Materials for the PANGEOS Uncertainty Quantification workshop
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Updated
Oct 1, 2024 - Jupyter Notebook
Materials for the PANGEOS Uncertainty Quantification workshop
These are python version of codes that I wrote to analyze images and videos of a macromolecule, that I took via fluorescence microscopy, in my research in grad school.
Jupyter widgets for use with NeuroML
Nitrogen balance model components and test procedures
Simulation of an FtsZ filament in C++
Toolbox for parameter estimation of the Standard Model of diffusion in white matter
Bottom-up dynamical modeling of biological systems using ordinary, partial, and stochastic differential equations.
Feed-forward neural network that predicts cytokine dynamics of T-cells in response to antigens
Model of BG components to learn more about mechanisms of DBS
Data and figures (R-notebooks) for the in-silico biodiversity experiment using DART biophysical model
CHARM, a model that estimates the GHG impacts and land-use requirements of forestry
Large-scale thalamocortical network model for simulating physiological and paroxysmal brain rhythms: version 2
Large-scale thalamocortical network model for simulating physiological and paroxysmal brain rhythms: version 1
This toolbox allows to simulate interactions between neural mass potentials (resonating at 40Hz) in the structural connectome. Here, a single brain region is seen as an oscillator whose dynamics is explained by a Stuart Landau Equation (Hopf Bifurcation including time delay).
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