To guide navigation, the nervous system integrates multisensory self-motion and landmark information. We dissected how these inputs generate spatial representations by recording entorhinal grid, border and speed cells in mice navigating virtual environments. Manipulating the gain between the animal’s locomotion and the visual scene revealed that border cells responded to landmark cues while grid and speed cells responded to combinations of locomotion, optic flow and landmark cues in a context-dependent manner, with optic flow becoming more influential when it was faster than expected. A network model explained these results by revealing a phase transition between two regimes in which grid cells remain coherent with or break away from the landmark reference frame. Moreover, during path-integration-based navigation, mice estimated their position following principles predicted by our recordings. Together, these results provide a theoretical framework for understanding how landmark and self-motion cues combine during navigation to generate spatial representations and guide behavior.
This is a preview of subscription content
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $4.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
Rowland, D. C., Roudi, Y., Moser, M. B. & Moser, E. I. Ten years of grid cells. Annu. Rev. Neurosci. 39, 19–40 (2016).
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).
Taube, J. S., Muller, R. U. & Ranck, J. B. J. Jr. Head-direction cells recorded from the postsubiculum in freely moving rats. I. Description and quantitative analysis. J. Neurosci. 10, 420–435 (1990).
Sargolini, F. et al. Conjunctive representation of position, direction, and velocity in entorhinal cortex. Science 312, 758–762 (2006).
Aronov, D. & Tank, D. W. Engagement of neural circuits underlying 2D spatial navigation in a rodent virtual reality system. Neuron 84, 442–456 (2014).
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).
Hinman, J. R., Brandon, M. P., Climer, J. R., Chapman, G. W. & Hasselmo, M. E. Multiple running speed signals in medial entorhinal cortex. Neuron 91, 666–679 (2016).
Kropff, E., Carmichael, J. E., Moser, M. B. & Moser, E. I. Speed cells in the medial entorhinal cortex. Nature 523, 419–424 (2015).
McNaughton, B. L., Battaglia, F. P., Jensen, O., Moser, E. I. & Moser, M. B. Path integration and the neural basis of the ‘cognitive map’. Nat. Rev. Neurosci. 7, 663–678 (2006).
Winter, S. S., Mehlman, M. L., Clark, B. J. & Taube, J. S. Passive transport disrupts grid signals in the parahippocampal cortex. Curr. Biol. 25, 2493–2502 (2015).
Winter, S. S., Clark, B. J. & Taube, J. S. Disruption of the head direction cell network impairs the parahippocampal grid cell signal. Science 347, 870–874 (2015).
Hardcastle, K., Ganguli, S. & Giocomo, L. M. Environmental boundaries as an error correction mechanism for grid cells. Neuron 86, 827–839 (2015).
Krupic, J., Bauza, M., Burton, S., Barry, C. & O’Keefe, J. Grid cell symmetry is shaped by environmental geometry. Nature 518, 232–235 (2015).
Barry, C., Hayman, R., Burgess, N. & Jeffery, K. J. Experience-dependent rescaling of entorhinal grids. Nat. Neurosci. 10, 682–684 (2007).
Stensola, T., Stensola, H., Moser, M. B. & Moser, E. I. Shearing-induced asymmetry in entorhinal grid cells. Nature 518, 207–212 (2015).
Perez-Escobar, J. A., Kornienko, O., Latuske, P., Kohler, L. & Allen, K. Visual landmarks sharpen grid cell metric and confer context specificity to neurons of the medial entorhinal cortex. Elife 23, e16937 (2016).
Chen, G., Manson, D., Cacucci, F. & Wills, T. J. Absence of visual input results in the disruption of grid cell firing in the mouse. Curr. Biol. 26, 2335–2342 (2016).
Krupic, J., Bauza, M., Burton, S., Lever, C. & O’Keefe, J. How environment geometry affects grid cell symmetry and what we can learn from it. Phil. Trans. R. Soc. Lond. B 369, 20130188 (2013).
Krupic, J., Bauza, M., Burton, S. & O’Keefe, J. Local transformations of the hippocampal cognitive map. Science 359, 1143–1146 (2018).
Dombeck, D. A., Khabbaz, A. N., Collman, F., Adelman, T. L. & Tank, D. W. Imaging large-scale neural activity with cellular resolution in awake, mobile mice. Neuron 56, 43–57 (2007).
Chen, G., King, J. A., Burgess, N. & O’Keefe, J. How vision and movement combine in the hippocampal place code. Proc. Natl Acad. Sci. USA 110, 378–383 (2013).
Eggink, H., Mertens, P., Storm, I. & Giocomo, L. M. Hyperpolarization-activated cyclic nucleotide-gated 1 independent grid cell-phase precession in mice. Hippocampus 24, 249–256 (2014).
Hardcastle, K., Maheswaranathan, N., Ganguli, S. & Giocomo, L. M. A multiplexed, heterogeneous, and adaptive code for navigation in medial entorhinal cortex. Neuron 94, 375–387.e7 (2017).
Jeewajee, A., Barry, C., O’Keefe, J. & Burgess, N. Grid cells and theta as oscillatory interference: electrophysiological data from freely moving rats. Hippocampus 18, 1175–1185 (2008).
McFarland, W. L., Teitelbaum, H. & Hedges, E. K. Relationship between hippocampal theta activity and running speed in the rat. J. Comp. Physiol. Psychol. 88, 324–328 (1975).
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).
Burak, Y. & Fiete, I. R. Accurate path integration in continuous attractor network models of grid cells. PLOS Comput. Biol. 5, e1000291 (2009).
Samsonovich, A. & McNaughton, B. L. Path integration and cognitive mapping in a continuous attractor neural network model. J. Neurosci. 17, 5900–5920 (1997).
Ocko, S.A., Hardcastle, K., Giocomo, L.M. & Ganguli, S. Emergent elasticity in the neural code for space. Preprint at bioRxiv https://doi.org/10.1101/326793 (2018).
Fuhs, M. C. & Touretzky, D. S. A spin glass model of path integration in rat medial entorhinal cortex. J. Neurosci. 26, 4266–4276 (2006).
Strogatz, S. H. From Kuramoto to Crawford: exploring the onset of synchronization in populations of coupled oscillators. Physica D. 143, 1–20 (2000).
Stensola, H. et al. The entorhinal grid map is discretized. Nature 492, 72–78 (2012).
Derdikman, D. et al. Fragmentation of grid cell maps in a multicompartment environment. Nat. Neurosci. 12, 1325–1332 (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).
Saleem, A. B., Ayaz, A., Jeffery, K. J., Harris, K. D. & Carandini, M. Integration of visual motion and locomotion in mouse visual cortex. Nat. Neurosci. 16, 1864–1869 (2013).
Burwell, R. D. & Amaral, D. G. Cortical afferents of the perirhinal, postrhinal, and entorhinal cortices of the rat. J. Comp. Neurol. 398, 179–205 (1998).
Wang, Q., Gao, E. & Burkhalter, A. Gateways of ventral and dorsal streams in mouse visual cortex. J. Neurosci. 31, 1905–1918 (2011).
Koganezawa, N., Gisetstad, R., Husby, E., Doan, T. P. & Witter, M. P. Excitatory postrhinal projections to principal cells in the medial entorhinal cortex. J. Neurosci. 35, 15860–15874 (2015).
Roth, M. M. et al. Thalamic nuclei convey diverse contextual information to layer 1 of visual cortex. Nat. Neurosci. 19, 299–307 (2016).
Andermann, M. L., Kerlin, A. M., Roumis, D. K., Glickfeld, L. L. & Reid, R. C. Functional specialization of mouse higher visual cortical areas. Neuron 72, 1025–1039 (2011).
Marshel, J. H., Garrett, M. E., Nauhaus, I. & Callaway, E. M. Functional specialization of seven mouse visual cortical areas. Neuron 72, 1040–1054 (2011).
Knill, D. C. & Pouget, A. The Bayesian brain: the role of uncertainty in neural coding and computation. Trends Neurosci. 27, 712–719 (2004).
Alexander, A. S. & Nitz, D. A. Retrosplenial cortex maps the conjunction of internal and external spaces. Nat. Neurosci. 18, 1143–1151 (2015).
Elduayen, C. & Save, E. The retrosplenial cortex is necessary for path integration in the dark. Behav. Brain Res. 272, 303–307 (2014).
Harvey, C. D., Coen, P. & Tank, D. W. Choice-specific sequences in parietal cortex during a virtual-navigation decision task. Nature 484, 62–68 (2012).
Whitlock, J. R., Pfuhl, G., Dagslott, N., Moser, M. B. & Moser, E. I. Functional split between parietal and entorhinal cortices in the rat. Neuron 73, 789–802 (2012).
Giocomo, L. M. et al. Grid cells use HCN1 channels for spatial scaling. Cell 147, 1159–1170 (2011).
Kadir, S. N., Goodman, D. F. & Harris, K. D. High-dimensional cluster analysis with the masked EM algorithm. Neural Comput. 26, 2379–2394 (2014).
Franklin, K.B.J. & Paxinos, G. The Mouse Brain in Stereotaxic Coordinates 3rd edn. (Academic, London, 2007).
Langston, R. F. et al. Development of the spatial representation system in the rat. Science 328, 1576–1580 (2010).
Wills, T. J., Cacucci, F., Burgess, N. & O’Keefe, J. Development of the hippocampal cognitive map in preweanling rats. Science 328, 1573–1576 (2010).
Domnisoru, C., Kinkhabwala, A. A. & Tank, D. W. Membrane potential dynamics of grid cells. Nature 495, 199–204 (2013).
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).
We thank A. Borrayo and A. Diaz for histology assistance, C. Moffatt for help collecting electrophysiological data, and C. Kim, C. Bennett and S. Hestrin for help setting up the VR system. L.M.G. is a New York Stem Cell Foundation – Robertson Investigator. This work was supported by funding from the New York Stem Cell Foundation, Whitehall Foundation, NIMH MH106475, an Office of Naval Research Young Investigator Program Award and a Klingenstein-Simons award to L.M.G., funding from the Simons Foundation, James S McDonnell Foundation awarded to L.M.G. and S.G., funding from the McKnight Foundation and Burroughs Wellcome Foundation to S.G., an NSF Graduate Research Fellowship and Baxter Fellowship awarded to M.G.C., a Karel Urbanek Postdoctoral Fellowship in Applied Physics awarded to S.A.O., an NSF Graduate Research Fellowship awarded to C.S.M. and funding from T32 MH020016 for I.I.C.L.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Figures 1–17
The video shows three trials of the task. In the first two trials, the animal was given a water reward after running 200 cm following the onset of the visual cues. In the third trial, the reward was omitted, but the animal slowed down in the location it usually received a reward. This spontaneous slowing behavior was used to estimate the animal’s perceived location on the track. Black and white squares on the floor, ceiling and walls were randomized each trial so that they could not be used as landmarks. The length of the intertrial interval was randomized each trial (30–130 cm)
About this article
Cite this article
Campbell, M.G., Ocko, S.A., Mallory, C.S. et al. Principles governing the integration of landmark and self-motion cues in entorhinal cortical codes for navigation. Nat Neurosci 21, 1096–1106 (2018). https://doi.org/10.1038/s41593-018-0189-y
Nature Communications (2022)
Communications Biology (2022)
Nature Reviews Neuroscience (2021)
Nature Communications (2021)
Deep reinforcement learning to study spatial navigation, learning and memory in artificial and biological agents
Biological Cybernetics (2021)