Deep Recurrent Encoder: A scalable end-to-end network to model brain signals

Understand

Understanding how the brain responds to sensory inputs is challenging: brain recordings are partial, noisy, and high dimensional; they vary across sessions and subjects and they capture highly nonlinear dynamics.

• These challenges have led the community to develop a variety of preprocessing and analytical (almost exclusively linear) methods, each designed to tackle one of these issues.
• Instead, we propose to address these challenges through a specific end-to-end deep learning architecture, trained to predict the brain responses of multiple subjects at once.
• We successfully test this approach on a large cohort of magnetoencephalography (MEG) recordings acquired during a one-hour reading task.