Noise-driven bifurcations in a neural field system modelling networks of grid cells.
J Math Biol
; 85(4): 42, 2022 09 27.
Article
em En
| MEDLINE
| ID: mdl-36166151
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 analysing the robustness of network activity patterns with respect to noise in an upscaled noisy grid cell model in the form of a system of partial differential equations. Inhomogeneous network patterns are numerically understood as branches bifurcating from unstable homogeneous states for small noise levels. We show 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.
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01-internacional
Base de dados:
MEDLINE
Assunto principal:
Redes Neurais de Computação
/
Neurônios
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article