The Hudson School of Mathematics
Dynamical Systems, Networks and Applications
This class deals mainly with the analysis of Dynamical Systems (mainly ODEs and PDEs)
embedded in a Network structure. Each node represents a unit which evolves dynamically and interact with other units. The interactions are represented by edges. The class consists in 9x3 hours sessions.
The program is as follows.
- Lecture 1. An introduction to classical Networks. Examples in Neuroscience context
- Lecture 2
Qualitative Analysis of Reaction-Diffusion Systems in Neuroscience Context. Extension to Synchronization of Networks of RD Systems
- Lecture 3
A network of FKPP equations to model dementia
- Lecture 4
Mathematical Modeling of Emotions
- Lecture 5
A simple Nonautonomous Network SIR Model for COVID19 DATA
- Lecture 6
Slow-Fast, MMOs and Canards in ODEs, Coupled Systems and PDEs
- Lecture 7. Free discussion around a few papers on the following topics: 1-How the fly's brain work (Lyu, Abbott,Maimon 2022),2- fondations of Chaos Theory and Ergodic Theory (two papers of Eckman and Ruelle, and Ruelle),3-
Introduction to the Theory of the Topological Degree and Application to PDEs
- Lecture 8.
Reduced models to generate signals that resemble brain rhythms.
- Lecture 9. Dendrite architecture determine mitochondrial localization patterns in vivo
To register for the class, contact us at contact@hudsonschoolmath.com or fill out this form .
More informations and material here (only for registered students)
