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A Stable‚ Fast‚ and Fully Automatic Learning Algorithm for Predictive Coding Networks

Tommaso Salvatori‚ Yuhang Song‚ Yordan Yordanov‚ Beren Millidge‚ Lei Sha‚ Cornelius Emde‚ Zhenghua Xu‚ Rafal Bogacz and Thomas Lukasiewicz


Predictive coding networks are neuroscience-inspired models with roots in both Bayesian statistics and neuroscience. Training such models, however, is quite inefficient and unstable. In this work, we show how by changing the temporal scheduling of the update rule for the synaptic weights leads to an algorithm that is much more efficient and stable than the original one, and has theoretical guarantees in terms of convergence. The proposed algorithm, that we call incremental predictive coding (iPC) is also more biologically plausible than the original one, as it it fully automatic. In an extensive set of experiments, we show that iPC constantly outperforms the original formulation on a large number of benchmarks for image classification, as well as for the training of both conditional and masked language models, in terms of test accuracy, efficiency, and convergence with respect to a large set of hyperparameters.

Book Title
Proceedings of the 12th International Conference on Learning Representations‚ ICLR 2024‚ Vienna‚ Austria‚ 7–11 May 2024