Optimizing deep video representation to match brain activity

Hugo Richard (1) , Ana Pinho (1) , Bertrand Thirion (1) , Guillaume Charpiat (2)

1 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
2 TAU - TAckling the Underspecified

The comparison of observed brain activity with the statistics generated by artificial intelligence systems is useful to probe brain functional organization under ecological conditions. Here we study fMRI activity in ten subjects watching color natural movies and compute deep representations of these movies with an architecture that relies on optical flow and image content. The association of activity in visual areas with the different layers of the deep architecture displays complexity-related contrasts across visual areas and reveals a striking foveal/peripheral dichotomy.

Mots clés: video encoding; brain mapping; deep learning

Domaines: Intelligence artificielle [cs.AI]; Imagerie médicale; Apprentissage [cs.LG]

Hugo Richard, Ana Pinho, Bertrand Thirion, Guillaume Charpiat. Optimizing deep video representation to match brain activity. CCN 2018 - Conference on Cognitive Computational Neuroscience, Sep 2018, Philadelphia, United States. ⟨hal-01868735⟩