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Noise-driven bifurcations in a neural field system modelling networks of grid cells

Abstract:
The activity generated by an ensemble of neurons is affected by various noise sources. It is a well-recognised challenge to understand the effects of noise on the stability of such networks. We demonstrate that the patterns of activity generated by networks of grid cells emerge from the instability of homogeneous activity for small levels of noise. This is carried out by upscaling a noisy grid cell model to a system of partial differential equations in order to analyse the robustness of network activity patterns with respect to noise. Inhomogeneous network patterns are numerically understood as branches bifurcating from unstable homogeneous states for small noise levels. We prove that there is a phase transition occurring as the level of noise decreases. Our numerical study also indicates the presence of hysteresis phenomena close to the precise critical noise value.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/s00285-022-01811-6

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author


Publisher:
Springer
Journal:
Journal of Mathematical Biology More from this journal
Volume:
85
Article number:
42
Publication date:
2022-09-27
Acceptance date:
2022-05-03
DOI:
EISSN:
1432-1416
ISSN:
0303-6812


Language:
English
Keywords:
Pubs id:
1195596
Local pid:
pubs:1195596
Deposit date:
2022-05-14

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