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

A major goal of cognitive neuroscience consists of identifying how the brain responds to distinct experimental conditions. While descriptive statistics and statistical tests are classically used to analyze neural data [1], this approach is not suited to predict how the brain should react to new conditions. The resulting models of the brain can thus be particularly challenging to compare. By contrast, predictive encoding models [2, 3] can be directly trained to predict brain responses to various experimental conditions, and compared on their ability to accurately predict novel conditions. For example, encoding models allow the estimation of integration constants in the brain [4, 5], the hierarchical organization of visual [6] and speech processing [7, 8]. Beyond MEG, predictive models have enabled automatic segmentation [9] and dynamical system identification [10, 11]. In functional Magnetic Resonance Imaging, predictive encoding models are starting to emulate complex neural processing [12] and are a step towards discovering new phenomena [13, 14]. Yet, this general objective of developing encoding models faces three major challenges when working with non-invasive and time-resolved signals collected by magneto-and electro-encephalography (M/EEG).