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Nonlinear partial differential equations in neuroscience: from modelling to mathematical theory

Abstract:
Many systems of partial differential equations have been proposed as simplified representations of complex collective behaviours in large networks of neurons. In this survey, we briefly discuss their derivations and then review the mathematical methods developed to handle the unique features of these models, which are often nonlinear and non-local. The first part focuses on parabolic FokkerPlanck equations: the Nonlinear Noisy Leaky Integrate and Fire neuron model, stochastic neural fields in PDE form with applications to grid cells, and rate-based models for decision-making. The second part concerns hyperbolic transport equations, namely the model of the Time Elapsed since the last discharge and the jump-based Leaky Integrate and Fire model. The last part covers some kinetic mesoscopic models, with particular attention to the kinetic Voltage-Conductance model and FitzHugh-Nagumo kinetic Fokker-Planck systems.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1142/S0218202525400044

Authors

More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author
ORCID:
0000-0001-8819-4660


More from this funder
Funder identifier:
https://ror.org/0472cxd90
Grant:
883363
More from this funder
Funder identifier:
https://ror.org/0439y7842
Grant:
EP/V051121/1


Publisher:
World Scientific Publishing
Journal:
Mathematical Models and Methods in Applied Sciences More from this journal
Volume:
35
Issue:
2
Pages:
403-584
Publication date:
2025-03-04
Acceptance date:
2025-01-10
DOI:
EISSN:
1793-6314
ISSN:
0218-2025


Language:
English
Keywords:
Pubs id:
2077406
Local pid:
pubs:2077406
Deposit date:
2025-01-12
ARK identifier:

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