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Berlin Theoretical Neuroscience Running seminar

Stochastic Partial Differential Equations

Participants should learn basic concepts, their theoretical foundation, and the most common models of stochastic evolution equations on Hilbert spaces with a view towards its applications to the modelling, analysis and numerical approximation of spatially extended neurons and neural systems subject to noise. Participants will learn basic techniques to analyze global properties of neural systems both qualitatively and quantitatively. Participants will also learn basic simulation techniques for stochastic neural systems and how to evaluate simulation output. Participants should also be able to adapt models to new problems as well as to develop new models of neural systems.
Contents include:
Gaussian measures on Hilbert spaces, stochastic integration on Hilbert spaces, semilinear stochastic evolution equations, stochastic reaction diffusion systems, continuum limits of neural networks.

Seminar

MSc, PhD
  • Maths: advanced
  • Programming: basic
  • Neurobiology: none

Bernstein Center Computational Neuroscience Berlin

Technical University of Berlin

Stannat
Winter term /weekly/2 h per week / 3 ECTS
Visit the course website