“Joint INT-INS workshop on model inversion techniques for neuroscience: linking neural models to brain data”
Have you ever asked yourself how to find the neural model that best describes your data? What a good question! For complex models, no easy solution exists. Generally, this issue is referred to as “model inversion”, and it often represents an ill-posed problem in data science, where no unique solution is at hand. However, recent advances in ML and AI are providing interesting tools that can be used to perform model inversion and fit neural models to brain data. The aim of the workshop is to provide an overview of projects at INT and INS focusing on model inversion. Although technical, the workshop will try to provide an overview for experimentalists and those who are not familiar with model inversion techniques.
PROGRAM
14:00 Nina Baldy (TNG-INS) - Dynamic Causal Modeling in Probabilistic Programming Languages
14:45 Pedro Garcia (BraiNets-INT) - Dynamic Causal Modelling to infer effective connectivity from task-related MEG high-gamma activitiy (HGA): a workflow for Bayesian model inversion
15:30 Pause ☕
15:45 Cyprien Dautrevaux (BraiNets-INT) - Simulation Based Inference (SBI) for patch clamp recordings and neuronal conductance estimation
16:30 Jean-Didier Lemaréchal (BraiNets-INT) - Bayesian inference applied to neuronal models: methods & applications
17:15 Abolfazl Ziaeemehr (TNG-INS) - Virtual Brain Inference (VBI): A flexible and integrative toolkit for efficient probabilistic inference on virtual brain models
18:00 Glam Rock 🍻 🥜