Large-Scale Cortical Networks for Hierarchical Prediction and Prediction Error in the Primate Brain

Abstract

According to predictive-coding theory, cortical areas continuously generate and update predictions of sensory inputs at different hierarchical levels and emit prediction errors when the predicted and actual inputs differ. However, predictions and prediction errors are simultaneous and interdependent processes, making it difficult to disentangle their constituent neural network organization. Here, we test the theory by using high-density electrocorticography (ECoG) in monkeys during an auditory "local-global" paradigm in which the temporal regularities of the stimuli were controlled at two hierarchical levels. We decomposed the broadband data and identified lower- and higher-level prediction-error signals in early auditory cortex and anterior temporal cortex, respectively, and a prediction-update signal sent from prefrontal cortex back to temporal cortex. The prediction-error and prediction-update signals were transmitted via γ (>40Hz) and α/β (<30Hz) oscillations, respectively. Our findings provide strong support for hierarchical predictive coding and outline how it is dynamically implemented using distinct cortical areas and frequencies.