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When machines look at neurons: statistical learning from neuroscience time series

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Understanding how the brain works in healthy and pathological conditions is considered as one of the challenges for the 21st century. After the first electroencephalography (EEG) measurements in 1929, the 90’s was the birth of modern functional brain imaging with the first functional MRI (fMRI) and full head magnetoencephalography (MEG) system. By offering noninvasively unique insights into the living brain, imaging has revolutionized in the last twenty years both clinical and cognitive neuroscience. Over the last 10 to 15 years, driven by more open data and recent algorithmic progress the field of brain imaging and electrophysiology has embraced a new set of tools in order to extract knowledge from data. Using statistical machine learning new applications have emerged, going from brain computer interaction systems, "mind reading" and cortical source imaging at a milisecond time scale. In this talk, I will briefly review some statistical and algorithmic challenges raised by the different techniques and show how modern computational and machine learning tools can help uncover neural activations in multivariate time series. Keywords : Convex optimization, High dimensional regression, Sparsity, Lasso, Convolutional networks, representation learning

Monday, January 16, 2017 - 14:00 to 15:15
Amphi Boda, Bâtiment B, Centrale Lille
Alexandre Gramfort
Télécom ParisTech, Paris