PredProp: Bidirectional Stochastic Optimization with Precision Weighted Predictive Coding
This work addresses optimization challenges in predictive coding networks, offering a novel method for researchers in machine learning, though it appears incremental as it builds on existing predictive coding frameworks.
The authors tackled optimization in predictive coding networks by introducing PredProp, a method that uses precision-weighted predictive coding for bidirectional stochastic optimization, and demonstrated that it outperforms Adam on simple image benchmark datasets.
We present PredProp, a method for optimization of weights and states in predictive coding networks (PCNs) based on the precision of propagated errors and neural activity. PredProp jointly addresses inference and learning via stochastic gradient descent and adaptively weights parameter updates by approximate curvature. Due to the relation between propagated error covariance and the Fisher information matrix, PredProp implements approximate Natural Gradient Descent. We demonstrate PredProp's effectiveness in the context of dense decoder networks and simple image benchmark datasets. We found that PredProp performs favorably over Adam, a widely used adaptive learning rate optimizer in the tested configurations. Furthermore, available optimization methods for weight parameters benefit from using PredProp's error precision during inference. Since hierarchical predictive coding layers are optimised individually using local errors, the required precisions factorize over hierarchical layers. Extending beyond classical PCNs with a single set of decoder layers per hierarchical layer, we also generalize PredProp to deep neural networks in each PCN layer by additionally factorizing over the weights in each PCN layer.