Cédric Goemaere, Gaspard Oliviers, Rafal Bogacz, Thomas Demeester

Predictive Coding (PC) offers a biologically plausible alternative to backpropagation for neural network training, yet struggles with deeper architectures. This paper identifies the root cause: an inherent signal decay problem where gradients attenuate exponentially with depth, becoming computationally negligible due to numerical precision constraints. To address this fundamental limitation, we introduce Error Optimization (EO), a novel reparameterization that preserves PC's theoretical properties while eliminating signal decay. By optimizing over prediction errors rather than states, EO enables signals to reach all layers simultaneously and without attenuation, converging orders of magnitude faster than standard PC. Experiments across multiple architectures and datasets demonstrate that EO matches backpropagation's performance even for deeper models where conventional PC struggles. Besides practical improvements, our work provides theoretical insight into PC dynamics and establishes a foundation for scaling biologically-inspired learning to deeper architectures on digital hardware and beyond.