Predictive Coding

Predictive coding (PC) is a biologically inspired framework for learning and inference that posits the brain minimizes prediction errors through hierarchical processing and feedback connections. Current research focuses on improving PC's efficiency and scalability, particularly through novel algorithms and architectures like those incorporating Langevin dynamics or leveraging diffusion probabilistic models, and exploring its application in diverse areas such as reinforcement learning, time series analysis, and image compression. The development of more efficient and robust PC algorithms holds significant potential for advancing both our understanding of brain function and the development of more biologically plausible and energy-efficient artificial intelligence systems.