak, Y. & Fiete, I. R. Accurate path integration in continuous attractor network models of grid cells. PLoS Comput. Biol. 5, e1000291 (2009).
Ben-Yishai, R., Bar-Or, R. L. & Sompolinsky, H. Theory of orientation tuning in visual cortex. Proc. Natl Acad. Sci. USA 92, 3844–3848 (1995).
Kim, S. S., Rouault, H., Druckmann, S. & Jayaraman, V. Ring attractor dynamics in the Drosophila central brain. Science 356, 849–853 (2017).
Gardner, R. J. et al. Toroidal topology of population activity in grid cells. Nature 602, 123–128 (2022).
Cueva, C. J. & Wei, X. -X. Emergence of grid-like representations by training recurrent neural networks to perform spatial localization. Peeprint at https://arxiv.org/abs/1803.07770 (2018).
Sorscher, B., Mel, G. C., Ganguli, S. & Ocko, S. A. A unified theory for the origin of grid cells through the lens of pattern formation. Adv. Neural Inf. Process. Syst. 32, 10003–10013 (2019).
Pritzel, A. et al. Neural episodic control. Preprint at https://arxiv.org/abs/1703.01988 (2017).
Hebb, D. O. The Organization of Behavior; a Neuropsychological Theory (Wiley, 1949).
Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities (associative memory/parallel processing/categorization/content-addressable memory/fail-soft devices). Biophysics 79, 2554–2558 (1982).
McKenzie, S. et al. Hippocampal representation of related and opposing memories develop within distinct, hierarchically organized neural schemas. Neuron 83, 202–215 (2014).
Bunsey, M. & Eichenbaum, H. Conservation of hippocampal memory function in rats and humans. Nature 379, 255–257 (1996).
Acknowledgements
We thank N. Burgess and C. Sun for helpful comments on earlier drafts of the manuscript. We thank the following funding sources: Sir Henry Wellcome Post-doctoral Fellowship (222817/Z/21/Z) to J.C.R.W.; Wellcome Trust DPhil Scholarship to D.M.; and Wellcome Principal Research Fellowship (219525/Z/19/Z), Wellcome Collaborator award (214314/Z/18/Z), and JS McDonnell Foundation award (JSMF220020372) to T.E.J.B.. The Wellcome Centre for Integrative Neuroimaging and Wellcome Centre for Human Neuroimaging are each supported by core funding from the Wellcome Trust (203139/Z/16/Z, 203147/Z/16/Z).
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J.C.R.W. and T.E.J.B. conceptualized the manuscript. J.C.R.W. and D.M. performed simulations. J.C.R.W. drafted the manuscript with input from D.M. J.C.R.W. and T.E.J.B. edited the manuscript with input from D.M. and J.J.W.B.
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Whittington, J.C.R., McCaffary, D., Bakermans, J.J.W. et al. How to build a cognitive map. Nat Neurosci 25, 1257–1272 (2022). https://doi.org/10.1038/s41593-022-01153-y
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DOI: https://doi.org/10.1038/s41593-022-01153-y
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