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Grid cell activity and path integration on 2-D manifolds in 3-D space

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Abstract

Spatial navigation relies on various types of neurons to form an internal representation in the brain of the external world. Among them, grid cells are generally believed to serve as an invariant metric system by their spatially periodic firing fields. But how this metrical coordinate system is organized in three-dimensional (3-D) real world remains a mystery, since most researches merely concerned the encoding scheme on the horizontal plane. We computationally explored the activity pattern of grid cell in the medial entorhinal cortex of crawling animal in 3-D space. By including the presumably referring signals of gravity and animal’s body plane, grid cell firing fields on curved surfaces were produced based on the novel gravity-modulated oscillatory model. The results can account for the known experimental recordings and predict a mosaic-type grid code consisting of dynamically rotated planar arrangements. We further analyzed the path integration mechanism and derived the condition to ensure the invariant grid fields on any curved surface in 3-D space. It turns out that if the grid code is indeed not fully volumetric, it may become trajectory-dependent in 3-D space. And thus for crawling animals, 3-D grid fields could be degenerated and impaired, causing the path integration and distance measurement inaccurate. Besides, the volumetric firing fields were also discussed, although it is more suitable for flying or aquatic animals. This work can help us understand the intrinsically different spatial codes and navigating abilities among species with various locomotion modes and provide new insights of how the actual physical world is represented in the brain.

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References

  1. Tolman, E.C.: Cognitive maps in rats and men. Psychol. Rev. 55(4), 189–208 (1948)

    Article  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(1), 171–5 (1971)

  3. O’Keefe, J., Recce, M.L.: Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus 3(3), 317–330 (1993)

    Article  Google Scholar 

  4. Yates, D.: Place cells as route planners. Nat. Rev. Neurosci. 14(6), 380–381 (2013)

    Article  Google Scholar 

  5. Wang, Y., Wang, R., Zhu, Y.: Optimal path-finding through mental exploration based on neural energy field gradients. Cognit. Neurodyn. 11(1), 99–111 (2017)

    Article  Google Scholar 

  6. Zeng, T., Si, B.: A brain-inspired compact cognitive mapping system. Cognit. Neurodyn. (2020). https://doi.org/10.1007/s11571-020-09621-6

    Article  Google Scholar 

  7. Sargolini, F., Fyhn, M., Hafting, T., McNaughton, B.L., Witter, M.P., Moser, M.B., et al.: Conjunctive representation of position, direction, and velocity in entorhinal cortex. Science 312(5774), 758–62 (2006)

    Article  Google Scholar 

  8. Hafting, T., Fyhn, M., Molden, S., Moser, M.B., Moser, E.I.: Microstructure of a spatial map in the entorhinal cortex. Nature 436(7052), 801–806 (2005)

    Article  Google Scholar 

  9. Fyhn, M., Molden, S., Moser, E.I., Moser, M.B.: Spatial representation in the entorhinal cortex. Science 305(5688), 1258–1264 (2004)

    Article  Google Scholar 

  10. Barry, C., Hayman, R., Burgess, N., Jeffery, K.: Experience-dependent rescaling of entorhinal grids. Nat. Neurosci. 10(6), 682–684 (2007)

    Article  Google Scholar 

  11. 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(8), 663–78 (2006)

    Article  Google Scholar 

  12. Finkelstein, A., Las, L., Ulanovsky, N.: 3-D maps and compasses in the brain. Ann. Rev. Neurosci. 39, 171–96 (2016)

    Article  Google Scholar 

  13. 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(5), 1086–1099 (2016)

    Article  Google Scholar 

  14. Domnisoru, C., Kinkhabwala, A.A., Tank, D.W.: Membrane potential dynamics of grid cells. Nature 495(7440), 199–204 (2013)

    Article  Google Scholar 

  15. Hayman, R., Casali, G., Wilson, J.J., Jeffery, K.: Grid cells on steeply sloping terrain: evidence for planar rather than volumetric encoding. Front. Psychol. 6, 925 (2015)

    Article  Google Scholar 

  16. Hayman, R., Verriotis, M.A., Jovalekic, A., Fenton, A.A., Jeffery, K.J.: Anisotropic encoding of three-dimensional space by place cells and grid cells. Nat. Neurosci. 14(9), 1182–8 (2011)

    Article  Google Scholar 

  17. Wang, Y., Xu, X., Wang, R.: An energy model of place cell network in three dimensional space. Front. Neurosci. 12, 264 (2018)

    Article  Google Scholar 

  18. Wang, Y., Xu, X., Wang, R.: The place cell activity is information-efficient constrained by energy. Neural Netw. 116, 110–118 (2019)

    Article  Google Scholar 

  19. Wang, Y., Wang, R., Xu, X.: Neural energy supply-consumption properties based on Hodgkin–Huxley model. Neural Plast. 2017, 6207141 (2017)

    Google Scholar 

  20. Wang, Y., Xu, X., Wang, R.: Energy features in spontaneous up and down oscillations. Cognit. Neurodyn. (2020). https://doi.org/10.1007/s11571-020-09597-3

    Article  Google Scholar 

  21. Tsoar, A., Nathan, R., Bartan, Y., Vyssotski, A., Dell’Omo, G., Ulanovsky, N.: Large-scale navigational map in a mammal. Proc. Natl. Acad. Sci. USA 108(37), E718–E724 (2011)

    Article  Google Scholar 

  22. Ulanovsky, N., Moss, C.F.: What the bat’s voice tells the bat’s brain. Proc. Natl. Acad. Sci. USA 105(25), 8491–8498 (2008)

    Article  Google Scholar 

  23. Yartsev, M.M., Ulanovsky, N.: Representation of three-dimensional space in the hippocampus of flying bats. Science 340(6130), 367–372 (2013)

    Article  Google Scholar 

  24. Yartsev, M.M., Witter, M.P., Ulanovsky, N.: Grid cells without theta oscillations in the entorhinal cortex of bats. Nature 479(7371), 103–7 (2011)

    Article  Google Scholar 

  25. Casali, G., Bush, D., Jeffery, K.: Altered neural odometry in the vertical dimension. Proc. Natl. Acad. Sci. USA 116(10), 4631–4636 (2019)

    Article  Google Scholar 

  26. Chen, X., Yang, T.: A neural network model of basal ganglia’s decision-making circuitry. Cognit. Neurodyn. (2020). https://doi.org/10.1007/s11571-020-09609-2

    Article  Google Scholar 

  27. Tozzi, A., Ahmad, M.Z., Peters, J.F.: Neural computing in four spatial dimensions. Cognit. Neurodyn. (2020). https://doi.org/10.1007/s11571-020-09598-2

    Article  Google Scholar 

  28. Wang, Y., Xu, X., Zhu, Y., Wang, R.: Neural energy mechanism and neurodynamics of memory transformation. Nonlinear Dyn. 97(1), 697–714 (2019)

    Article  MATH  Google Scholar 

  29. Riley, S.N., Davies, J.: A spiking neural network model of spatial and visual mental imagery. Cognit. Neurodyn. 14(2), 239–251 (2020)

    Article  Google Scholar 

  30. Tozzi, A., Peters, J.F.: Points and lines inside human brains. Cognit. Neurodyn. 13(5), 417–428 (2019)

    Article  Google Scholar 

  31. Giocomo, L.M., Moser, M.B., Moser, E.I.: Computational models of grid cells. Neuron 71(4), 589–603 (2011)

    Article  Google Scholar 

  32. Burgess, N., Barry, C., O’Keefe, J.: An oscillatory interference model of grid cell firing. Hippocampus 17(9), 801–12 (2007)

    Article  Google Scholar 

  33. Geisler, C., Robbe, D., Zugaro, M., Sirota, A., Buzsaki, G.: Hippocampal place cell assemblies are speed-controlled oscillators. Proc. Natl. Acad. Sci. USA 104(19), 8149–8154 (2007)

    Article  Google Scholar 

  34. Zilli, E.A., Hasselmo, M.E.: Coupled noisy spiking neurons as velocity-controlled oscillators in a model of grid cell spatial firing. J. Neurosci. 30(41), 13850–60 (2010)

    Article  Google Scholar 

  35. 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(4), 609–617 (2019)

    Article  Google Scholar 

  36. Burak, Y., Fiete, I.R.: Accurate path integration in continuous attractor network models of grid cells. PLoS Comput. Biol. 5(2), e1000291 (2009)

    Article  MathSciNet  Google Scholar 

  37. Fuhs, M.C., Touretzky, D.S.: A spin glass model of path integration in rat medial entorhinal cortex. J. Neurosci. 26(16), 4266–76 (2006)

    Article  Google Scholar 

  38. Burgess, N.: Grid cells and theta as oscillatory interference: theory and predictions. Hippocampus 18(12), 1157–74 (2008)

    Article  Google Scholar 

  39. Burgess, N., O’Keefe, J.: Models of place and grid cell firing and theta rhythmicity. Curr. Opinion Neurobiol. 21(5), 734–44 (2011)

    Article  Google Scholar 

  40. Laurens, J., Kim, B., Dickman, J.D., Angelaki, D.E.: Gravity orientation tuning in macaque anterior thalamus. Nat. Neurosci. 19(12), 1566–1568 (2016)

    Article  Google Scholar 

  41. Porter, B.S., Schmidt, R., Bilkey, D.K.: Hippocampal place cell encoding of sloping terrain. Hippocampus 28(11), 767–782 (2018)

    Article  Google Scholar 

  42. Horiuchi, Timothy K., Moss, Cynthia F.: Grid cells in 3-D: reconciling data and models. Hippocampus 25, 1489–1500 (2015)

    Article  Google Scholar 

  43. Zilli, Eric A.: Models of grid cell spatial firing published 2005–2011. Front. Neural Circuits 6, 16 (2012)

    Article  Google Scholar 

Download references

Acknowledgements

We thank the editor and anonymous reviewers for their valuable feedback and insightful advice. This work is supported by the National Natural Science Foundation of China (No.11802095, 11702096, 11972159, 11872180), the Natural Science Foundation of Shanghai (No. 19zr1473100), the Fundamental Research Funds for the Central Universities (No. 22201814025) and the research fund of East China University of Science and Technology (No. 50621111901004).

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Correspondence to Xuying Xu.

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Wang, Y., Xu, X., Pan, X. et al. Grid cell activity and path integration on 2-D manifolds in 3-D space. Nonlinear Dyn 104, 1767–1780 (2021). https://doi.org/10.1007/s11071-021-06337-y

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