Unlike traditional inverse identification tools that rely on gradient and gradient-free methods, simulation-based inference has been established as the powerful alternative approach that yields twofold improvement over such methods. Firstly, it does not only result in a single set of optimal parameters, rather simulation-based inference acts as if the actual statistical inference is performed and provides an estimate of the full posterior distribution over parameters. Secondly, it exploits prior system knowledge sparsely, using only the most important features to identify mechanistic models which are consistent with the measured data. The aim of the project is to support the simulation-based inference in the brian2modelfitting toolbox by linking it to the sbi, PyTorch powered library for simulation-based inference, development of which is coordinated at the Macke lab.



Ante Lojic Kapetanovic


  • Marcel Stimberg