Hybrid predictive coding: Inferring, fast and slow
PLoS Computational Biology August 2, 2023 Alexander Tscshantz, Beren Millidge, Anil K. Seth et al. 56 citations
Predictive coding theory holds that the brain perceives by minimizing prediction errors through cycles of neural activity. However, some visual perception, including complex object recognition, happens too quickly for such cycles. This paper proposes that the initial fast 'feedforward sweep' performs amortized inference, using a learned function to map data directly to beliefs, while slower recurrent processing performs iterative inference, sequentially updating beliefs for greater accuracy. A hybrid predictive coding network combining both methods is introduced, implemented in a biologically plausible neural architecture using local Hebbian rules. The hybrid model achieves rapid perception for familiar data while retaining context-sensitivity and sample efficiency for novel situations, and adaptively balances both inference modes based on uncertainty.