In a broad sense, much of the scientific endeavor in neuroscience is about deriving from experimental observations, at various spatial and temporal scales, information on the effective connectivity underlying the collective processing of synaptically connected neurons.
From the modeling viewpoint, this often takes the form of an ‘inverse problem’, so called because - instead of deriving the predictions of a defined model - one tries to infer an explanatory (and hopefully predictive) model from the recorded neural signals.
Depending on the experimental context and data source (fMRI, calcium fluorescence imaging, electrophysiology...), partially overlapping scientific communities developed an array of methods, borrowing from multivariate statistics, machine learning, statistical physics and more.
We believe time is ripe for attempting a cross-communities dialogue to promote an unified view of the different approaches to the definition and reconstruction of functional connectivities, and possibly a transfer of related methods. This EITN Workshop aims at taking a step in this direction.

The registration is free however mandatory due to a limited number of seats.

Speakers:

Sophie Achard (CNRS Gipsa-lab)
Demian Battaglia (Aix-Marseille University)
Viviana Betti (Sapienza University of Rome)
Cristiano Capone (Sapienza University of Rome)
Simona Cocco (LPSENS)
Gustavo Deco (University Pompeu Fabra)
Ulisse Ferrari (UPMC)
Karl Friston (University College London)
Mathieu Gilson (University Pompeu Fabra)
Joe Lizier (University of Sydney)
Daniele Marinazzo (Ghent university)
Remi Monasson (LPTENS)
Stefano Panzeri (Istituto italiano di Tecnologia)
Il Memming Park (CATNIP Lab)
Gaia Tavoni (University of Pennsylvania)
Federico Ricci Tersenghi (Sapienza University of Rome)
Jens Wilting (Max Planck Institute)

This event is sold out. Please contact the EITN (eitn@unic.cnrs-gif.fr) to be put on the waiting list.