On the Infinite Width and Depth Limits of Predictive Coding Networks

arXiv:2602.07697v2 Announce Type: replace Abstract: Predictive coding (PC) is a biologically plausible alternative to standard backpropagation (BP) that minimises an energy function with respect to network activities before updating weights. Recent work has improved the training stability of deep PC networks (PCNs) by leveraging some BP-inspired reparameterisations. However, the full scalability and theoretical basis of these methods remain unclear. To address this gap, we study the infinite width and depth limits of PCNs. For linear residual networks, we show that the set of width- and depth-

Source: arXiv cs.LG — read the full report at the original publisher.

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