Simulation-based inference for mechanistic models of neural dynamics

Computational modelling plays a crucial role in both generating and testing hypotheses in the natural sciences. Typically, models are designed to capture certain aspects of empirical data. These models are then analysed for a deeper understanding of the system of study and to provide predictions for further experimental validation. Despite its importance, however, this process is not without its hurdles. Notably, the quantitative linking of models to data -- also known as statistical inference --, is a task that frequently proves elusive.We will tackle this challenge by developing algorithms for simulation-based inference, a rapidly growing field in machine learning dealing with statistical inference on simulator-based models. Then, we will use these new methods to investigate the principles and biological mechanisms underlying the remarkable robustness of neural systems to perturbations. This project will be performed through collaboration with experimental partners.

Keywords:neural circuits, robustness, simulation-based inference

Disciplines:Neurophysiology, Computational biomodelling and machine learning

Project type:PhD project