2021/12/19 by Linxing Preston Jiang, Rajesh P. N. Rao · 1 voice · 31 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Neuroscience · #Biology #Coding (social sciences) #Computer science #Evolutionary biology #Function (biology) #Mathematics #Neural Networks and Applications #Neural dynamics and brain function #Neuroscience #Predictive coding #Statistics #q-bio.NC
paper · pdf · doi:10.1093/acrefore/9780190264086.013.328
published in Oxford Research Encyclopedia of Neuroscience
arxiv published 2021/12/19 · openalex publication_date 2022/11/21 · arxiv updated 2023/05/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/26
Abstract Predictive coding is a unifying framework for understanding perception, action, and neocortical organization. In predictive coding, different areas of the neocortex implement a hierarchical generative model of the world that is learned from sensory inputs. Cortical circuits are hypothesized to perform Bayesian inference based on this generative model. Specifically, the Rao–Ballard hierarchical predictive coding model assumes that the top-down feedback connections from higher to lower order cortical areas convey predictions of lower-level activities. The bottom-up, feedforward connections in turn convey the errors between top-down predictions and actual activities. These errors are used to correct current estimates of the state of the world and generate new predictions. Through the objective of minimizing prediction errors, predictive coding provides a functional explanation for a wide range of neural responses and many aspects of brain organization.