Advertisement

Abstract

This paper is devoted to the problem of understanding mechanisms underlying behavioral correlates of head direction (HD) cells in the mammalian retrosplenial cortex. HD cells become active when an animal, such as rat, is facing a particular direction in its environment. The robustness of this phenomenon is usually attributed to attractor dynamics of the HD cell system. According to the standard view, a ring attractor exists in some abstract space, with HD cells symbolically allocated on the ring, so that any natural state of the system corresponds to a bump of activity on the ring. In apparent contradiction with this standard model are recent discoveries of so-called “flip cells”, that constitute a minority of HD cells and can either rotate their directional tuning by 180° when an animal transitions between two environments, or interpolate between discordant cues, or demonstrate a bimodal tuning curve. Here a continuous attractor network model is described that is capable of a qualitative reproduction of these phenomena, while being consistent with the ring attractor hypothesis. The model assumes that there is more than one attractor ring in the HD system. Results of the concept-proof simulation suggest a correction to the standard view of how the internal sense of direction is formed in the rat brain.

Keywords

Attractor neural networks Continuous attractor Navigation Head direction cells Animal cognition 

Notes

Acknowledgments

The author is grateful to Drs. Kate J. Jeffery and Hector Page from the Institute of Behavioural Neuroscience, University College London, London, United Kingdom, for fruitful discussions of the ideas of this work and its outcome. This work was supported by the RSF Grant # 15-11-30014.

References

  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    Dumont, J.R., Taube, J.S.: The neural correlates of navigation beyond the hippocampus. Prog. Brain Res. 219, 83–102 (2015)CrossRefGoogle Scholar
  3. 3.
    Taube, J.S.: Head direction cells and the neurophysiological basis for a sense of direction. Prog. Neurobiol. 55(3), 225–256 (1998)CrossRefGoogle Scholar
  4. 4.
    Samsonovich, A., McNaughton, B.L.: Path integration and cognitive mapping in a continuous attractor neural network model. J. Neurosci. 17(15), 5900–5920 (1997)CrossRefGoogle Scholar
  5. 5.
    Strogatz, S.H.: Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering, p. 324. Addison-Wesley, Reading, MA (1994)Google Scholar
  6. 6.
    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. In: Tesauro, G., Touretzky, D., Leen, T. (eds.) Advances in Neural Information Processing Systems, pp. 130–180. MIT, Cambridge (1995)Google Scholar
  7. 7.
    Zhang, K.: Representation of spatial orientation by the intrinsic dynamics of the head-direction cell ensemble: a theory. J. Neurosci. 16(6), 2112–2126 (1996)CrossRefGoogle Scholar
  8. 8.
    Samsonovich, A.V.: Continuous attractor network. In: Izhikevich, E.M. (ed.) Scholarpedia: The Free Peer-Reviewed Encyclopedia (2010). http://www.scholarpedia.org/article/Continuous_Attractor_Network
  9. 9.
    Peyrache, A., Lacroix, M.M., Petersen, P.C., Buzsaki, G.: Internally organized mechanisms of the head direction sense. Nat. Neurosci. 18(4), 569–575 (2015). https://doi.org/10.1038/nn.3968CrossRefGoogle Scholar
  10. 10.
    Jacob, P.Y., Casali, G., Spieser, L., Page, H., Overington, D., Jeffery, K.: Nat. Neurosci. 20(2), 173–175 (2017). https://doi.org/10.1038/nn.4465CrossRefGoogle Scholar
  11. 11.
    Knight, R., Piette, C.E., Page, H., Walters, D., Marozzi, E., Nardini, M., Stringer, S., Jeffery, K.J.: Weighted cue integration in the rodent head direction system. Philos. Trans. R. Soc. Lond. B 369(1635), 20120512 (2013)CrossRefGoogle Scholar
  12. 12.
    Page, H.J.I., Walters, D.M., Knight, R., Piette, C.E., Jeffery, K.J., Stringer, S.M.: A theoretical account of cue averaging in the rodent head direction system. Philos. Trans. R. Soc. Lond. B 369(1635), 20130283 (2013)CrossRefGoogle Scholar
  13. 13.
    Samsonovich, A.V.: Bringing consciousness to cognitive neuroscience: a computational perspective. J. Integr. Des. Process Sci. 11(3), 19–30 (2007)Google Scholar
  14. 14.
    Finkelstein, A., Derdikman, D., Rubin, A., Foerster, J.N., Las, L., Ulanovsky, N.: Three-dimensional head-direction coding in the bat brain. Nature 517(7533), 159-U65 (2015). https://doi.org/10.1038/nature14031CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Cybernetics and BICA Lab, Institute for Cyber Intelligence SystemsNational Research Nuclear University “Moscow Engineering Physics Institute”MoscowRussian Federation
  2. 2.George Mason UniversityFairfaxUSA

Personalised recommendations