Large-Scale Neuronal Networks that Organize the Predictive Brain
The predictive coding theory proposes that the brain continuously generates and updates predictions of sensory information at multiple levels of abstraction, and emits prediction-error signals when the predicted and actual sensory inputs differ. This framework offers a unified model of perception and action, and may offer insight into psychiatric disorders where prediction or error signals may go awry, such as schizophrenia and autism. My current research interests focus on: (1) grounding the theory by identifying brain signals that subserve predictions and prediction errors at different hierarchical levels, and (2) generalizing the theory into cognitive domains other than perception and action. A better understanding of how information at different spatial and temporal scales merge into coherent unity can facilitate the development of neuromorphic engineering and advance the search for neural markers for the prognosis and diagnosis of brain disorders.