Noise Resilience of an RGNG-based Grid Cell Model

Jochen Kerdels, Gabriele Peters


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.


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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

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)},

in EndNote Style

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