Refining local computations for inference and learning of artificial neural networks: A narrative review of predictive coding as a potent alternative to backpropagation

Artificial neural networks with backpropagation algorithms have successfully simulated the hierarchical biological brain structures to realize machine learning and describe biological brain learning in many cognitive tasks. However, backpropagation algorithms do not fully follow the rules of biological brain learning to update weights and transmit information, which affects the biological plausibility of backpropagation in the field of neuroscience. The objective of this review is to investigate predictive coding theory as a biologically plausibility alternative to backpropagation, examining both its theoretical potential and practical application feasibility. The predictive coding proposes that the brain minimizes the error between external inputs and expectations by continuously generating and updating internal predictive models, thereby efficiently understanding and interpreting sensory information. Compared with backpropagation, the two core advantages of predictive coding lie in its capacity for efficient local computation and inherent biological plausibility, making it a promising alternative approach to backpropagation. A series of neuroscience experiments have further validated the role of predictive coding in perception, motor control, and cognitive function, highlighting its significance in the study of brain credit assignment mechanisms.