Predictive Coding

Predictive coding is a theoretical framework in neuroscience and artificial intelligence that posits that the brain and intelligent systems minimize the difference between predicted and actual sensory inputs. This approach involves generating predictions about incoming data based on internal models and updating these models based on the prediction errors observed. Mathematically, this can be represented as minimizing a loss function that quantifies the discrepancy between predicted outcomes and actual observations. Predictive coding is closely related to Bayesian inference, where prior beliefs are updated with new evidence to refine predictions. In AI, this concept is applied in various domains, including reinforcement learning and generative models, facilitating efficient learning and adaptation by focusing on the most informative aspects of sensory input. The framework emphasizes the importance of error correction in learning processes, making it a foundational concept in cognitive science and machine learning.