Noise Resilience of an RGNG-based Grid Cell Model

Jochen Kerdels, Gabriele Peters

Abstract

Grid cells are neurons in the entorhinal cortex of mammals that are known for their peculiar, grid-like firing patterns. We developed a generic computational model that describes the behavior of neurons with such firing patterns in terms of a competitive, self-organized learning process. Here we investigate how this process can cope with increasing amounts of noise in its input signal. We demonstrate, that the firing patterns of simulated neurons are mostly unaffected with regard to their structure even if high levels of noise are present in the input. In contrast, the maximum activity of the corresponding neurons decreases significantly with increasing levels of noise. Based on these results we predict that real grid cells can retain their triangular firing patterns in the presence of noise, but may exhibit a noticeable decrease in their peak firing rates.

References

  1. Boccara, C. N., Sargolini, F., Thoresen, V. H., Solstad, T., Witter, M. P., Moser, E. I., and Moser, M.-B. (2010). Grid cells in pre- and parasubiculum. Nat Neurosci, 13(8):987-994.
  2. Doherty, K., Adams, R., and Davey, N. (2005). Hierarchical growing neural gas. In Ribeiro, B., Albrecht, R., Dobnikar, A., Pearson, D., and Steele, N., editors, Adaptive and Natural Computing Algorithms, pages 140-143. Springer Vienna.
  3. Domnisoru, C., Kinkhabwala, A. A., and Tank, D. W. (2013). Membrane potential dynamics of grid cells. Nature, 495(7440):199-204.
  4. Fritzke, B. (1995). A growing neural gas network learns topologies. In Advances in Neural Information Processing Systems 7, pages 625-632. MIT Press.
  5. Fyhn, M., Molden, S., Witter, M. P., Moser, E. I., and Moser, M.-B. (2004). Spatial representation in the entorhinal cortex. Science, 305(5688):1258-1264.
  6. Gatome, C., Slomianka, L., Lipp, H., and Amrein, I. (2010). Number estimates of neuronal phenotypes in layer {II} of the medial entorhinal cortex of rat and mouse. Neuroscience, 170(1):156 - 165.
  7. Giocomo, L. M. and Hasselmo, M. E. (2008). Time constants of h current in layer ii stellate cells differ along the dorsal to ventral axis of medial entorhinal cortex. The Journal of Neuroscience, 28(38):9414-9425.
  8. Giocomo, L. M., Zilli, E. A., Fransn, E., and Hasselmo, M. E. (2007). Temporal frequency of subthreshold oscillations scales with entorhinal grid cell field spacing. Science, 315(5819):1719-1722.
  9. Hafting, T., Fyhn, M., Molden, S., Moser, M.-B., and Moser, E. I. (2005). Microstructure of a spatial map in the entorhinal cortex. Nature, 436(7052):801-806.
  10. Hausser, M. (2014). Optogenetics: the age of light. Nat Meth, 11(10):1012-1014.
  11. Jacobs, J., Weidemann, C. T., Miller, J. F., Solway, A., Burke, J. F., Wei, X.-X., Suthana, N., Sperling, M. R., Sharan, A. D., Fried, I., and Kahana, M. J. (2013). Direct recordings of grid-like neuronal activity in human spatial navigation. Nat Neurosci, 16(9):1188-1190.
  12. Kerdels, J. (2016). A Computational Model of Grid Cells based on a Recursive Growing Neural Gas. PhD thesis, FernUniversität in Hagen, Hagen.
  13. Kerdels, J. and Peters, G. (2013). A computational model of grid cells based on dendritic self-organized learning. In Proceedings of the International Conference on Neural Computation Theory and Applications.
  14. Kerdels, J. and Peters, G. (2015a). Analysis of highdimensional data using local input space histograms. Neurocomputing, 169:272 - 280.
  15. Kerdels, J. and Peters, G. (2015b). A new view on grid cells beyond the cognitive map hypothesis. In 8th Conference on Artificial General Intelligence (AGI 2015) .
  16. Killian, N. J., Jutras, M. J., and Buffalo, E. A. (2012). A map of visual space in the primate entorhinal cortex. Nature, 491(7426):761-764.
  17. Koch, C. (2004). Biophysics of Computation: Information Processing in Single Neurons. Computational Neuroscience Series. Oxford University Press, USA.
  18. Krupic, J., Burgess, N., and OKeefe, J. (2012). Neural representations of location composed of spatially periodic bands. Science, 337(6096):853-857.
  19. Lingenhhl, K. and Finch, D. (1991). Morphological characterization of rat entorhinal neurons in vivo: somadendritic structure and axonal domains. Experimental Brain Research, 84(1):57-74.
  20. Mhatre, H., Gorchetchnikov, A., and Grossberg, S. (2010). Grid cell hexagonal patterns formed by fast selforganized learning within entorhinal cortex (published online 2010). Hippocampus, 22(2):320-334.
  21. Pilly, P. K. and Grossberg, S. (2012). How do spatial learning and memory occur in the brain? coordinated learning of entorhinal grid cells and hippocampal place cells. J. Cognitive Neuroscience, pages 1031-1054.
  22. Podolak, I. and Bartocha, K. (2009). A hierarchical classifier with growing neural gas clustering. In Kolehmainen, M., Toivanen, P., and Beliczynski, B., editors, Adaptive and Natural Computing Algorithms, volume 5495 of Lecture Notes in Computer Science, pages 283-292. Springer Berlin Heidelberg.
  23. Sargolini, F., Fyhn, M., Hafting, T., McNaughton, B. L., Witter, M. P., Moser, M.-B., and Moser, E. I. (2006). Conjunctive representation of position, direction, and velocity in entorhinal cortex. Science, 312(5774):758-762.
  24. Stensola, H., Stensola, T., Solstad, T., Froland, K., Moser, M.-B., and Moser, E. I. (2012). The entorhinal grid map is discretized. Nature, 492(7427):72-78.
  25. Yartsev, M. M., Witter, M. P., and Ulanovsky, N. (2011). Grid cells without theta oscillations in the entorhinal cortex of bats. Nature, 479(7371):103-107.
Download


Paper Citation


in Harvard Style

Kerdels J. and Peters G. (2016). Noise Resilience of an RGNG-based Grid Cell Model . In Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (IJCCI 2016) ISBN 978-989-758-201-1, pages 33-41. DOI: 10.5220/0006045400330041


in Bibtex Style

@conference{ncta16,
author={Jochen Kerdels and Gabriele Peters},
title={Noise Resilience of an RGNG-based Grid Cell Model},
booktitle={Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (IJCCI 2016)},
year={2016},
pages={33-41},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006045400330041},
isbn={978-989-758-201-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (IJCCI 2016)
TI - Noise Resilience of an RGNG-based Grid Cell Model
SN - 978-989-758-201-1
AU - Kerdels J.
AU - Peters G.
PY - 2016
SP - 33
EP - 41
DO - 10.5220/0006045400330041